Detailed Notice
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 .
Status of Claims
Claims 1-20 are currently pending.
Claim 1 is amended.
Claims 2-20 are new.
Claims 1-20 are rejected.
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 7, 14, and 19 are 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 7 recites a dependency on claim 6, which is dependent on claim 1. In its current form, claim 7 recites “determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated with a diagnosis of the glycogen storage disease condition” which lacks antecedent basis. For the purposes of examination, claim 7 was interpreted to be recite “determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated with a diagnosis of the lysosomal storage disease condition”. Claims 14 and 19 recite similar issues and will be interpreted similarly, however claim 14 will still depend from claim 13, and claim 19 will still depend from claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-7 are directed toward a computer-implemented method (i.e. a process), claims 8-14 are directed toward a non-transitory media (i.e., manufacture), and claims 15-20 are directed toward a system (i.e. machine). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 8, and 15 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer-implemented method, comprising: generating an electronic multi-variable logistic regression statistical model capable of estimating a level of probability or level of severity of lysosomal storage disorder using a plurality of variables, wherein: the electronic multi-variable logistic regression statistical model is generated, based on an algorithm selected from a group comprising at least a support vector machine (SVM) learning technique and a gradient boosting machine learning technique, to reduce an initial dimensionality associated with a set of patient information and associated with a statistical analysis relating to the set of patient information; receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual, the data set including a time stamps associated with the test results, and the laboratory test results determined from measurements received at multiple measurement-session times; determining a probability of the lysosomal storage disorder for the target individual or a severity of the lysosomal storage disorder for the target individual based on the input data set and the electronic multi-variable logistic regression statistical model; modifying an electronic medical record associated with the target individual according to whether the determined probability or severity of the lysosomal storage disorder for the target individual indicates that the target individual is or is not a candidate for additional diagnostic testing and treatment; and based on the determined probability or severity of the lysosomal storage disorder for the target individual, determined from the input data set and the electronic multi-variable logistic regression statistical model, initiating an intervention, wherein the intervention comprises undertaking additional diagnostic or prognostic enzymatic or genetic testing directed to one or more specific lysosomal storage disorders”.
The limitations of estimating a level of probability or level of severity of lysosomal storage disorder using a plurality of variables, wherein: reduce an initial dimensionality associated with a set of patient information and associated with a statistical analysis relating to the set of patient information; receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual, the data set including a time stamps associated with the test results, and the laboratory test results determined from measurements received at multiple measurement-session times; determining a probability of the lysosomal storage disorder for the target individual or a severity of the lysosomal storage disorder for the target individual based on the input data set and the model; modifying an electronic medical record associated with the target individual according to whether the determined probability or severity of the lysosomal storage disorder for the target individual indicates that the target individual is or is not a candidate for additional diagnostic testing and treatment; and based on the determined probability or severity of the lysosomal storage disorder for the target individual, determined from the input data set and the model, initiating an intervention, wherein the intervention comprises undertaking additional diagnostic or prognostic enzymatic or genetic testing directed to one or more specific lysosomal storage disorders, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of estimating, receiving, determining, modifying, and initiating, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 8 and 15 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2-7, 9-14, and 16-20 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 15. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “non-transitory media”, “hardware processors”, and “electronic multi-variable logistic statistical model”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 3, and [0015]-[0017], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “generating an electronic multi-variable logistic regression statistical model capable of”, “the electronic multi-variable logistic regression statistical model is generated, based on an algorithm selected from a group comprising at least a support vector machine (SVM) learning technique and a gradient boosting machine learning technique, to”, and “the electronic multi-variable logistic regression statistical model”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-7, 9-14, and 16-20 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 8, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-7, 9-14, and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 8, and 15, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
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)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Naughton et al. (US 20190384777 A1), hereinafter Naughton, in view of Deciu et al. (US 20170316150 A1), hereinafter Deciu, and Cella et al. (US 20190227536 A1), hereinafter Cella, and Wolf et al. (US 20140171491 A1), hereinafter Wolf.
