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 .
Remarks
In response to communications sent July 14, 2025, claim(s) 1-6, 8-17, 19, 20, and 27 is/are pending in this application; of these claim(s) 1, 6, and 17 is/are in independent form. Claim(s) 7, 18, and 21-26 is/are cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 14, 2025 has been entered.
Terminal Disclaimer
The terminal disclaimer filed on July 17, 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent 11,062,792 has been reviewed and is NOT accepted.
The terminal disclaimer was not signed.
Specifically, the correct party did not sign the terminal disclaimer. See the Office communication sent July 14, 2025.
Claim Interpretation
The term “genome” is interpreted in the context of “genetic algorithms” rather than interpreted as a literal biological “genome”. See applicant’s specification at Table 4 and Table 5 for illustrations of the “genomes” in an algorithmic sense. See applicant’s specification at Para [0031] for example of the genetic algorithm affecting the algorithmic “genome” parameters.
The claims recite selecting “one or more features” and also recites selecting the type of machine learning algorithm by selecting an index. Hence, it would be unreasonable to interpret the “type of machine learning algorithm” as being “the type of algorithm as demarcated by the set of input features to the algorithm”. This would not be a reasonable interpretation because the “set of features” is already positively recited as a different element from the machine learning algorithm type.
Response to Arguments
Applicant’s arguments, see page 10 line 9 to page 12 line 14, filed July 14, 2025, with respect to the rejection(s) of claim(s) 1-6, 8-17, 19, 20, and 27 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 over US 2010/0036782 A1 (“Zhao”) in view of “Ordonez.” The Ordonez reference is:
Ordóñez, Francisco Javier, Agapito Ledezma, and Araceli Sanchis. "Genetic Approach for Optimizing Ensembles of Classifiers." FLAIRS. 2008
Applicant's arguments filed July 14, 2025 have been fully considered but they are not persuasive. The Terminal Disclaimer was not approved, therefore the non-statutory double patenting rejection is not withdrawn.
Claim Objections
Claim 8 objected to because of the following informalities: The claim recites “first genome” at the end of the claim. The Examiner believes that the Applicant intended to delete the word “first” when amending the claims. 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 applicant regards as his invention.
Claims 1-5 and 27 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 limitations reciting “component” has been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because of Applicant’s Remarks at page 10 section B. Applicant argued that the claim term "component" is not intended to be limiting and can include or implement, in various configurations, none, all, or a portion of the claimed "memory" and/or none, all, or a portion of the claimed "processor". However, if the “components” mean none of the claimed memory and none or the claimed processor, then it would be a nonce term; whereas if the “components” do mean a memory or processor, then they would have structure. Since Applicant asserts that the term encompasses a nonce term and also encompasses structure, the Examiner must reject under 35 U.S.C. § 112(b).
The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 16 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The step of “modifying at least one machine learning algorithm parameter of the first identified genome” is already present in the independent claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Analysis - 35 USC § 101
No rejection is made under 35 U.S.C. § 101 because the claim involves math, but is directed to training models with an improvement to the manner of training.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6, 8-17, 19, 20, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2010/0036782 A1 (“Zhao”) in view of “Ordonez.” The Ordonez reference is:
Ordóñez, Francisco Javier, Agapito Ledezma, and Araceli Sanchis. "Genetic Approach for Optimizing Ensembles of Classifiers." FLAIRS. 2008
As to claim 1, Zhao teaches a system, having a memory and a processor, for discovering machine learning genomes (the term “genome” is interpreted in the context of “genetic algorithms” rather than interpreted as a literal biological “genome”; see the “claim interpretation” section above for support for this claim interpretation; see Zhao Para [0008]: “performing genetic algorithm-based feature selection”; the Examiner maps “features” to elements of Applicant’s claimed “genome”), the system comprising:
a first component configured to generate a plurality of genomes, wherein each genome identifies at least one feature (Zhao’s Para [0003]; the subset mentioned in Para [0008] is the set of features selected as part of the genetic algorithm) … , wherein generating a first genome of the plurality of genomes comprises:
randomly selecting, from among a set of features, one or more of the features (Zhao Para [0008]: “genetic algorithm-based feature selection is performed”; see “mutation” in the definition of a genetic algorithm at Zhao’s Para [0003]; the subset mentioned in Para [0008] is the set of features selected as part of the genetic algorithm),
…
a second component configured to, for each generated genome,
train a one or more models using the generated genome (Zhao Para [0008]: “applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result”), and
for each model trained using the generated genome (Zhao Para [0008]: for the plurality of classification patterns),
calculate a fitness score for the trained model at least in part by applying the trained model to a validation data set (Zhao Para [0008]: “integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result”), and
produce a fitness score for the generated genome based at least in part on the fitness scores generated for the models trained using the generated genome (Zhao Para [0008]: “outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset”);
a third component configured to identify, from among the generated genomes, a plurality of genomes having a fitness score that exceeds a fitness threshold (Zhao Para [0032]: the genetic algorithm continues and a “best” feature subset is determined; the “best” feature has a fitness rank that exceeds an implicit threshold); and
a fourth component configured to, for each of the identified genomes, mutate the identified genome (Zhao Para [0032]: mutate the candidate feature set),
wherein at least one of the components comprises computer-executable instructions stored in the memory for execution by the system (Zhao Para [0018]: in a computerized system).
