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 .
Status of Claims
Claims 6, 9-11, 17, 19, 23-31 and 34-40 are cancelled.
Claims 1-5, 7, 8, 12-16, 18, 20-22, 32, 33, 41 and 42 are currently pending and examined on the merits.
Information Disclosure Statement
The information disclosure statement submitted 05/30/2023 has been considered.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Priority
This application is a National Stage Application under 35 U.S.C. 371, of PCT AU2021/050095, filed 05 February 2021, and claims priority to AU 2020900337. The effective filing date for the claims is 07 February 2020.
Drawings
The drawings filed 8/04/2022, are accepted.
Nucleotide and/or Amino Acid Sequence Disclosures
Figure 9 contains a DNA sequence. There are two sequences with listed SEQ IDs on page 46 of the specification with no computer readable form (CRF).
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application contains a “Sequence Listing as a PDF file (37 CFR 1.821(c)(2)) or as physical sheets of paper (37 CFR 1.821(c)(3)), but fails to comply with the requirements of 37 CFR 1.821 - 1.825 because a copy of the "Sequence Listing" in computer readable form (CRF) has not been submitted as required by 37 CFR 1.821(e)(1)(i) or 1.821(e)(2)(i) as indicated in item 2) above.
Required response - Applicant must provide:
A new CRF of the “Sequence Listing” in accordance with 37 CFR 1.821(e)(1)(i) or 1.821(e)(2)(i) and
A statement that the content of the CRF is identical of the “Sequence Listing” part of the disclosure, submitted as a PDF file (37 CFR 1.821(c)(2)) or on physical sheets of paper (37 CFR 1.821(c)(3)), as required by 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii).
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the drawings are not identified by sequence identifiers in accordance with 37 CFR 1.821(d). Sequence identifiers for nucleotide and/or amino acid sequences must appear either in the drawings or in the Brief Description of the Drawings.
Required response – Applicant must provide:
Replacement and annotated drawings in accordance with 37 CFR 1.121(d) inserting the required sequence identifiers;
AND/OR
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers into the Brief Description of the Drawings, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
Claim Objections
Claim 20 objected to because of the following informalities: “measure” should be “measured”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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(s) 1, 2, 5, 21, 22, 32, and 33 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 1, which recites “…a PID prediction equation developed by fitting into a linear mixed model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID…”, it is unclear if there is an active step of developing the PID prediction equation by fitting into a linear mixed model a transcriptomic relationship matrix, and if there is an active step of generating the matrix from a reference set of transcriptome profiles of reference subjects with and without PID. Further, it is unclear if there is an active step of producing a prediction equation result.
Regarding claim 2, it is unclear if there is an active step of developing the PID prediction equation by fitting into a linear mixed model a transcriptomic relationship matrix. Further, it is unclear if there is an active step of generating a matrix from a reference set of transcriptome profiles of reference subjects with and without PID.
Regarding claim 5, the metes and bounds of “machine learning approaches” are unclear. The specification is silent regarding a definition of “machine learning approaches”. One skilled in the art would not recognize the metes and bounds of the limitation. The limitation will be interpreted as the application of any machine learning algorithm.
