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. Claims 1-21 are presented for examination. Information Disclosure Statement The information disclosure statement (IDS) submitted on August 7, 2023 and February 13, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Specification The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. The abstract of the disclosure is objected to because in the antepenultimate and penultimate sentences, “are generated” should be moved to the end of the sentences . A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). The use of the term s GOOGLE (paragraphs 106 and 124) and MATLAB (paragraph 106 ) , which are trade name s or mark s used in commerce, has been noted in this application. The term s should be accompanied by the generic terminology; furthermore , the term s should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term s . Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. Claim Objections Claims 3 and 14 are objected to because of the following informalities: “based on solely on” should be “based solely on”. Claims 4 and 15 are objected to for dependency on claims 3 and 14, respectively. Claim s 6 and 17 are objected to because of the following informalities: “at least one selected from” should be “at least one is selected from” . Claims 7 and 18 are objected to because of the following informalities: “generating … comprise” should be “generating … comprises”. Claim 10 is objected to because of the following informalities: “one; a sum” should be “one; and a sum” and “less than half” should be “less than one half”. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 10 recites, inter alia, that “a sum of said fractions would be less than half.” (Emphasis added.) The use of this conditional language renders it unclear whether the sum being less than half is a requirement of the claim or whether the sum being less than half is contingent on some other condition that is not specified by the claim. For purposes of examination, Examiner will construe this language as though it read “a sum of said fractions is less than one half.” Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim s 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”). Claim s 1 and 21 Step 1 : The claims recite a method; therefore, they are directed to the statutory category of processes. Step 2A Prong 1 : The claim s recite, inter alia: [I] nferring , … from a plurality of objects, an inferred frequency of each class of at least three classes: This limitation could encompass mentally classifying the objects and mentally inferring the frequency of each class based thereon. [G] enerating a respective upscaled magnitude of each class of the at least three classes from the inferred frequency of the class: This limitation could encompass mentally generating the upscaled magnitude. Upscaling is also a mathematical concept. [G] enerating a respective integer of each class of the at least three classes from the upscaled magnitude of the class: This limitation could encompass mentally generating the integer. [E] stimating , based on said integers of the at least three classes and a target integer respectively for each class of the at least three classes: a count of the plurality of objects that are true positives of the class, a count of the plurality of objects that are false positives of the class, and a count of the plurality of objects that are false negatives of the class: This limitation could encompass mentally generating the counts of true positives, false positives, and false negatives based on the integers and the target integer. [G] enerating , based on the counts of true positives of the at least three classes, an estimated total of true positives that characterizes fitness of the trained classifier: This limitation could encompass mentally estimating a total of true positives and using that total as a measure of classifier fitness. [G] enerating , based on the counts of false positives of the at least three classes, an estimated total of false positives that characterizes the fitness of the trained classifier: This limitation could encompass mentally estimating a total of false positives and using that total as a measure of classifier fitness. [G] enerating , based on the counts of false negatives of the at least three classes, an estimated total of false negatives that characterizes fitness of the trained classifier: This limitation could encompass mentally estimating a total of false negatives and using that total as a measure of classifier fitness. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The claims further recite that the inferred frequency is generated “by a trained classifier” and that “the method is performed by one or more computers.” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). Step 2B : The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2. As an ordered whole, the claims are directed to a mentally performable process of estimating true positives, false positives, and false negatives from the outputs of a classifier. Nothing in the claims provides significantly more than this. As such, the claims are not patent eligible. Claim 2 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ said generating the estimated total of false positives comprises summing the counts of false positives of the at least three classes. ” This is a mathematical concept; moreover, summing the counts could be performed in the mind given sufficiently small counts. