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 .
Claim Status
Claims 1-20 are currently pending and under examination herein.
Claim(s) 1-20 are rejected.
Claim(s) 19-20 are objected to.
Priority
The instant application also claims benefit to U.S. provisional application No. 63104416. Domestic benefit is acknowledged. As such, the effective filing date of claims 1-20 is 10/22/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/22/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. A signed copy of a list of references cited from each IDS is included in this Office Action.
Drawings
The drawings filed on 4/22/2023 are objected to because they contain partial views on different pages that are not numbered in accordance with 37 CFR 1.84(u). Appropriate correction is required.
Specification
The specification filed on 4/22/2023 is accepted.
Claim Objections
Claim 19 is objected to because of the following informalities: the recitation of “comprising computer method”. The claim limitations should read “comprising a computer method”. Appropriate correction is required.
Claim 20 is objected to because of the following informalities: the recitation of “comprising computer method”. The claim limitations should read “comprising the computer method”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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.
Claim 19-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a written description rejection.
The specification does not describe what EDMURA is, its architecture, algorithm, computational methodology, model type, training methodology, or any other characteristics sufficient to identify or implement the trained model. The specification mentions broadly, for example, "providing the data as input to a trained machine learning model, wherein the model is EDMURA" in paragraph [40]. Therefore, the specification does not provide an adequate description of EDMURA and is not conventional or known in the art (see MPEP 2163.I.A). Appropriate correction is required.
Claim 2-8 and 10-20 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.
Regarding claim 2, the claim recites “The method of claim 1, wherein said detecting markers is a Weighted Sample Correlation Network Analysis (WSCNA)”. However, contrary to the applicant’s specification (see paragraphs [36]- [38]) and related claim 11, WSCNA is listed under a type of machine learning model rather than “detecting markers”. It is unclear if this limitation is intended to limit the detecting step or the machine learning model. As the applicant’s specification and related claims describe WSCNA as a machine learning model, the Examiner will interpret WSCNA as a type of machine learning model consistent with claim 11 and the applicant’s specification. Appropriate correction is required.
Regarding claim 10, there is insufficient detail for the terms “AD subtype A, AD subtype B1, AD subtype B2, AD subtype Cl, or AD subtype C2” and they are not common terms within the art. Although the applicant broadly describes each subtype with “including, for example…” in paragraphs [49]-[52], there is insufficient explicit disclosure as to what each subtype is. Of note, the meaning of every term used in a claim should be apparent from the prior art or from the specification and drawings at the time the application is filed. Claim language may not be "ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention." (see MPEP 2173.05(a) and In re Packard, 751 F.3d 1307, 1311, 110 USPQ2d 1785, 1787 (Fed. Cir. 2014)). In addition, when there is more than one meaning for a term, it is incumbent upon applicant to make clear which meaning is being relied upon to claim the invention. Until the meaning of a term or phrase used in a claim is clear, a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate. It is appropriate to compare the meaning of terms given in technical dictionaries in order to ascertain the accepted meaning of a term in the art. In re Barr, 444 F.2d 588, 170 USPQ 330 (CCPA 1971). See also MPEP § 2111.01. Accordingly, the Examiner will interpret the claim as any subtype of Alzheimer’s disease.
Regarding claim 19, claim 19 recites the limitation "comprising using computer implemented method" in line 1. There is insufficient antecedent basis for this limitation in the claim. There are two different methods described and it is unclear whether the method described refers to the method of claim 2 or the computer implemented method. Appropriate correction is required.
Regarding claim 20, recites the limitation "comprising using computer implemented method" in line 1. There is insufficient antecedent basis for this limitation in the claim. There are two different methods described and it is unclear whether the method described refers to the method of claim 2 or the computer implemented method. Appropriate correction is required.
In addition, claims 19-20 recites “EDMURA” as a model without sufficient written description and it is not identified as a recognized machine learning model or algorithm. One of ordinary skill in the art would not be able to determine, with reasonably certainty, the scope of the claimed limitation required that “the model is EDMURA,” therefore, the claims are indefinite (see MPEP 2173). Accordingly, the Examiner will interpret the model as any machine learning model.