Regarding claims 1, 8, and 15, Naughton teaches a computer-implemented method, comprising: generating an electronic multi-variable logistic regression statistical model capable of estimating a level of probability or level of severity of… disorder using a plurality of variables (Naughton, [0027]: “model is determined for making a phenotype-related prediction given an individual's genetic, family history, and environmental information. In some embodiments, modeling techniques (e.g., machine learning techniques such as regularized logistic regression, decision tree, support vector machine, etc.) are applied to all or some of the member information to train a model for predicting the likelihood associated with a phenotype such as a disease as well as the likelihood of having a non-disease related genotype such as eye color, height, etc.”, and [0032]: “In one example where logistic regression is performed, for each row of the table, the genetic and environmental information is encoded as a multidimensional vector”), wherein: the electronic multi-variable logistic regression statistical model is generated, based on an algorithm selected from a group comprising at least a support vector machine (SVM) learning technique (Naughton, [0027]: “model is determined for making a phenotype-related prediction given an individual's genetic, family history, and environmental information. In some embodiments, modeling techniques (e.g., machine learning techniques such as regularized logistic regression, decision tree, support vector machine, etc.) are applied to all or some of the member information to train a model for predicting the likelihood associated with a phenotype such as a disease as well as the likelihood of having a non-disease related genotype such as eye color, height, etc.” and [0032]-[0035]: “In one example where logistic regression is performed, for each row of the table, the genetic and environmental information is encoded as a multidimensional vector”); determining a probability of the… disorder for the target individual or a severity of the… disorder for the target individual based on the input data set and the electronic multi-variable logistic regression statistical model (Naughton, [0012]: “A phenotype prediction engine 156 is used to predict a phenotype given certain information about an individual. Phenotypes that can be predicted by the prediction engine include disease as well as non-disease related traits, such as height, weight, body mass index (BMI), cholesterol levels, etc. The types of predictions include but are not limited to the probability of a disease occurring over the course of an individual's lifetime, the probability of a disease occurring within a specific time frame, the probability that the individual currently has the disease, estimates of the value of a quantitative measurement, or estimates of the distribution of likely measurements” and [0033]: “As specified above, for any vector x, the logistic regression model outputs a value between 0 and 1 indicating the probability that an individual with encoded features x will report having developed the phenotype such as a disease”); modifying an electronic medical record associated with the target individual according to whether the determined probability or severity of the… disorder for the target individual indicates that the target individual is or is not a candidate for additional diagnostic testing and treatment (Naughton, [0045]: “If the predicted result exceeds the threshold, the system may provide the individual with additional feedback or suggestions such as getting additional testing that more precisely targets the genotype in question and requesting the user to provide additional phenotypic, family, and/or environmental information to improve the prediction, etc. If the individual provides the additional information requested, the prediction process may be re-executed while taking into account the additional information”); and based on the determined probability or severity of the… disorder for the target individual, determined from the input data set and the electronic multi-variable logistic regression statistical model (Naughton, [0012]: “A phenotype prediction engine 156 is used to predict a phenotype given certain information about an individual. Phenotypes that can be predicted by the prediction engine include disease as well as non-disease related traits, such as height, weight, body mass index (BMI), cholesterol levels, etc. The types of predictions include but are not limited to the probability of a disease occurring over the course of an individual's lifetime, the probability of a disease occurring within a specific time frame, the probability that the individual currently has the disease, estimates of the value of a quantitative measurement, or estimates of the distribution of likely measurements” and [0045]: “If the predicted result exceeds the threshold, the system may provide the individual with additional feedback or suggestions such as getting additional testing that more precisely targets the genotype in question and requesting the user to provide additional phenotypic, family, and/or environmental information to improve the prediction, etc. If the individual provides the additional information requested, the prediction process may be re-executed while taking into account the additional information”), initiating an intervention, wherein the intervention comprises undertaking additional diagnostic testing (Naughton, [0012]: “A phenotype prediction engine 156 is used to predict a phenotype given certain information about an individual. Phenotypes that can be predicted by the prediction engine include disease as well as non-disease related traits, such as height, weight, body mass index (BMI), cholesterol levels, etc. The types of predictions include but are not limited to the probability of a disease occurring over the course of an individual's lifetime, the probability of a disease occurring within a specific time frame, the probability that the individual currently has the disease, estimates of the value of a quantitative measurement, or estimates of the distribution of likely measurements” and [0045]: “If the predicted result exceeds the threshold, the system may provide the individual with additional feedback or suggestions such as getting additional testing that more precisely targets the genotype in question and requesting the user to provide additional phenotypic, family, and/or environmental information to improve the prediction, etc. If the individual provides the additional information requested, the prediction process may be re-executed while taking into account the additional information” or prognostic enzymatic or genetic testing (Naughton, [0045]: “suggestions such as getting additional testing that more precisely targets the genotype in question and requesting the user to provide additional phenotypic, family, and/or environmental information to improve the prediction, etc.”).