However, Zhao does not teach:
a first component configured to generate a plurality of genomes, wherein each genome identifies … at least one parameter for at least one machine learning algorithm;
Nor does Zhao teach:
randomly selecting, from among a set of parameters for at least one machine learning algorithm, one or more of the parameters, wherein a particular one or more parameters comprises an index that corresponds to a particular type of the at least one machine learning algorithm, and
assigning at least one random value to a particular index of a particular one of each of the selected parameters.
Nevertheless, Ordonez teaches:
a first component configured to generate a plurality of genomes, wherein each genome identifies … at least one parameter for at least one machine learning algorithm (Ordonez page 90 first paragraph of the section “Problem Encoding”: indexing a machine learning algorithm as a binary flag on a virtual chromosome as part of a genetic algorithm; see Also Figure 1, “Phase III” “Chromosomes” and “Decoding”);
randomly selecting, from among a set of parameters for at least one machine learning algorithm, one or more of the parameters, wherein a particular one or more parameters comprises an index that corresponds to a particular type of the at least one machine learning algorithm (Ordonez page 91 “Problem Encoding” second paragraph and page 91 section of “Genetic Search”: applying a genetic algorithm to the chromosomes in Figure 1 that encoding binary identifiers of various algorithms), and
assigning at least one random value to a particular index of a particular one of each of the selected parameters Ordonez page 91 “Problem Encoding” second paragraph and page 91 section of “Genetic Search”: applying a genetic algorithm to the chromosomes in Figure 1 that encoding binary identifiers of various algorithms).
Zhao and Ordonez are in the same field of feature selection for predictive modeling. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhao to include the teachings of Ordonez because ensembles of classifiers are more accurate than individual classifiers and improves accurate than stacking classifiers (See Ordonez Abstract). There would be a reasonable expectation of success because mutating algorithms of an ensemble is compatible with separately mutating representations of the set of parameters in a separate pre-generation step (Ordonez page 90 first paragraph of “GA-Ensemble” section), which has a similar result as simultaneous feature set determination and algorithm set determination.
As to claim 2, Zhao in view of Ordonez teaches the system of claim 1, further comprising:
a fifth component configured to, for the first genome comprising a first set of features, identify correlated features from among the first set of features (Zhao Para [0020]: identity data feature patterns to identify those that induce bias fitness values, for example, due to similarities) at least in part by:
for each feature of the first set of features (Zhao Para [0024]: for each feature of a combination of features),
applying a feature generator associated with the feature to a training set of data to generate a feature vector for the feature (Zhao Para [0024]: create a linear combination of features for a feature vector),
for at least one pair of feature vectors (Zhao Para [0024]: for “classes” subject to “dimension reduction”),
calculating a distance between each feature vector of the pair of feature vectors (Zhao Para [0024]: performing a dimension reduction, which entails distance determinations),
determining that the calculated distance is less than a distance threshold (Zhao Para [0024]: as part of a dimension reduction of the features, determine how to maximize separation of classes rather than have small distances between classes),
in response to determining that the calculated distance is less than a distance threshold, removing, from the first genome, a feature corresponding to at least one feature vector of the pair of feature vectors (Zhao Para [0024]: removing features by reducing the dimensionality of the feature set into a smaller number of features),
wherein each feature vector includes, for each of a plurality of patients, a single value generated by applying a first feature generator to at least one representation of physiological data representative of the patient (Zhao Para [0024]: using the dimension-reduced feature vector for later classification in a Computer-Aided Diagnosis system).