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 1-7, 9-16, and 19-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Of note, claims 17-18 are patent eligible as no abstract ideas are recited within the method steps including receiving an AD subtype (that is defined in a product-by-process type limitation) and administering a treatment. Therefore, the claim as a whole is not directed to judicial exception (Step 2A: NO) and thus is eligible at Pathway B, thereby concluding the eligibility analysis (see MPEP 2106.04).
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claim 1 recites a method for treating Alzheimer's Disease in a patient in need thereof, the method comprising: normalizing said marker levels, using a trained machine learning technique to provide a score for the AD subtype of said patient, comparing said score with a predetermined reference standard, and determining said AD subtype.
Claim 2 and 11 recites the method of claim 1 and 10, respectively, wherein said detecting markers is a Weighted Sample Correlation Network Analysis (WSCNA).
Claim 7 recites the method of claim 2, wherein the at least one Alzheimer's Disease subtype is selected from the group consisting of: AD subtype A, AD subtype B1, AD subtype B2, AD subtype C1, and AD subtype C2.
Claim 9 and 16 recites the method of claim 1 and 10, respectively, wherein the trained machine learning technique is selected from the group consisting of: Random Forest, hierarchical clustering, k-means clustering, MEGENA, Bayesian causal network, CNVnator, Pindel, MetaSV, Delly2, Quasipoisson regression, AdaBoost, logistic regression, decision tree, nearest neighbors (KNN), support vector machines (SVM), naive Bayes, multi-layer perceptron, and Ensemble.
Claim 10 recites providing said data as input to a trained machine learning technique, wherein the technique determines the AD subtype based on the data; wherein the AD subtype is any one of: AD subtype A, AD subtype B1, AD subtype B2, AD subtype Cl, or AD subtype C2; and obtaining, from the machine learning technique, the predicted AD subtype.
Claims 19 and 20 recite the method of claim 2 and 11, respectively, further comprising providing the data as input to a trained machine learning model, wherein the model is EDMURA; and obtaining from the model, the drug associated with an Alzheimer's Disease subtype.
The limitations of normalizing market levels, using a trained machine learning technique to provide a score for the AD subtype of said patient, and providing said data as input to a trained machine learning technique, wherein the technique determines the AD subtype based on the data, providing the data as input to a trained machine learning model, wherein the model is EDMURA; and obtaining from the model, the drug associated with an Alzheimer's Disease subtype (as EDMURA is interpreted as any machine learning model) are verbal equivalents of a mathematical concept and therefore falls under the “mathematical concept” grouping of ideas.
The limitations of comparing said score with a predetermined reference standard, determining said AD subtype can be performed in the human mind or with pen and paper and therefore fall under the “mental processes” grouping of ideas. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016)
The limitations of said detecting markers is a Weighted Sample Correlation Network Analysis (WSCNA); at least one Alzheimer's Disease subtype is selected from the group consisting of: AD subtype A, AD subtype B1, AD subtype B2, AD subtype C1, and AD subtype C2; the trained machine learning technique is selected from the group consisting of: Random Forest, hierarchical clustering, k-means clustering, MEGENA, Bayesian causal network, CNVnator, Pindel, MetaSV, Delly2, Quasipoisson regression, AdaBoost, logistic regression, decision tree, nearest neighbors (KNN), support vector machines (SVM), naive Bayes, multi-layer perceptron, and Ensemble merely serve to further limit the abstract idea.
Additionally, these limitations recite a correlation between biomarkers and Alzheimer’s disease which are limited specifically to particular subtypes of interest found in claims 1-7, 9-16, and 19-20 with the correlation between biomarker score and AD subtype being similar to the concept of a correlation between the presence of myeloperoxidase in a bodily sample and cardiovascular disease risk that the courts identified as a natural phenomenon in Cleveland Clinic Foundation V. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017). As such, claims 1-7, 9-16, and 19-20 recite an abstract idea.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites detecting markers associated with at least one Alzheimer's Disease (AD) subtype in a biological sample from said patient and providing a treatment for AD subtype.