Naughton further teaches one or more non-transitory media having computer-readable instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to facilitate a plurality of operations (Naughton, [0015]-[0019]) of claim 8 and system having one or more hardware processors configured to facilitate a plurality of operations (Naughton, [0015]-[0019]) of claim 15.
Naughton does not teach a statistical analysis relating to the set of patient information; receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual, the data set including a time stamps associated with the test results, and the laboratory test results determined from measurements received at multiple measurement-session times.
However, Deciu teaches a statistical analysis relating to the set of patient information (Deciu, [0175]: “a suitable wavelet decomposition generating process is represented in S or R code or by a package (e.g., an R package). R, R source code, R programs, R packages and R documentation for wavelet decomposition generating processes are available for download from a CRAN or CRAN mirror site (e.g., The Comprehensive R Archive Network (CRAN); World Wide Web URL cran.us.r-project.org). CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R”, [0212]: “processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets”, [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”, and [0224]: “The one or more filtering and/or normalizing procedures sometimes can reduce data set complexity and/or dimensionality, in some embodiments. An outcome can be provided based on a data set of reduced complexity and/or dimensionality”); receiving an input data set of content corresponding to instances of the set of patient information for a target individual based on laboratory test results for the target individual (Deciu, [0322]: “Counts measured for a test sample and are in a test region (e.g., a set of portions of interest) are referred to as “test counts” herein. Test counts sometimes are processed counts, averaged or summed counts, a representation, normalized counts, or one or more levels or levels as described herein”, [0323]: “In some embodiments reference counts and test counts are obtained from the same sample and/or the same subject. In some embodiments reference counts are from different samples and/or from different subjects. In some embodiments reference counts are determined from and/or compared to a corresponding segment of the genome from which the test counts are derived and/or determined”), the data set including a time stamps associated with the test results (Deciu, [0048]: “Nucleic acid may be isolated at a different time point as compared to another nucleic acid, where each of the samples is from the same or a different source”, [0137]: “Each iterative addition of a nucleotide is detected and the process is repeated multiple times until a sequence of a nucleic acid strand is obtained”, and [0213]: “processing steps may be the same step repeated two or more times (e.g., filtering two or more times, normalizing two or more times), and in certain embodiments, processing steps may be two or more different processing steps (e.g., filtering, normalizing; normalizing, monitoring peak heights and edges; filtering, normalizing, normalizing to a reference, statistical manipulation to determine p-values, and the like), carried out simultaneously or sequentially”), and the laboratory test results determined from measurements received at multiple measurement-session times (Deciu, [0048]: “Nucleic acid may be isolated at a different time point as compared to another nucleic acid, where each of the samples is from the same or a different source”, [0137]: “Each iterative addition of a nucleotide is detected and the process is repeated multiple times until a sequence of a nucleic acid strand is obtained”, and [0213]: “processing steps may be the same step repeated two or more times (e.g., filtering two or more times, normalizing two or more times), and in certain embodiments, processing steps may be two or more different processing steps (e.g., filtering, normalizing; normalizing, monitoring peak heights and edges; filtering, normalizing, normalizing to a reference, statistical manipulation to determine p-values, and the like), carried out simultaneously or sequentially”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton to incorporate the teachings of Deciu and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Naughton and Deciu do not teach a gradient boosting machine learning technique, to reduce an initial dimensionality associated with a set of patient information.