As to claim 3, Zhao in view of Ordonez teaches the system of claim 2, wherein the removing, from the first genome, of at least one feature corresponding to at least one feature vector of a first pair of feature vectors comprises:
randomly selecting one of feature vector of the first pair of feature vectors (Zhao Para [0023]: randomly selecting a feature subset, which comprises at least one feature vector),
identifying, from among the features of the first genome, a feature corresponding to the randomly selected feature vector (Zhao Para [0024]: reducing the dimensionality of the feature vector); and
removing, from the first genome, the identified feature (Zhao Para [0024]: as part of a dimension reduction of the features, determine how to maximize separation of classes rather than have small distances between classes).
As to claim 4, Zhao in view of Ordonez teaches the system of claim 1, further comprising:
a fifth component configured to, for the first genome comprising a first set of features, generate a graph comprising a vertex for each feature of the first set of features (Zhao Para [0026]: generate a simple linear-shaped graph referred to in Zhao as a “chromosome” containing a “feature subset” as vertices);
a sixth component configure to generate an edge between vertices whose corresponding features have a correlation value that exceeds a correlation threshold or a distance value that is less than a distance threshold (Zhao Para [0032]: determining specificity and sensitivity of a particular chromosome to evaluate its predictive capability; the Examiner interprets that correlated features would result in poor specificity or sensitivity during prediction and would be discarded by the genetic algorithm); and
a seventh component configured to remove vertices from the graph until no connected vertices remain in the graph (Zhao Para [0032]: using crossover to determine good feature subsets that are the best featured subsets due to the predictive capability).
As to claim 5, Zhao in view of Ordonez teaches the system of claim 1, further comprising:
a machine configured to receive physiological signal data from at least one patient (Zhao para [0033]: diagnostic data);
a fifth component configured to, for each patient,
apply at least one of the trained models to at least a portion of the physiological signal data received for the patient by the machine (Zhao Para [0033]: applying the genetic algorithm for feature selection from the physiological measurements of the imaging modalities), and
generate a prediction for the patient based at least in part on an application of the at least one of the trained models to at least a portion of the received physiological signal (Zhao Para [0033]: using the algorithm for Computer-Aided Detection).
As to claim 6, Zhao teaches a method, performed by a computing system having a memory and a processor, for discovering machine learning genomes (the term “genome” is interpreted in the context of “genetic algorithms” rather than interpreted as a literal biological “genome”; see the “claim interpretation” section above for support for this claim interpretation; see Zhao Para [0008]: “performing genetic algorithm-based feature selection”; the Examiner maps “features” to elements of Applicant’s claimed “genome”), the method comprising:
generating, with the processor, a plurality of genomes, wherein each genome identifies at least one feature (Zhao’s Para [0003]; the subset mentioned in Para [0008] is the set of features selected as part of the genetic algorithm) …;
for each generated genome (Zhao Para [0008]: for the plurality of classification patterns),
training at least one model using the generated genome (Zhao Para [0008]: “applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result”), and
producing a fitness score for the genome based at least in part on the trained at least one model (Zhao Para [0008]: “integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result”);
identifying, from among the generated genomes, at least one genome having a fitness score that exceeds a fitness threshold (Zhao Para [0032]: the genetic algorithm continues and a “best” feature subset is determined; the “best” feature has a fitness rank that exceeds an implicit threshold); and
mutating each identified genome (Zhao Para [0032]: mutate the candidate feature set).
However, Zhao does not teach generating, with the processor, a plurality of genomes, wherein each genome identifies … at least one random parameter comprising an index that corresponds to a particular type of at least one machine learning algorithm, the index having a random value assigned thereto.
Nevertheless, Ordonez teaches:
generating, with the processor, a plurality of genomes (Ordonez page 90 first paragraph of the section “Problem Encoding”: indexing a machine learning algorithm as a binary flag on a virtual chromosome as part of a genetic algorithm; see Also Figure 1, “Phase III” “Chromosomes” and “Decoding”), wherein each genome identifies … at least one random parameter comprising an index that corresponds to a particular type of at least one machine learning algorithm, the index having a random value assigned thereto (Ordonez page 91 “Problem Encoding” second paragraph and page 91 section of “Genetic Search”: applying a genetic algorithm to the chromosomes in Figure 1 that encoding binary identifiers of various algorithms).