Regarding claims 21, 22, 31, and 32, it is unclear if there is an active step for generating “a transcriptomic relationship matrix from the reference set of transcriptome profiles…”
If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b). The disclosure of the structure (or material or acts) may be implicit or inherent in the specification if it would have been clear to those skilled in the art what structure (or material or acts) corresponds to the means- (or step-) plus-function claim limitation. See id. at 1380, 53 USPQ2d at 1229; In re Dossel, 115 F.3d 942, 946-47, 42 USPQ2d 1881, 1885 (Fed. Cir. 1997). However, "[a] bare statement that known techniques or methods can be used does not disclose structure" in the context of a means plus function limitation. Biomedino, LLC v. Waters Technology Corp., 490 F.3d 946, 952, 83 USPQ2d 1118, 1123 (Fed. Cir. 2007) (Disclosure that an invention "may be controlled by known differential pressure, valving and control equipment" was not a disclosure of any structure corresponding to the claimed "control means for operating [a] valving " and the claim was held indefinite). See also Budde v. Harley-Davidson, Inc., 250 F.3d 1369, 1376, 58 USPQ2d 1801, 1806 (Fed. Cir. 2001); Cardiac Pacemakers, Inc. v. St. Jude Med., Inc., 296 F.3d 1106, 1115-18, 63 USPQ2d 1725, 1731-34 (Fed. Cir. 2002) (Court interpreted the language of the "third monitoring means for monitoring the ECG signal…for activating …" to require the same means to perform both functions and the only entity referenced in the specification that could possibly perform both functions is the physician. The court held that excluding the physician, no structure accomplishes the claimed dual functions. Because no structure disclosed in the embodiments of the invention actually performs the claimed dual functions, the specification lacks corresponding structure as required by 35 U.S.C. 112, sixth paragraph, and fails to comply with 35 U.S.C. 112, second paragraph.) (See MPEP 2181).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-5, 7,8,14,15, 20-22, 32, and 33, 41, and 42 are rejected under 35 U.S.C. 101 because claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Step 2A, Prong 1
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). In the instant application, the claims recite the following limitations that equate to an abstract idea/law of nature/natural phenomenon:
Claim 1 recites:
“…using a linear mixed model to fit a transcriptome profile of the subject to a PID prediction equation developed by fitting into a linear mixed model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID…”, which is a mathematical concept, i.e. a calculation
Claim 2 recites:
“…fitting into a linear mixed model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID to develop the PID prediction equation.”, mathematical concept
Claim 3 recites: “…measuring the transcriptome profile of the subject…”, mental step
Claim 4 recites: “…measuring the transcriptome profiles of the reference subjects.”, mental step
Claim 5 recites: “…the linear mixed model is best linear unbiased prediction (BLUP), BayesR, or machine learning approaches”, which further limits claim 41.
Claim 7 recites:
“…measuring a RNA sequence mutation profile of the subject for whom the determination of PID or susceptibility to PID is to be made…”, mental step
“…measuring or determining the DNA sequence mutation profile of the subject for whom the determination of PID or susceptibility to PID is to be made.”, mental step
Claim 8 recites:
“…the linear mixed model is used to fit the transcriptome profile and a RNA sequence mutation profile of the subject to the PID prediction equation…”, mathematical concept
“…the linear mixed model is used to fit the transcriptome profile and a DNA sequence mutation profile of the subject to the PID prediction equation…”, mathematical concept
Claim 14 recites: “…measuring or determining the metagenome profile of the subject for whom the determination of PID or susceptibility to PID is to be made…”, mental step
Claim 15 recites: “…the linear mixed model is used to fit the transcriptome profile and a metagenome profile of the subject to the PID prediction equation…”, mathematical concept
Claim 20 recites: “…analyzing a biological sample previously obtained from the subject…”, mental step
Claim 21 and 22 recite:
“…processing genomic information…”, mental step
“…generating a transcriptomic relationship matrix…”, mathematical concept
“…fitting the transcriptomic relationship matrix into a linear mixed model to generate a PID prediction equation…”, mathematical concept
“…fitting the subject transcriptome profile to the PID prediction equation…”, mathematical concept
Claim 32 and 33 recite:
“…generate a transcriptomic relationship matrix from the reference set of transcriptome profiles…” , mathematical concept
“…fit the transcriptomic relationship matrix into a linear mixed model to generate a PID prediction equation…” , mathematical concept
“…fit the subject transcriptome profile to the PID prediction equation” , mathematical concept
Claim 41 recites:
“…if the subject is indicated to have or be susceptible to PID, the method further comprises administering to the subject a therapy specific to the PID”, which further limits claim 1.
Claim 42 recites:
“wherein the reference set of transcriptome profiles and/or transcriptome profile of the subject for whom the determination of PID or susceptibility to PID is to be made includes at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, or all 500 of the genes listed in Table 1, Table 2, or Tables 1 and 2,wherein Table 1 is…”, which further limits claim 2.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas.
There are no additional limitations that indicate that this model requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-5, 7,8,14,15, 20-22, 32, 33, 41, and 42 recite(s) an abstract idea & law of nature (Step 2A, Prong 1: YES).
Step 2A, Prong 2
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 an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
Claims 32 and 33 recite: “…a processor…”
Claim 20 recites: “…analyzing a biological sample previously obtained from the subject…”
Claims 21 , 22, 32, 33, recites “accessing a reference set of transcriptome profiles”
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems or laws of nature, natural phenomena, generally. As such, these limitations equate to either 1) 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) or instructions to obtain natural phenomena, which the courts have stated does not render an abstract idea eligible in Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 589-91, 106 USPQ2d 1972, 1978-79 (2013). As such, claims 1-4,7,8,14,15, 20-22, 32, 33, 41 and 42 is/are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2: NO).