However, Cella teaches a gradient boosting machine learning technique, to reduce an initial dimensionality associated with a set of patient information (Cella, [0345]: “machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton and Deciu to incorporate the teachings of Cella and account for interpreting data from a plurality of input sensors, wherein each of the plurality of input sensors is operationally coupled to a component of an industrial environment. Methods including operating a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data, determining a reduced dimensionality view of the data in response to the determined structure in the data, wherein the reduced dimensionality view includes fewer dimensions than the data from the plurality of input sensors, and providing the reduced dimensionality view to a user interface (Cella, Abstract and [0010]-[0013]).
Naughton, Deciu, and Cella do not teach lysosomal storage disorder and directed to one or more specific lysosomal storage disorders.
However, Wolf teaches lysosomal storage disorder (Wolf, [0005]: “Lysosomal storage diseases include, but are not limited to… glycogen storage disease I; glycogen storage disease Ib; glycogen storage disease Ic; glycogen storage disease III; glycogen storage disease IV; glycogen storage disease V; glycogen storage disease VI; glycogen storage disease VII; glycogen storage disease ”) and directed to one or more specific lysosomal storage disorders (Wolf, [0005]: “Lysosomal storage diseases include, but are not limited to… glycogen storage disease I; glycogen storage disease Ib; glycogen storage disease Ic; glycogen storage disease III; glycogen storage disease IV; glycogen storage disease V; glycogen storage disease VI; glycogen storage disease VII; glycogen storage disease”, and [0022]-[0023]: ““Treating” or “treatment” within the meaning herein refers to an alleviation of symptoms associated with a disorder or disease, “inhibiting” means inhibition of further progression or worsening of the symptoms associated with the disorder or disease, and “preventing” refers to prevention of the symptoms associated with the disorder or disease… an “effective amount” or a “therapeutically effective amount” of an agent of the invention e.g., a lysosomal storage enzyme or recombinant AAV encoding a lysosomal storage enzyme, refers to an amount of the agent that alleviates, in whole or in part, symptoms associated with the disorder or condition, or halts or slows further progression or worsening of those symptoms, or prevents or provides prophylaxis for the disorder or condition, e.g., an amount that is effective to prevent, inhibit or treat in the individual one or more neurological symptoms”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton, Deciu, and Cella to incorporate the teachings of Wolf and account for a method to prevent, inhibit or treat one or more neurological symptoms associated with a lysosomal storage disease in a mammal in need thereof, which includes intranasally administering to the mammal a composition comprising an effective amount of a lysosomal storage enzyme or a recombinant adeno-associated virus vector comprising an open reading frame encoding a lysosomal storage enzyme (Wolf, Abstract and [0003]-[0004]).
Regarding claims 2, 9, and 16, Naughton, Cella, and Wolf does not teach the SVM learning technique utilizes or is associated with an R-system operation, and further comprising generating the electronic multi-variable logistic regression statistical model based on one or both of an SVM algorithm and the R-system operation.
However, Deciu teaches the SVM learning technique utilizes or is associated with an R-system operation, and further comprising generating the electronic multi-variable logistic regression statistical model based on one or both of an SVM algorithm and the R-system operation (Deciu, [0175]: “a suitable wavelet decomposition generating process is represented in S or R code or by a package (e.g., an R package). R, R source code, R programs, R packages and R documentation for wavelet decomposition generating processes are available for download from a CRAN or CRAN mirror site (e.g., The Comprehensive R Archive Network (CRAN); World Wide Web URL cran.us.r-project.org). CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R”, [0212]: “processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets”, [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”, and [0224]: “The one or more filtering and/or normalizing procedures sometimes can reduce data set complexity and/or dimensionality, in some embodiments. An outcome can be provided based on a data set of reduced complexity and/or dimensionality”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton to incorporate the teachings of Naughton, Cella, and Wolf and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Regarding claims 3, 10, and 17, Naughton, Deciu, and Wolf do not teach generating the electronic multi-variable logistic regression statistical model based on a gradient boosting machine learning algorithm.