Claim 3 and 13 recites the method of claim 2 and 10, respectively, wherein said detecting identifies polynucleotide markers, polypeptide markers, or both.
Claim 4 recites the method of claim 3, wherein said polynucleotide markers are selected from the group consisting of: polynucleotide length, epigenetic markers, methylation levels, nucleotide sequence, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations.
Claim 5 recites the method of claim 3, wherein said detecting identifies polypeptides, epitopes, or fragments.
Claim 6 and 12 recites the method of claim 2 and 10, respectively, wherein said biological sample is selected from the group consisting of: blood, cerebrospinal fluid, lymphatic tissue, cells, epithelial tissue, and adipose tissue.
Claim 8 recites the method of claim 2 comprising providing a therapeutically effective amount of a drug selected from the group consisting of: thioproperazine; nalbuphine; gabexate; mesoridazine; menadione; carbamazepine; diphenidol; epirizole; timolol; mestranol; naphazoline; hesperidin; ethisterone; amlodipine; amsacrine; febuxostat; famciclovir; ezetimibe; carbetocin; orphenadrine; hyoscyamine; amiodarone.hcl; erythromycin- ethylsuccinate; meclizine; dobutamine; phenazopyridine; spironolactone; meclofenamic- acid; parachorophenol; bemegride; ketorolac; and brinzolamide.
Claim 10 recites a computer-implemented method to predict an AD subtype of a subject, the method comprising: obtaining data concerning specific characteristics of a biological sample collected from a subject.
Claim 14 recites the method of claim 13, wherein said polynucleotide markers are selected from the group consisting of: sequence length, epigenetic markers, methylation levels, sequence code, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations.
Claim 15 recites the method of claim 13, wherein said data comprises information concerning polypeptides, epitopes, or fragments.
Claims 19 and 20 recite the method of claim 2 and 11, respectively, further comprising using computer implemented method for identifying candidate compounds for use in treating an Alzheimer's Disease subtype, the method comprising: obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease subtype signatures;
The limitations of detecting markers associated with at least one Alzheimer's Disease (AD) subtype in a biological sample from said patient; a computer-implemented method to predict an AD subtype of a subject, the method comprising: obtaining data concerning specific characteristics of a biological sample collected from a subject, obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease subtype signatures; providing the data as input to a trained machine learning model, wherein the model is EDMURA; and obtaining from the model, the drug associated with an Alzheimer's Disease subtype, equate to insignificant extra solution activity. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
The limitation of providing a treatment for AD subtype merely recite a field of use limitation and does not integrate the judicial exception into a practical application. Of note, the courts have ruled in Parker V. Flook, 437 U.S. 584, 198 USPQ 193 (1978) that limiting an abstract idea to a field of use or adding post solution components does not make the concept patentable. Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The limitations of detecting identifies polynucleotide markers, polypeptide markers, or both; detecting identifies polypeptides, epitopes, or fragments; biological sample is selected from the group consisting of: blood, cerebrospinal fluid, lymphatic tissue, cells, epithelial tissue, and adipose tissue; polynucleotide markers are selected from the group consisting of: polynucleotide length, epigenetic markers, methylation levels, nucleotide sequence, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations; said polynucleotide markers are selected from the group consisting of: sequence length, epigenetic markers, methylation levels, sequence code, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations; said data comprises information concerning polypeptides, epitopes, or fragments serve to merely further limit the data gathering steps but do not change their characterization as data gathering steps and therefore does not integrate the judicial exception into a practical application.
Furthermore, there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-7, 9-16, and 19-20 do not integrate the judicial exception into a practical application.
The limitations of providing a therapeutically effective amount of a drug selected from the group consisting of: therapeutically effective amount of a drug selected from the group consisting of: thioproperazine; nalbuphine; gabexate; mesoridazine; menadione; carbamazepine; diphenidol; epirizole; timolol; mestranol; naphazoline; hesperidin; ethisterone; amlodipine; amsacrine; febuxostat; famciclovir; ezetimibe; carbetocin; orphenadrine; hyoscyamine; amiodarone.hcl; erythromycin- ethylsuccinate; meclizine; dobutamine; phenazopyridine; spironolactone; meclofenamic- acid; parachorophenol; bemegride; ketorolac; and brinzolamide constitute a particular treatment as application or use of the judicial exception meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter (see MPEP 2106.04(d)(2)). As such, claim 8 is patent eligible.