Zhao and Ordonez are in the same field of feature selection for predictive modeling. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhao to include the teachings of Ordonez because ensembles of classifiers are more accurate than individual classifiers and improves accurate than stacking classifiers (See Ordonez Abstract). There would be a reasonable expectation of success because mutating algorithms of an ensemble is compatible with separately mutating representations of the set of parameters in a separate pre-generation step (Ordonez page 90 first paragraph of “GA-Ensemble” section), which has a similar result as simultaneous feature set determination and algorithm set determination.
As to claim 8, Zhao in view of Ordonez teaches the method of claim 6, wherein generating the each generated genome further comprises:
for each feature (Zhao Para [0024]: for each feature of a combination of features),
retrieving a feature vector for the feature based at least in part on a feature generator associated with the feature and a training set of data (Zhao Para [0024]: create a linear combination of features for a feature vector);
identifying pairs of correlated feature vectors from among the generated feature vectors (Zhao Para [0024]: performing a dimension reduction, which entails distance determinations between pairs); and
for each identified pair of correlated feature vectors,
identifying one feature vector of the pair of correlated feature vectors (Zhao Para [0024]: for “classes” subject to “dimension reduction”),
removing, from the genome, the feature associated with the feature generator used to generate the identified feature vector (Zhao Para [0024]: removing features by reducing the dimensionality of the feature set into a smaller number of features);
randomly selecting, from among the set of features, a feature to add to the genome (Zhao Para [0023]: randomly selecting a feature subset, which comprises at least one feature vector), and
adding the randomly selected feature to the first genome (Zhao Para [0008]: in addition to mutating the “genomes” of features, “crossover” the “genomes” of features).
As to claim 9, Zhao in view of Ordonez teaches the method of claim 8, wherein identifying pairs of correlated feature vectors comprises:
for each pair of feature vectors,
calculating a distance metric for the pair of feature vectors (Zhao Para [0024]: performing a dimension reduction, which entails distance determinations), and
determining whether the distance metric calculated for the pair of feature vectors is less than a distance threshold (Zhao Para [0024]: as part of a dimension reduction of the features, determine how to maximize separation of classes rather than have small distances between classes
wherein the distance threshold is determined based at least in part on the calculated distance metrics determined for each pair of feature vectors (Zhao Para [0024]: removing features by reducing the dimensionality of the feature set into a smaller number of features).
As to claim 10, Zhao in view of Ordonez teaches the method of claim 6, wherein producing a fitness score for a first genome comprises:
identifying a number of false positives generated by applying, to two or more validation data sets, a model trained using the first genome (Zhao Para [0032]: determining specificity and sensitivity of a particular chromosome to evaluate its predictive capability, which entails determining false positives); and
identifying a number of false negatives generated by applying, to two or more validation data sets, a model trained using the first genome (Zhao Para [0032]: determining specificity and sensitivity of a particular chromosome to evaluate its predictive capability, which entails determining false negatives).
As to claim 11, Zhao in view of Ordonez teaches the method of claim 6, wherein producing a fitness score for a first genome comprises:
generating, for at least one model trained using the first genome, a receiver operating characteristic curve (Zhao Para [0032]: determining specificity and sensitivity of a particular chromosome to evaluate its predictive capability, which the Examiner interprets to be components of a receiver operator characteristic); and
calculating an area under the generated receiver operating characteristic curve (Zhao Para [0032]: calculating a fitness value that includes both the specificity values and sensitivity values; the Examiner argues that the area under the curve of a ROC curve is at once envisaged as a way to combine specificity and sensitivity into a single value for evaluation).
As to claim 12, Zhao in view of Ordonez teaches the method of claim 6, wherein producing a fitness score for a first genome comprises calculating, for at least one model trained using the first genome, one or more error selected from the group comprising: mean squared prediction error, mean absolute error, interquartile error, and log loss error, receiver-operator characteristic curve error, and f-score error (Zhao Para [0032]: a fitness value, from which an absolute error is at once envisaged as functionally equivalent, but negated and subject to negated operations).
As to claim 13, Zhao in view of Ordonez teaches the method of claim 6, wherein mutating a first identified genome comprises:
selecting at least one feature of the first identified genome (Zhao Para [0024]: for “classes” subject to “dimension reduction”); and
removing, from the first identified genome, each of the selected features of the first identified genome (Zhao Para [0024]: removing features by reducing the dimensionality of the feature set into a smaller number of features).