However, Cella teaches generating the electronic multi-variable logistic regression statistical model based on a gradient boosting machine learning algorithm (Cella, [0345]: “machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may also be classified as machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton, Deciu, and Wolf to incorporate the teachings of Cella and account for interpreting data from a plurality of input sensors, wherein each of the plurality of input sensors is operationally coupled to a component of an industrial environment. Methods including operating a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data, determining a reduced dimensionality view of the data in response to the determined structure in the data, wherein the reduced dimensionality view includes fewer dimensions than the data from the plurality of input sensors, and providing the reduced dimensionality view to a user interface (Cella, Abstract and [0010]-[0013]).
Regarding claims 4, 11, and 18, Naughton, Cella, and Wolf does not teach reducing a particular dimensionality, associated with one or both of the set of patient information and the statistical analysis relating to the set of patient information, based on the generating of the electronic multi-variable logistic regression statistical model.
However, Deciu teaches reducing a particular dimensionality, associated with one or both of the set of patient information and the statistical analysis relating to the set of patient information, based on the generating of the electronic multi-variable logistic regression statistical model (Deciu, [0175]: “a suitable wavelet decomposition generating process is represented in S or R code or by a package (e.g., an R package). R, R source code, R programs, R packages and R documentation for wavelet decomposition generating processes are available for download from a CRAN or CRAN mirror site (e.g., The Comprehensive R Archive Network (CRAN); World Wide Web URL cran.us.r-project.org). CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R”, [0212]: “processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets”, [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”, and [0224]: “The one or more filtering and/or normalizing procedures sometimes can reduce data set complexity and/or dimensionality, in some embodiments. An outcome can be provided based on a data set of reduced complexity and/or dimensionality”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton to incorporate the teachings of Naughton, Cella, and Wolf and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Regarding claims 5, 12, and 20, Naughton, Cella, and Wolf does not teach utilizing a feature selection operation to reduce a particular dimensionality associated with one or both of the set of patient information and the statistical analysis relating to the set of patient information.
However, Deciu teaches utilizing a feature selection operation to reduce a particular dimensionality associated with one or both of the set of patient information and the statistical analysis relating to the set of patient information (Deciu, [0175]: “a suitable wavelet decomposition generating process is represented in S or R code or by a package (e.g., an R package). R, R source code, R programs, R packages and R documentation for wavelet decomposition generating processes are available for download from a CRAN or CRAN mirror site (e.g., The Comprehensive R Archive Network (CRAN); World Wide Web URL cran.us.r-project.org). CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R”, [0211]: “In certain embodiments a threshold is exceeded by results obtained by methods described herein and a subject is diagnosed with a genetic variation (e.g. trisomy 21). A threshold value or range of values often is calculated by mathematically and/or statistically manipulating sequence read data (e.g., from a reference and/or subject), in some embodiments, and in certain embodiments, sequence read data manipulated to generate a threshold value or range of values is sequence read data (e.g., from a reference and/or subject)”, [0212]: “processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets”, [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”, and [0224]: “The one or more filtering and/or normalizing procedures sometimes can reduce data set complexity and/or dimensionality, in some embodiments. An outcome can be provided based on a data set of reduced complexity and/or dimensionality”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton to incorporate the teachings of Naughton, Cella, and Wolf and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Regarding claims 6 and 13, Naughton further teaches initiating an SVM computation to identify, from a plurality of input variables corresponding to the plurality of variables, a subset of input variables (Naughton, [0027]: “a model is determined for making a phenotype-related prediction given an individual's genetic, family history, and environmental information. In some embodiments, modeling techniques (e.g., machine learning techniques such as regularized logistic regression, decision tree, support vector machine, etc.) are applied to all or some of the member information to train a model for predicting the likelihood associated with a phenotype such as a disease as well as the likelihood of having a non-disease related genotype such as eye color, height, etc. In some embodiments, the models are derived based on parameters published in scientific literature and/or a combination of literature and learned parameters. As will be described in greater detail below, in some embodiments, the model will account for, among other things, genetic inheritance and any known relationships between genetic information and the phenotype”).