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 1 recites detecting markers associated with at least one Alzheimer's Disease (AD) subtype in a biological sample from said patient and providing a treatment for AD subtype.
Claim 3 and 13 recites the method of claim 2 and 10, respectively, wherein said detecting identifies polynucleotide markers, polypeptide markers, or both.
Claim 4 recites the method of claim 3, wherein said polynucleotide markers are selected from the group consisting of: polynucleotide length, epigenetic markers, methylation levels, nucleotide sequence, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations.
Claim 5 recites the method of claim 3, wherein said detecting identifies polypeptides, epitopes, or fragments.
Claim 6 and 12 recites the method of claim 2 and 10, respectively, wherein said biological sample is selected from the group consisting of: blood, cerebrospinal fluid, lymphatic tissue, cells, epithelial tissue, and adipose tissue.
Claim 10 recites a computer-implemented method to predict an AD subtype of a subject, the method comprising: obtaining data concerning specific characteristics of a biological sample collected from a subject.
Claim 14 recites the method of claim 13, wherein said polynucleotide markers are selected from the group consisting of: sequence length, epigenetic markers, methylation levels, sequence code, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations.
Claim 15 recites the method of claim 13, wherein said data comprises information concerning polypeptides, epitopes, or fragments.
Claims 19 and 20 recite the method of claim 2 and 11, respectively, further comprising using computer implemented method for identifying candidate compounds for use in treating an Alzheimer's Disease subtype, the method comprising: obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease subtype signatures.
The limitations of detecting markers associated with at least one Alzheimer's Disease (AD) subtype in a biological sample from said patient including polynucleotide markers (e.g. sequence length, epigenetic markers, methylation levels, sequence code, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations), polypeptide markers, polypeptides, epitopes, or fragments are well-understood, routine, and conventional activities. The courts have ruled that determining the level of a biomarker in blood by any means as laboratory techniques that are well-understood, routine, conventional activity in the life science arts (see MPEP 2106.05(d) and Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)).
The limitations of using computer implemented method for identifying candidate compounds for use in treating an Alzheimer's Disease subtype, the method comprising: obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease subtype signatures; providing the data as input to a trained machine learning model, wherein the model is EDMURA; and obtaining from the model, the drug associated with an Alzheimer's Disease subtype with each subsequent step is considered a well-understood, routine, and conventional activity as evidenced by Lee (see “Introduction” in paragraph 3 on page 2; many computational drug repositioning methods based on transcriptomic data have been developed to identify potential new indications for drugs and although applications to AD are limited, it is due to tissue availability rather than known protocol).
Excluding claim 8, there are no additional elements that comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-7, 9-16, and 19-20 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 10-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Milind (Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet.).
Regarding claim 10, Milind teaches:
A computer-implemented method (software explicitly mentioned in first paragraph of “Single-variant association mapping of submodule eigengenes across cohorts” on page 4) to predict an AD subtype of a subject (clustering subtype prediction seen in Fig. 1), the method comprising: obtaining data concerning specific characteristics of a biological sample collected from a subject (brain samples obtained from cohorts wherein each sample is derived from a particular brain region in “Results” on page 3 with multiple variables are used in the normalization process); providing said data as input to a trained machine learning technique (clustering technique used in the iterativeWGCNA application in the classification pipeline as seen in Fig. 1 on page 3), wherein the technique determines the AD subtype based on the data; wherein the AD subtype is any one of: AD subtype A, AD subtype B1, AD subtype B2, AD subtype Cl, or AD subtype C2 (different LOAD subtypes identified based on their eigengene expression in part C in Fig. 1); and obtaining, from the machine learning technique, the predicted AD subtype (subsequent identification and quantitative trait mapping described in Fig. 1).