Step 2B
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 equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claim 20 recites: “…analyzing a biological sample previously obtained from the subject…”
Regarding claims 1-4,7,8,14,15, 20-22, 32, and 33, the steps of obtaining sequencing data and performing sample collection do not integrate the abstract idea into a practical application and constitutes an insignificant extra-solution activity (i.e., data gathering and presentation), which does not impose a meaningful limit on the abstract idea. As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity, as evidenced by Sloan et al., in reference to Encyclopedia of DNA Elements (ENCODE) data at the ENCODE portal) (Nucleic Acids Res. 2016 Jan 4;44(D1):D726-32.) and the GTEx Consortium, in reference to The Genotype-Tissue Expression (GTEx) project (Nat Genet 45, 580–585 (2013)).
The GTEx Project reflects a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues. The GTEx Project therefore necessarily contains methods of obtaining biological sample, extracting DNA and RNA, and sequencing said transcripts to develop an DNA and RNA profile for various tissues. The GTEx project outlines resources and methods which may also be used to develop a reference set of transcriptome profiles for said tissues.
Similarly, the ENCODE portal reflects a database of expansion of assays that measure diverse RNA populations, identify proteins that interact with RNA and DNA, probe regions of DNA hypersensitivity, and measure levels of DNA methylation in a wide range of human cell and tissue types to identify putative regulatory elements. Sloan et al. discloses that the ENCODE portal is “…the central source for ENCODE raw data, analysis data, methods, standards and experimental metadata.” (p. D727) Therefore, the ENCODE portal contains methods of obtaining a biological sample and analyzing its transcriptional activity to develop a profile for said sample.
The additional elements do not 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 1-4,7,8,14,15, 20-22, 32, 33, 41 and 42 are not patent eligible.
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, 2, 21, 22, 32, 33, and 42 are rejected under 35 U.S.C. 103 as being unpatentable over Keerthikumar et al. (DNA Res. 2009 Dec;16(6):345-51), in view of Lentaigne et al. (Blood (2019) 134 (23): 2070–2081.), in view of Orange et al. (J Allergy Clin Immunol. 2011 Apr 17;127(6):1360–7.e6).
Regarding claims 1 and 2, Keerthikumar et al. discloses a linear classifier and applies said classifier to PID-related genes. Page 347 states “The trained [support vector machine] was used to classify PID or non-PID genes from an unlabeled test data set which consists of all human genes (Fig. 2). Examining PID-related and unrelated genes reads on using a linear classifier to fit a transcriptome profile of a subject to a PID prediction equation (pg. 349, sec. 3.3, p.346, col.1, para. 5; Fig. 1, p.347, col.1, par.2, re: clm. 1, …using a linear…model to fit a transcriptome profile of the subject to a PID prediction equation developed by fitting into a linear…model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID, wherein the prediction equation's result indicates whether the subject has or is susceptible to PID, clm. 2, … fitting into a linear … model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID to develop the PID prediction equation.).
Keerthikumar et al. does not disclose the use of linear mixed model explicitly (re: clm. 1, using a linear mixed model…, clm. 2, …a linear mixed model…)
Lentaigne et al. teaches a linear mixed regression model as applied to a study of platelet size in patients with transcription factor IKZF5 and those without. Lentaigne et al. states methods on page 2078:
“IKZF5 is expressed across hematopoietic lineages but IKZF5 cases have normal blood counts, except for thrombocytopenia. To understand this, we performed RNA-sequencing on the CD41 T cells, monocytes, neutrophils, and platelets in 3 unrelated cases from pedigrees A, B, and E and 14 unrelated controls.” The RNA sequencing element of the methods reads on a transcriptomic relationship matrix generated from a reference set of profiles from patients (pg. 2076, Fig. 2, re: clm. 1, using a linear mixed model… fitting into a linear mixed model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects; clm. 2, … fitting into a linear mixed model a transcriptomic relationship matrix generated from a reference set of transcriptome profiles of reference subjects with and without PID to develop the PID prediction equation).