Regarding claim 7 Naughton and Cella does not teach determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated.
However, Deciu teaches determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated (Deciu, [0075]: “In some embodiments a fitted relations is chosen from a decision tree model, support-vector machine model and neural network model” and [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton to incorporate the teachings of Deciu and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Naughton, Cella, and Deciu do not teach with a diagnosis of the glycogen storage disease condition.
However, Wolf teaches with a diagnosis of the glycogen storage disease condition (Wolf, [0005]: “Lysosomal storage diseases include, but are not limited to… glycogen storage disease I; glycogen storage disease Ib; glycogen storage disease Ic; glycogen storage disease III; glycogen storage disease IV; glycogen storage disease V; glycogen storage disease VI; glycogen storage disease VII; glycogen storage disease ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton, Deciu, and Cella to incorporate the teachings of Wolf and account for a method to prevent, inhibit or treat one or more neurological symptoms associated with a lysosomal storage disease in a mammal in need thereof, which includes intranasally administering to the mammal a composition comprising an effective amount of a lysosomal storage enzyme or a recombinant adeno-associated virus vector comprising an open reading frame encoding a lysosomal storage enzyme (Wolf, Abstract and [0003]-[0004]).
Regarding claim 19 Naughton teaches initiating an SVM computation to identify, from a plurality of input variables corresponding to the plurality of variables, a subset of input variables (Naughton, [0027]: “a model is determined for making a phenotype-related prediction given an individual's genetic, family history, and environmental information. In some embodiments, modeling techniques (e.g., machine learning techniques such as regularized logistic regression, decision tree, support vector machine, etc.) are applied to all or some of the member information to train a model for predicting the likelihood associated with a phenotype such as a disease as well as the likelihood of having a non-disease related genotype such as eye color, height, etc. In some embodiments, the models are derived based on parameters published in scientific literature and/or a combination of literature and learned parameters. As will be described in greater detail below, in some embodiments, the model will account for, among other things, genetic inheritance and any known relationships between genetic information and the phenotype”).
Naughton and Cella does not teach determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated.
However, Deciu teaches determining, based on the SVM computation, that members of the subset of input variables are statistically significantly associated (Deciu, [0075]: “In some embodiments a fitted relations is chosen from a decision tree model, support-vector machine model and neural network model” and [0223]: “a data set can be analyzed by utilizing multiple (e.g., 2 or more) statistical algorithms (e.g., least squares regression, principle component analysis, linear discriminant analysis, quadratic discriminant analysis, bagging, neural networks, support vector machine models, random forests, classification tree models, K-nearest neighbors, logistic regression and/or loss smoothing) and/or mathematical and/or statistical manipulations (e.g., referred to herein as manipulations)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton and Cella to incorporate the teachings of Deciu and account for non-invasive assessment of genetic variations that make use of nucleic acid fragments from circulating cell free nucleic acid (Deciu, Abstract and [0004]-[0005]).
Naughton, Deciu, and Cella do not teach with a diagnosis of the glycogen storage disease condition.
However, Wolf teaches with a diagnosis of the glycogen storage disease condition (Wolf, [0005]: “Lysosomal storage diseases include, but are not limited to… glycogen storage disease I; glycogen storage disease Ib; glycogen storage disease Ic; glycogen storage disease III; glycogen storage disease IV; glycogen storage disease V; glycogen storage disease VI; glycogen storage disease VII; glycogen storage disease ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Naughton, Deciu, and Cella to incorporate the teachings of Wolf and account for a method to prevent, inhibit or treat one or more neurological symptoms associated with a lysosomal storage disease in a mammal in need thereof, which includes intranasally administering to the mammal a composition comprising an effective amount of a lysosomal storage enzyme or a recombinant adeno-associated virus vector comprising an open reading frame encoding a lysosomal storage enzyme (Wolf, Abstract and [0003]-[0004]).
Conclusion
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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