Regarding claim 11, Milind teaches:
The method of claim 10, wherein said machine learning technique is a Weighted Sample Correlation Network Analysis (WSCNA) (see “Abstract” on page 1 for dimensionality reduction and gene isolation). The Examiner notes that although the applicant recites the technique as WSCNA and the prior art recites it as WGCNA, both methods use the same steps in analyzing gene expression across various tissues and diseases and are considered the gold standard for these types of analyses. Therefore, under the broadest reasonable interpretation, they are functionally equivalent and meets the limitation of claim 11.
Regarding claim 12, Milind teaches:
The method of claim 10, wherein said biological sample is selected from the group consisting of: blood, cerebrospinal fluid, lymphatic tissue, cells, epithelial tissue, andadipose tissue (brain samples obtained from cohorts wherein each sample is derived from a particular brain region in “Results” on page 3 with multiple variables are used in the normalization process). The Examiner notes that under the broadest reasonable interpretation, neurons are considered specific types of cells and therefore meets the limitations of claim 12.
Regarding claim 13,
Millind teaches the method of claim 10, wherein said data comprises information concerning polynucleotide markers, polypeptide markers, or both (iterativeWGCNA approach includes incorporating information from cell-type-specific markers derived from bulk and single cell RNA sequencing; see “Refinement of 26 human co-expression modules identifies disease-associated transcriptomic signals” on page 4 and Fig. 2)
Regarding claim 14, Millind teaches:
The method of claim 13, wherein said polynucleotide markers are selected from the group consisting of: sequence length, epigenetic markers, methylation levels, sequence code, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations (iterativeWGCNA approach includes incorporating information from cell-type-specific markers derived from bulk and single cell RNA sequencing refined as co-expression modules; see “Refinement of 26 human co-expression modules identifies disease-associated transcriptomic signals” on page 4 and Fig. 2)
Regarding claim 15, Chang teaches:
The method of claim 13, wherein said data comprises information concerning polypeptides, epitopes, or fragments (co-expression modules described above are specific to pathways associated with LOAD AD subtypes including that of tau-protein kinase activity; see “Refinement of 26 human co-expression modules identifies disease-associated transcriptomic signals” on page 4 and Fig. 2).
Regarding claim 16, Milind teaches:
The method of claim 10, wherein the trained machine learning technique is selected from the group consisting of: Random Forest, hierarchical clustering, k-means clustering, MEGENA, Bayesian causal network, CNVnator, Pindel, MetaSV, Delly2, Quasipoisson regression, AdaBoost, logistic regression, decision tree, nearest neighbors (KNN), support vector machines (SVM), naive Bayes, multi-layer perceptron, and Ensemble (clustering method seen in Fig. 1 on page 3).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claim(s) 1-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (WO2019169007A1) as filed in the IDS on 4/22/2023, in view of Milind et al (Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet.).
Regarding claim 1, Chang teaches:
A method for treating Alzheimer's Disease in a patient in need thereof, the method comprising:
detecting markers associated with at least Alzheimer's Disease (AD) in a biological sample from said patient (see [0050] and Fig. 3 for explicit recitation of collection of co-expression and/or differentially expressed gene signature, and/or other biomarkers)
normalizing said marker levels (RNA profiles had been normalized using disclosed processes in paragraph [0053]),
using a trained machine learning technique to provide a score for the disease identification (such as AD) of said patient (see the details of a Bayesian neural network that generates a score in [0024] and subsequently evaluates the fitness to the data set to be used in disease phenotype identification as recited in [0013]).
and providing a treatment for said AD (HSPA2 gene identified as an intervention point for therapy recited in [0048]).
Although Chang does teach a general machine learning algorithm to identify AD for treatment purposes, he does not teach the specific method of machine learning technique using score comparison for identification of AD subtypes. Milind teaches a similar method of using biomarkers to implement into a machine learning algorithm comprising: comparing said score with a predetermined reference standard (Euclidean distance comparison from centroid in clustering identification in Fig. 1 on page 3) and determining said AD subtypes (Distinct LOAD subtypes were identified for all three study cohorts from association testing in “Abstract” page 1; see also Fig. 1 on page 3). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Milind’s score-based subtype identification technique into Chang’s larger identification pipeline in order to stratify Late-Onset Alzheimer’s disease into distinct molecular subtypes based on affected disease pathways to address the lack of detection on new risk variants as evidenced by Milind (see “Abstract” and “Author Summary” on page 1). This incorporation would have been accomplished with reasonable expectation of success as both methods operate in the same field of endeavor of identifying AD for targeted therapies.