As to claim 14, Zhao in view of Ordonez teaches the method of claim 6, wherein mutating the each identified genome further comprises:
randomly selecting, from among a set of features, a plurality of the features (Zhao Para [0023]: randomly selecting a feature subset, which comprises at least one feature vector); and
adding, to the each identified genome, each of the randomly selected plurality of features (Zhao Para [0008]: in addition to mutating the “genomes” of features, “crossover” the “genomes” of features).
As to claim 15, Zhao in view of Ordonez teaches the method of claim 6, wherein mutating a first identified genome comprises:
modifying at least one feature of the first identified genome (Zhao Para [0032]: mutate the candidate feature set).
As to claim 16, Zhao in view of Ordonez teaches the method of claim 6, wherein mutating a first identified genome comprises:
modifying at least one machine learning algorithm parameter of the first identified genome (Ordonez page 91 “Problem Encoding” second paragraph and page 91 section of “Genetic Search”: applying a genetic algorithm to the chromosomes in Figure 1 that encoding binary identifiers of various algorithms).
As to claim 17, Zhao teaches a computer-readable medium storing instructions that, if executed by a computing system having a memory and a processor, cause the computing system to perform a method for discovering machine learning genomes (the term “genome” is interpreted in the context of “genetic algorithms” rather than interpreted as a literal biological “genome”; see the “claim interpretation” section above for support for this claim interpretation; see Zhao Para [0008]: “performing genetic algorithm-based feature selection”; the Examiner maps “features” to elements of Applicant’s claimed “genome”), the method comprising:
generating a plurality of genomes, wherein each genome identifies at least one feature (Zhao’s Para [0003]; the subset mentioned in Para [0008] is the set of features selected as part of the genetic algorithm) … ;
for each generated genome,
training at least one model using the generated genome (Zhao Para [0008]: “applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result”), and
producing a fitness score for the genome based at least in part on the trained at least one model (Zhao Para [0008]: “integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result”); and
identifying, from among the generated genomes, one or more genomes having a fitness score that exceeds a fitness threshold (Zhao Para [0032]: the genetic algorithm continues and a “best” feature subset is determined; the “best” feature has a fitness rank that exceeds a threshold).
However, Zhao does not teach generating a plurality of genomes, wherein each genome identifies … at least one random parameter comprising an index that corresponds to a particular type of at least one machine learning algorithm, the index having a random value assigned thereto.
Nevertheless, Ordonez teaches generating a plurality of genomes (Ordonez page 90 first paragraph of the section “Problem Encoding”: indexing a machine learning algorithm as a binary flag on a virtual chromosome as part of a genetic algorithm; see Also Figure 1, “Phase III” “Chromosomes” and “Decoding”), wherein each genome identifies … at least one random parameter comprising an index that corresponds to a particular type of at least one machine learning algorithm, the index having a random value assigned thereto (Ordonez page 91 “Problem Encoding” second paragraph and page 91 section of “Genetic Search”: applying a genetic algorithm to the chromosomes in Figure 1 that encoding binary identifiers of various algorithms).
Zhao and Ordonez are in the same field of feature selection for predictive modeling. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhao to include the teachings of Ordonez because ensembles of classifiers are more accurate than individual classifiers and improves accurate than stacking classifiers (See Ordonez Abstract). There would be a reasonable expectation of success because mutating algorithms of an ensemble is compatible with separately mutating representations of the set of parameters in a separate pre-generation step (Ordonez page 90 first paragraph of “GA-Ensemble” section), which has a similar result as simultaneous feature set determination and algorithm set determination.
As to claim 19, Zhao in view of Ordonez teaches the computer-readable medium of claim 17, the method further comprising:
mutating each identified genome (Zhao Para [0032]: mutate the candidate feature set) having a fitness score that exceeds the fitness threshold (Zhao Para [0008]: genetic algorithm-based mutating; the subset mentioned in Para [0008] is the set of features selected as part of the genetic algorithm).
As to claim 20, Zhao in view of Ordonez teaches the computer-readable medium of claim 17, the method further comprising:
computing the fitness threshold at least in part by, determining an overall fitness score based on the fitness scores produced for each of the generated genomes (Zhao Para [0008]: integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result).