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 3 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ said generating the upscaled magnitude comprises using a particular multiplicand selected from a group consisting of a multiplicand that is based on solely on the plurality of objects and an integer multiplicand. ” This is a mathematical concept, and upscaling using a multiplicand could also be performed in the mind. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 4 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ the particular multiplicand is not based on a count of the at least three classes. ” Generating the upscaled magnitude using the multiplicand remains a mathematical concept/mental process under these further assumptions. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 3 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 3 analysis. Claim 5 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites “ predefining a distinct weight for each class of the at least three classes; and said generating the estimated total of false negatives comprises using the weights of the at least three classes as multiplicands. ” Predefining a weight may be performed mentally, and generating a total using multiplicands is a mathematical concept that may be executed mentally given sufficiently simple data. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 6 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ at least one [is] selected from a group consisting of: the weight of each class of the at least three classes is less than one, and the weights of the at least three classes sum to one. ” Predefining the weights remains mentally performable under these further assumptions. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 5 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 5 analysis. Claim 7 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ said generating the integers of the at least three classes comprise one selected from a group consisting of: rounding up, rounding down, and rounding to respective nearest integers. ” Rounding is a mathematical concept and may be performed mentally. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 8 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites “ generating the plurality of objects from a parse tree; and generating the target integer for each class of the at least three classes based on a frequency of a respective n-gram in the parse tree. ” The first limitation could encompass a human visually observing the parse tree and generating the objects based thereon. As for the second, generating the target integer remains mentally performable under these further assumptions. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 9 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ generating the upscaled magnitude comprises using a multiplicand that is based solely on the parse tree. ” Generating the upscaled magnitude remains a mathematical concept/mental process under these further assumptions. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 8 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 8 analysis. Claim 1 0 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites that “ the estimated total of true positives, the estimated total of false positive s, and the estimated total of false negatives are fractions that are less than one; [and] a sum of said fractions [is] less than [one] half. ” Estimating these values remains mentally performable under these further conditions. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. See claim 1 analysis. Step 2B : The claim does not contain significantly more than the judicial exception. See claim 1 analysis. Claim 1 1 Step 1 : A process, as above. Step 2A Prong 1 : The claim recites the same judicial exceptions as in claim 1. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The claim further recites that “ the trained classifier is a single neural network. ” The recitation that the inference is performed by a neural network is a mere instruction to apply the exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.06(f). Step 2B : The claim does not contain significantly more than the judicial exception. The claim further recites that “ the trained classifier is a single neural network. ” The recitation that the inference is performed by a neural network is a mere instruction to apply the exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.06(f). Claim s 1 2-20 Step 1 : The claims recite a non-transitory computer-readable medium; therefore, they are directed to the statutory category of articles of manufacture. Step 2A Prong 1 : The claim s recite the same judicial exceptions as in claims 1-9, respectively. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 1-9, respectively, except insofar as these claims additionally recite “[o] ne or more non-transitory computer-readable media storing instructions that, when executed by one or more processors , [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). Step 2B : The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 1-9, respectively, except insofar as these claims additionally recite “[o] ne or more non-transitory computer-readable media storing instructions that, when executed by one or more processors , [perform the method]”. However, this amounts to a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f). 