Regarding claim 2,
Milind teaches the method of claim 1, wherein said detecting markers is a Weighted Sample Correlation Network Analysis (WSCNA) (see “Abstract” on page 1 for dimensionality reduction and gene isolation). The Examiner notes that although the applicant recites the technique as WSCNA and the prior art recites it as WGCNA, both methods use the same steps in analyzing gene expression across various tissues and diseases and are considered the gold standard for these types of analyses. Therefore, under the broadest reasonable interpretation, they are functionally equivalent and meets the limitation of claim 2.
Regarding claim 3, Chang teaches:
The method of claim 2, wherein said detecting identifies polynucleotide markers, polypeptide markers, or both (identification and quantification of peptides described in data collection step recited in [0053]).
Regarding claim 4, Chang teaches:
The method of claim 3, wherein said polynucleotide markers are selected from the group consisting of: polynucleotide length, epigenetic markers, methylation levels, nucleotide sequence, copy number, single nucleotide polymorphisms, sequence expression levels, RNA expression, RNA stability, and sequence transpositions or translocations (see step 310 which incorporates ENCODE/RoadMap epigenetic data (e.g. WGS and SNP data) in [0050]).
Regarding claim 5, Chang teaches:
The method of claim 3, wherein said detecting identifies polypeptides, epitopes, or fragments (identification and quantification of peptides described in data collection step recited in [0053]).
Regarding claim 6, Chang teaches:
The method of claim 2, wherein said biological sample is selected from the group consisting of: blood, cerebrospinal fluid, lymphatic tissue, cells, epithelial tissue, and adipose tissue (see [0069] for explicit recitation of blood extraction from target groups for pipeline analysis).
Regarding claim 7, Milind teaches:
The method of claim 2, wherein the at least one Alzheimer's Disease subtype is selected from the group consisting of: AD subtype A, AD subtype B1, AD subtype B2, AD subtype C1, and AD subtype C2 (Distinct LOAD subtypes were identified for all three study cohorts from association testing in “Abstract” page 1; see also Fig. 1 on page 3).
Regarding claim 9, Milind teaches:
The method of claim 1, wherein the trained machine learning technique is selected from the group consisting of: Random Forest, hierarchical clustering, k-means clustering, 62 MEGENA, Bayesian causal network, CNVnator, Pindel, MetaSV, Delly2, Quasipoisson regression, AdaBoost, logistic regression, decision tree, nearest neighbors (KNN), support vector machines (SVM), naive Bayes, multi-layer perceptron, and Ensemble (clustering method seen in Fig. 1 on page 3).
Claim 17 is rejected under 35 U.S.C. as being unpatentable over Milind (Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet.).
Regarding claim 17, Milind teaches:
A method for treating Alzheimer's in a subject in need thereof, the method comprising: receiving the AD subtype of the subject, which has been obtained using the method of receiving the AD subtype of the subject (different LOAD subtypes identified based on their eigengene expression in part C in Fig. 1), which has been obtained using the method of 11 and therapeutically effective amount of drug for targeting the obtained AD subtype (dissection of genetic loci can serve as the basis for treatment based on subtype as recited in “Discussion” on page 11-12 in paragraph 4). Although Milind does not explicitly state the administration of the drug towards the AD subtype, it would be prima facie obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify the Milind’s subtype identification pipeline to include the administration of a drug as Millind already identifies genetic loci as the basis of treatment in his disclosure (see Discussion” on page 11-12 in paragraph 4).
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (WO2019169007A1) as filed in the IDS on 4/22/2023, and Milind et al (Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet.) as applied to claim 2 above, in view of Bamdad (US20030060487A1) as filed in the IDS on 4/22/2023.
Regarding claim 8,
Chang and Milind disclose the claimed invention substantially as stated above.