As to claim 27, Zhao in view of Ordonez teaches the system of claim 1, wherein the third component is configured to use a set of the fitness scores produced for the generated genomes to automatically calculate the fitness threshold, and wherein the fitness threshold is based on the set of fitness scores (Zhao Para [0032]: the genetic algorithm continues and a “best” feature subset is determined; the “best” feature has a fitness rank that exceeds an implicit threshold).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-6, 8, 13-16, and 27 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2,3, 5, 16, 18, 19, 21, and 22 of U.S. Patent No. 11,062,792. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are a genus of the species of the reference patent, with minor differences that are at once envisaged.
Instant Application 17/339583
U.S. Patent 11,062,792
1. A system, having a memory and a processor, for discovering machine learning genomes, the system comprising: a first component configured to generate a plurality of genomes, wherein each genome identifies at least one feature and at least one parameter for at least one machine learning algorithm, wherein generating a first genome of the plurality of genomes comprises: randomly selecting, from among a set of features, one or more of the features, randomly selecting, from among a set of parameters for at least one machine learning algorithm, one or more of the parameters, wherein a particular one or more parameters comprises an index that corresponds to a particular type of the at least one machine learning algorithm, and assigning at least one random value to a particular index of a particular one of each of the selected parameters; a second component configured to, for each generated genome, train a one or more models using the generated genome, and for each model trained using the generated genome, calculate a fitness score for the trained model at least in part by applying the trained model to a validation data set, and produce a fitness score for the generated genome based at least in part on the fitness scores generated for the models trained using the generated genome; a third component configured to identify, from among the generated genomes, a plurality of genomes having a fitness score that exceeds a fitness threshold; and a fourth component configured to, for each of the identified genomes, mutate the identified genome, wherein at least one of the components comprises computer-executable instructions stored in the memory for execution by the system.
1. A system, having a memory and a processor, for discovering machine learning genomes the system comprising: a first component configured to generate a plurality of genomes, wherein each genome identifies at least one feature and at least one parameter for at least one machine learning algorithm, wherein generating a first genome of the plurality of genomes comprises: randomly selecting, from among a set of features, one or more of the features, randomly selecting, from among a set of parameters for at least one machine learning algorithm, one or more of the parameters, and assigning at least one random value to each of the selected parameters; a second component configured to, for each generated genome, train one or more models using the generated genome, and for each model trained using the generated genome, calculate a fitness score for the trained model at least in part by applying the trained model to a validation data set, and produce a fitness score for the generated genome based at least in part on the fitness scores calculated for the models trained using the generated genome; a third component configured to identify, from among the generated genomes, a plurality of genomes having a fitness score that exceeds a fitness threshold; a fourth component configured to for each of the identified genomes, mutate the identified genome; and a fifth component configured to identify correlated features from among a first set of features of the first genome at least in part by: for each feature of the first set of features, applying a feature generator associated with the feature to a training set of data to generate a feature vector for the feature, for at least one pair of feature vectors, calculating a distance between each feature vector of the pair of feature vectors, determining that the calculated distance is less than a distance threshold, and in response to determining that the calculated distance is less than the distance threshold, removing, from the first genome, a feature corresponding to at least one feature vector of the pair of feature vectors, wherein each feature vector includes, for each of a plurality of patients, a single value generated by applying a first feature generator to at least one representation of physiological data representative of the patient, wherein at least one of the components comprises computer-executable instructions stored in the memory for execution by the system.
2. The system of claim 1, further comprising: a fifth component configured to, for the first genome comprising a first set of features, identify correlated features from among the first set of features at least in part by: for each feature of the first set of features, applying a feature generator associated with the feature to a training set of data to generate a feature vector for the feature, for at least one pair of feature vectors, calculating a distance between each feature vector of the pair of feature vectors, determining that the calculated distance is less than a distance threshold, in response to determining that the calculated distance is less than a distance threshold, removing, from the first genome, a feature corresponding to at least one feature vector of the pair of feature vectors, wherein each feature vector includes, for each of a plurality of patients, a single value generated by applying a first feature generator to at least one representation of physiological data representative of the patient.
See claim 1.
3. The system of claim 2, wherein the removing, from the first genome, of at least one feature corresponding to at least one feature vector of a first pair of feature vectors comprises: randomly selecting one of feature vector of the first pair of feature vectors, identifying, from among the features of the first genome, a feature corresponding to the randomly selected feature vector; and removing, from the first genome, the identified feature.