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claim s 1-4, 11-15, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Amershi et al. (US 20180046935 ) (“ Amershi ”) in view of Csord ás et al. (US 10380753) (“ Csordás ”) . Regarding claim 1, Amershi discloses “[a] method comprising: inferring, by a trained classifier, from a plurality of objects, an inferred frequency of each class of at least three classes ( many performance metrics in multi-class classification [i.e., classification of at least three classes] are derived from different categories of prediction counts; for example, accuracy is computed as the number of correct predictions over the total number of predictions (correct and incorrect) – Amershi , paragraph 49; analysis tool can count the amount of items designated as true positives, false positives, and false negatives for each class as well as for overall performance across classes – id. at paragraph 50 [note that the total of these three plus true negatives equals an inferred frequency of each class]; see also paragraph 44 (disclosing that the multi-class classifiers are built by training binary classifiers and combining their outputs) ) ; generating a respective … magnitude of each class of the at least three classes from the inferred frequency of the class ( analysis tool can count the amount of items designated as true positives, false positives, and false negatives for each class as well as for overall performance across classes ; the amount [frequency] of items falling into each category [class] can be broken down into the amount of items falling into confidence score ranges [magnitudes] – Amershi , paragraph 50 ) ; generating a respective integer of each class of the at least three classes from … the class ( Amershi paragraph 62 describes the classes as being labeled 0-9 ) ; estimating, based on said integers of the at least three classes and a target integer respectively for each class of the at least three classes : a count of the plurality of objects that are true positives of the class, a count of the plurality of objects that are false positives of the class, and a count of the plurality of objects that are false negatives of the class ( bidirectional bar graphs are concurrently displayed for each class available in a multi-class classifier; the true positives are portrayed in the color associated with the class; the false positives are displayed in a color associated with the class into which the item should have been classified; the false negatives are portrayed in the color of the class into which they were actually assigned – Amershi , paragraph 29 ; item of test data labeled as a 4 should be classified as a 4, but if it is classified as a 6 (a false positive), it will be depicted on the right side of the class 6 bidirectional graph in the color associated with class 4 [i.e., each classification is based on the actual integer to which the class being [i.e., the target class] and the integer predicted by the classifier] – id. at paragraph 67 ) ; generating, based on the counts of true positives of the at least three classes, an estimated total of true positives that characterizes fitness of the trained classifier; generating, based on the counts of false positives of the at least three classes, an estimated total of false positives that characterizes the fitness of the trained classifier; and generating, based on the counts of false negatives of the at least three classes, an estimated total of false negatives that characterizes fitness of the trained classifier ( accuracy is computed as the number of correct predictions over the total number of predictions (correct and incorrect); precision is computed as the number of true positives over the number of true and false positives and recall is the number of true positives over the number of true positives and false negatives [i.e., the true positives, false positives, and false negatives are aggregated across classes and used in precision and recall metrics that characterize the fitness of the classifier] – Amershi , paragraph 49 ) ; wherein the method is performed by one or more computers ( Amershi Fig. 1 shows that the method is performed on a computer comprising a processor and a memory ).” Amershi appears not to disclose explicitly the further limitations of the claim. However, Csordás discloses “generating a respective upscaled magnitude ( input disparity maps are upsampled in upsampling units to the next scale [upscaled magnitude]; in an exemplary case where the scale is 2, units are labeled by “x2” – Csordás , col. 30, l. 46-col. 31, l. 6 ) …; [and] generating [data] from the upscaled magnitude ( output of left upscaling unit is given to a warping unit for warping the right feature map [warped map = data generated from upscaled magnitude] – Csordás , col. 31, ll. 17-29 ) ….” Csordás and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amershi to upscale the magnitude and generate data therefrom , as disclosed by Csordás , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the data to be represented at a larger scale than previously, thereby magnifying its effect on downstream data when needed. See Csordás , col. 30, l. 46-col. 31, l. 6 . Claim 12 is a non-transitory computer-readable medium claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim. Similarly, c laim 21 corresponds to claim 1 except insofar as claim 21 recites “a plurality of classes” instead of “at least three classes”. However, since three classes constitute a plurality, the rejection of claim 1 is equally applicable to claim 21. Regarding claim 2, Amershi , as modified by Csordás , discloses that “said generating the estimated total of false positives comprises summing the counts of false positives of the at least three classes ( analysis tool counts the amount of items designated as true positives, false positives, and false negatives for each class as well as for overall performance across classes [e.g., by summing the false positives across classes] – Amershi , paragraph 50 ).” Claim 13 is a non-transitory computer-readable medium claim corresponding to method claim 2 and is rejected for the same reasons as given in the rejection of that claim. Regarding claim 3, Amershi , as modified by Csordás , discloses that “said generating the upscaled magnitude comprises using a particular multiplicand selected from a group consisting of a multiplicand that is based on solely on the plurality of objects and an integer multiplicand ( input disparity maps are upsampled in upsampling units to the next scale [upscaled magnitude]; in an exemplary case where the scale is 2, units are labeled by “x2” [2 = integer multiplicand] – Csordás , col. 30, l. 46-col. 31, l. 6 ).