Chang as modified does not teach the method of claim 2, comprising providing a therapeutically effective amount of a drug selected from the group consisting of: thioproperazine; nalbuphine; gabexate; mesoridazine; menadione; carbamazepine; diphenidol; epirizole; timolol; mestranol; naphazoline; hesperidin; ethisterone; amlodipine; amsacrine; febuxostat; famciclovir; ezetimibe; carbetocin; orphenadrine; hyoscyamine; amiodarone.hcl; erythromycin- ethylsuccinate; meclizine; dobutamine; phenazopyridine; spironolactone; meclofenamic- acid; parachorophenol; bemegride; ketorolac; and brinzolamide. Bamdad teaches a method for providing a therapeutically effective treatment of neurodegenerative disease such as Alzheimer’s disease with spironolactone (administration of spironolactone recited in [0234]). Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date of the claimed invention to incorporate Bamded’s specific treatment into Chang as modified’s pipeline in order to address the need for new drugs for treatment of diseases with abnormal protein aggregation as recited by Bamded (see [0012]). This incorporation would have been accomplished with reasonable expectation of success as Chang already identifies targeted treatment with his identification of the HSPA2 gene (HSPA2 gene identified as an intervention point for therapy recited in [0048]).
Regarding claim 18, Bamded teaches:
The method of claim 17, comprising administering a therapeutically effective amount of a drug selected from the group consisting of: thioproperazine; nalbuphine; gabexate;mesoridazine; menadione; carbamazepine; diphenidol; epirizole; timolol; mestranol; naphazoline; hesperidin; ethisterone; amlodipine; amsacrine; febuxostat; famciclovir; ezetimibe; carbetocin; orphenadrine; hyoscyamine; amiodarone.hcl; erythromycin- ethylsuccinate; meclizine; dobutamine; phenazopyridine; spironolactone; meclofenamic- acid; parachorophenol; bemegride; ketorolac; and brinzolamide (see rejection on claim 8).
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (WO2019169007A1) as filed in the IDS on 4/22/2023, and Milind et al (Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology. PLoS Genet.) as applied to claims 2 and 11 above, in view of Lee et al (A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease. Front Pharmacol.)
Regarding claim 19,
Lee teaches the method of claim 2, further comprising using computer implemented method for identifying candidate compounds for use in treating an Alzheimer's Disease (see “Abstract”; page 1 wherein a computational drug repositioning method for Alzheimer’s disease is taught) the method comprising: obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease (see “Abstract” in first paragraph on page 1 where a Gene Peturbation Signature database is created; AD subtype signatures are generated from RNA-seq, microarray, and proteomic datasets also disclosed); providing the data as input to a trained machine learning model, wherein the model is EDMURA and obtaining from the model (DRPS/C computational model is fed using the disease and drug signatures and subsequently classified to predict anti-AD drug candidates; see “Abstract” page 1), the drug associated with an Alzheimer's Disease. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorportate Lee’s drug identification method into Chang as modified’s subtyping pipeline in order to predict novel potential anti-neuronal drug candidates based on biological multi-omics signatures (see “Discussion” in the last paragraph on page 9). This incorporation would have been accomplished with reasonable expectation of success as both operate in the same field of endeavor as Chang already identifies targeted treatment with his identification of the HSPA2 gene (HSPA2 gene identified as an intervention point for therapy recited in [0048]).
Regarding claim 20,
The method of claim 11, further comprising using computer implemented method for identifying candidate compounds for use in treating an Alzheimer's Disease subtype, the method comprising: obtaining data of drug induced signatures for candidate compounds and Alzheimer's Disease subtype signatures; providing the data as input to a trained machine learning model, wherein the model is EDMURA; and obtaining from the model, the drug associated with an Alzheimer's Disease subtype (see rejection above on claim 19).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liang et al. (Application of Weighted Gene Co-Expression Network Analysis to Explore the Key Genes in Alzheimer's Disease. J Alzheimers Dis. 2018) discusses the application of Weighted Gene Co-Expression Network Analysis to explore the key genes in Alzheimer's Disease.
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/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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