2. The system of claim 1, wherein the removing, from the first genome, of at least one feature corresponding to at least one feature vector of a first pair of feature vectors comprises: randomly selecting one feature vector of the first pair of feature vectors, identifying, from among the features of the first genome, a feature corresponding to the randomly selected feature vector; and removing, from the first genome, the identified feature.
4. The system of claim 1, further comprising: a fifth component configured to, for the first genome comprising a first set of features, generate a graph comprising a vertex for each feature of the first set of features; a sixth component configure to generate an edge between vertices whose corresponding features have a correlation value that exceeds a correlation threshold or a distance value that is less than a distance threshold; and a seventh component configured to remove vertices from the graph until no connected vertices remain in the graph.
See claim 1, based on the interpretation that a graph with correlation edges is an abstract representation of what is already in claim 1 without specifying a graph/network expressly.
5. The system of claim 1, further comprising: a machine configured to receive physiological signal data from at least one patient; a fifth component configured to, for each patient, apply at least one of the trained models to at least a portion of the physiological signal data received for the patient by the machine, and generate a prediction for the patient based at least in part on an application of the at least one of the trained models to at least a portion of the received physiological signal.
3. The system of claim 1, further comprising: a machine configured to receive physiological signal data from at least one patient; a sixth component configured to, for each patient, apply at least one of the trained models to at least a portion of the physiological signal data received for the patient by the machine, and generate a prediction for the patient based at least in part on the application of the at least one of the trained models to at least a portion of the received physiological signal.
6. A method, performed by a computing system having a memory and a processor, for discovering machine learning genomes, the method comprising: generating, with the processor, a plurality of genomes, wherein each genome identifies at least one feature and at least one random parameter comprising an index that corresponds to a particular type of at least one machine learning algorithm, the index having a random value assigned thereto; for each generated genome, training at least one model using the generated genome, and producing a fitness score for the genome based at least in part on the trained at least one model; identifying, from among the generated genomes, at least one genome having a fitness score that exceeds a fitness threshold; and mutating each identified genome.
5. A method, performed by a computing system having a memory and a processor, for discovering machine learning genomes, the method comprising: generating, with the processor, a plurality of genomes, wherein each genome identifies at least one feature and at least one parameter for at least one machine learning algorithm; for each generated genome, training at least one model using the generated genome, and producing a fitness score for the generated genome based at least in art on the trained at least one model; identifying, from among the generated genomes at least one genome having a fitness score that exceeds a fitness threshold; and mutating each identified genome, wherein mutating a first identified genome having a first number of features comprises removing at least one feature so that the mutated first identified genome has a second number of features that is different from the first number of features and wherein mutating a second identified genome having a third number of features comprises adding at least one feature so that the mutated second identified genome has a fourth number of features that is different from the third number of features and the second number of features.
8. The method of claim 6, wherein generating the each generated genome further comprises: for each feature, retrieving a feature vector for the feature based at least in part on a feature generator associated with the feature and a training set of data; identifying pairs of correlated feature vectors from among the generated feature vectors; and for each identified pair of correlated feature vectors, identifying one feature vector of the pair of correlated feature vectors, removing, from the genome, the feature associated with the feature generator used to generate the identified feature vector; randomly selecting, from among the set of features, a feature to add to the genome, and adding the randomly selected feature to the first genome.
17. The method of claim 16, wherein generating the first genome further comprises: for each feature of the randomly selected features, retrieving a feature vector for the feature based at least in part on a feature generator associated with the feature and a training set of data; identifying pairs of correlated feature vectors from among the retrieved feature vectors; and for each identified pair of correlated feature vectors, identifying one feature vector of the pair of correlated feature vectors, removing, from the first genome, the feature associated with the feature generator used to generate the identified feature vector; randomly selecting, from among the set of features, a feature to add to the first genome, and adding the randomly selected feature to the first genome.
9. The method of claim 8, wherein identifying pairs of correlated feature vectors comprises: for each pair of feature vectors, calculating a distance metric for the pair of feature vectors, and determining whether the distance metric calculated for the pair of feature vectors is less than a distance threshold, wherein the distance threshold is determined based at least in part on the calculated distance metrics determined for each pair of feature vectors.
18. The method of claim 17, wherein identifying pa