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amershi to upscale by an integer multiplicand, as disclosed by Csordás , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the data to be represented at a larger scale than previously, thereby magnifying its effect on downstream data when needed. See Csordás , col. 30, l. 46-col. 31, l. 6 . Claim 14 is a non-transitory computer-readable medium claim corresponding to method claim 3 and is rejected for the same reasons as given in the rejection of that claim. Regarding claim 4, Amershi , as modified by as disclosed by Csordás , discloses that “the particular multiplicand is not based on a count of the at least three classes ( a number being a power of 2 is a typical choice for the upscaling factor, but other integers can be used as the upscaling factor , taking into consideration the scale of the pair of feature maps [i.e., not taking into consideration a count of classes] – Csordás , col. 30, l. 46-col. 31, l. 6 ).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amershi to choose an integer multiplicand for upscaling , as disclosed by Csordás , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the data to be represented at a larger scale than previously, thereby magnifying its effect on downstream data when needed. See Csordás , col. 30, l. 46-col. 31, l. 6 . Claim 15 is a non-transitory computer-readable medium claim corresponding to method claim 4 and is rejected for the same reasons as given in the rejection of that claim. Regarding claim 11, Amershi , as modified by Csordás , discloses that “the trained classifier is a single neural network ( neural network-based feature extractor branch [part of a single neural network] is applied on an image – Csordás , col. 4, ll. 38-49 ).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amershi to employ a neural network, as disclosed by Csordás , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the user to utilize off-the-shelf models without having to build them manually. See Csordás , col. 4, ll. 38-49 . Claim s 5-6 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Amershi in view of Csordás and further in view of Kalech et al. (US 20180173599) (“ Kalech ”). Regarding claim 5, the rejection of claim 1 is incorporated. Amershi further discloses “each class of the at least three classes” and “generating the estimated total of false negatives”, as shown in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Kalech discloses that “ the method further comprises predefining a distinct weight for each class ( one approach to combine fault likelihood estimates [classes] is by using some weighted linear combination, such that the weights are positive and sum up to one – Kalech , paragraph 85 ) … ; and said generating the estimated total … comprises using the weights of the … classes as multiplicands ( one approach to combine fault likelihood estimates [classes] is by using some weighted linear combination [total that uses weights as multiplicands] , such that the weights are positive and sum up to one – Kalech , paragraph 85 ).” Kalech and the instant application both relate to artificial intelligence and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to use a weight for each class as a multiplicand , as disclosed by Kalech , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the weights to represent probability estimates, thereby increasing their utility. See Kalech , paragraph 85 . Claim 16 is a non-transitory computer-readable medium claim corresponding to method claim 5 and is rejected for the same reasons as given in the rejection of that claim. Regarding claim 6 , the rejection of claim 5 is incorporated. Amershi further discloses “ at least three classes”, as shown in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Kalech discloses that “ at least one [is] selected from a group consisting of: the weight of each class of the … classes is less than one, and the weights of the … classes sum to one ( one approach to combine fault likelihood estimates [classes] is by using some weighted linear combination, such that the weights are positive and sum up to one – Kalech , paragraph 85 ) . ” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to use weights that sum to one, as disclosed by Kalech , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the weights to represent probability estimates, thereby increasing their utility. See Kalech , paragraph 85. Claim 17 is a non-transitory computer-readable medium claim corresponding to method claim 6 and is rejected for the same reasons as given in the rejection of that claim. Claim s 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Amershi in view of Csordás and further in view of Soni et al. (US 20200120131) (“Soni”). Regarding claim 7 , the rejection of claim 1 is incorporated. Amershi further discloses “ generating the integers of the at least three classes ”, as shown above in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Soni discloses that “ said generating the [data] … comprise [s] one selected from a group consisting of: rounding up, rounding down, and rounding to respective nearest integers ( in a model of k classes corresponding to protocols, the model predicts a real number, and this real number is rounded to the nearest classification in the range of integers [0, k – 1] – Soni, paragraph 100 ) . ” Soni and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to round the data to the nearest integer , as disclosed by Soni , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow continuous quantities to be represented as discrete classes, thereby saving processing power. See Soni, paragraph 100 . Claim 18 is a non-transitory computer-readable medium claim corresponding to method claim 7 and is rejected for the same reasons as given in the rejection of that claim. Claim s 8-9 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Amershi in view of Csordás and further in view of Ehlen et al. (US 20150186790) (“ Ehlen ”). Regarding claim 8 , the rejection of claim 1 is incorporated. Amershi further discloses “ generating the target integer for each class of the at least three classes”, as shown above in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Ehlen discloses “ generating the plurality of objects from a parse tree ( using tokenized sentences, a parse tree for each sentence may be generated; any noun, noun-noun, or adjective-noun combinations [objects] are extracted from each noun phrase – Ehlen , paragraph 41 ) ; and generating the [data] … based on a frequency of a respective n-gram in the parse tree ( phrases or n-grams common to a trigger are extracted and counted, and phrases or n-grams are scored by frequency for their relevancy [data] to the buying decision trigger – Ehlen , paragraph 77; see also paragraphs 58-63 (disclosing that the n-grams are part of a parsing algorithm, e.g., a parse tree) ) . ” Ehlen and the instant application both relate to parse trees and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to generate data based on frequency of elements of a parse tree , as disclosed by Ehlen , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to understand the syntactical structure of the objects being parsed. See Ehlen , 41 . Claim 19 is a non-transitory computer-readable medium claim corresponding to method claim 8 and is rejected for the same reasons as given in the rejection of that claim. Regarding claim 9 , the rejection of claim 8 is incorporated. Csordás further discloses that “ said generating the upscaled magnitude comprises using a multiplicand ”, as shown in the rejection of claim 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Amershi to perform upscaling using a multiplicand , as disclosed by Csordás , for substantially the reasons given in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Ehlen discloses “using a [datum] that is based solely on the parse tree ( using tokenized sentences, a parse tree for each sentence may be generated; any noun, noun-noun, or adjective-noun combinations [objects] are extracted from each noun phrase and added to a set of attribute candidates [used data based solely on the parse tree] – Ehlen , paragraph 41 ) . ” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to derive data from a parse tree , as disclosed by Ehlen , and an ordinary artisan could reasonably expect to have done so successfully. Doing so would allow the system to understand the syntactical structure of the objects being parsed. See Ehlen , 41. Claim 20 is a non-transitory computer-readable medium claim corresponding to method claim 9 and is rejected for the same reasons as given in the rejection of that claim. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Amershi in view of Csordás and further in view of Burke et al. (WO 2017079398) (“Burke”). Regarding claim 10 , the rejection of claim 1 is incorporated. Amershi further discloses “ the estimated total of true positives, the estimated total of false positives, and the estimated total of false negatives ”, as shown in the rejection of claim 1. Neither Amershi nor Csordás appears to disclose explicitly the further limitations of the claim. However, Burke discloses that “ the estimated total [s] … are fractions that are less than one; [and] a sum of said fractions [is] less than [one] half ( values of e(C, f) may be summed for small fractions for each conditions, e.g., the maximum is selected for the sum over a set of values f1, f2, f3, … for two or more fractions less than half and not equal to zero for one condition [e.g., for f1 and f2 < ¼, the sum is less than one half] – Burke, paragraph 54 ) . ” Burke and the instant application both relate to machine learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Amershi and Csordás to use fractional weights that sum to less than one half, as disclosed by Burke, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would prevent certain objects from being excessively weighted. See Burke, paragraph 54 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT RYAN C VAUGHN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4849 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-R 7:00a-5:00p ET . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Kamran Afshar , can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-7796 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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