Keerthikumar et al. and Lentaigne et al. do not explicitly disclose predicting whether a subject has or is susceptible to PID (re: clm 1. A method for developing a primary immunodeficiency (PID) prediction equation for determining whether a subject has or is susceptible to developing a PID; clm. 2, … A method for developing a primary immunodeficiency (PID) prediction equation for determining whether a subject has or is susceptible to developing a PID…)
Orange et al. teaches a study of three hundred sixty-three patients with CVID from 4 study sites genotyped with 610,000 single nucleotide polymorphisms (SNPs). Patients were divided into a discovery cohort of 179 cases in comparison with 1,917 control subjects and a replication cohort of 109 cases and 1,114 control subjects (Abstract, Methods). Orange et al. discloses methods on page 5:
“We used a Support Vector Machine (SVM) algorithm to determine how well we could
predict the CVID phenotype in a pool of “unknown” samples…The SVM algorithm was trained on the discovery cohort of 179 CVID cases and 1917 control subjects.” This reads on a study identifying if a subject has PID, and a PID prediction equation (as a SVM algorithm relies on equations) (re: clm. 1, A method for determining whether a subject has or is susceptible to developing a primary immunodeficiency (PID), … PID prediction equation…, clm. 2, … for determining whether a subject has or is susceptible to developing a PID)
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to the methods of Keerthikumar et al., Orange et al. and Lentangne et al., substitution of a linear mixed model as taught by Lentangne et al. for a linear model as taught by Keerthikumar et al. is applying a known technique to a known method with no more than a predictable outcome of a linear mixed model applied to predict PID-related genes. One of skill in the art of genomics would have had a reasonable expectation of success at applying the linear mixed model technique to the method of applying a linear classifier to PID genes as Keethikumar et al. discloses a linear classifier, Lentangne et al. provides all the necessary instructions or elements (Fig. 2, Fig 4C of Lentangne et al.) for the discloses linear mixed model, and Orange et al. discloses a relationship between single nucleotide polymorphisms and expressed genes (relating transcriptomic measures to genomic in their discussion). Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Regarding claims 21, 22, Keerthikumar et al. discloses a linear classifier and computationally applies said classifier to PID-related genes, and additionally accesses transcriptome profiles of reference subjects, and additionally fits a transcriptome profile to a PID prediction equation (re: clm. 21, 22, …accessing a reference set of transcriptome profiles of reference subjects, each reference subject either having or not having a primary immunodeficiency (PID); generating a transcriptomic relationship matrix from the reference set of transcriptome profiles; fitting the transcriptomic relationship matrix into a linear mixed model to generate a PID prediction equation; and fitting the subject transcriptome profile to the PID prediction equation.)
The combination of Keerthikumar et al. with Lentaigne et al. teaches the linear mixed model element (re: clm. 21, 22 …linear mixed model…)
Regarding claims 32 and 33, the combination of Lentaigne et al. and Keerthikumar et al. discloses a linear classifier and linear mixed model, and computationally applies said model to PID-related genes, (which inherently includes storage and a processor) and additionally accesses transcriptome profiles of reference subjects, and additionally fits a transcriptome profile to a PID prediction equation (re: clm. 32, 33, access a reference set of transcriptome profiles of reference subjects, each reference subject either having or not having a primary immunodeficiency (PID); generate a transcriptomic relationship matrix from the reference set of transcriptome profiles; fit the transcriptomic relationship matrix into a linear mixed model to generate a PID prediction equation; receive a subject transcriptome profile; and fit the subject transcriptome profile to the PID prediction equation…)
Regarding claim 42, Keerthikumar et al. further discloses 1446 genes predicted via SVM to be associated with PID, which inherently originate from the reference set of transcriptomic profiles (p. 348, Supplementary Table S2, re: clm. 42, wherein the reference set of transcriptome profiles and/or transcriptome profile of the subject for whom the determination of PID or susceptibility to PID is to be made includes at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, or all 500 of the genes listed in Table 1, Table 2, or Tables 1 and 2…[65 genes from Keerthikumar et al. et al. match the genes listed in Table 1, Table 2, or Tables 1 and 2, those genes being:
ABCG2
ALAS2
ALOX15
ALPL
ANXA2
CAPG
CD200
CD22
CD33
CD3EAP
CDC34
CEBPA
CITED2
CPA3
CTSE
CTSG
DPM2
EIF2AK2
EOMES
EPB42
EPOR
GATA2
HBA2
HCST
HEPH
HLA-DRB5
HLA-G
HP
ICOSLG
IGFBP2
IGKC
IL15
IL15RA
IL5RA
ITGA2B
JAK2
JUND
KIR3DL1
KIR3DL2
LDLR
LGALS3
LTF
MARCO
OLFM4
PAX5
PRDX2
PSEN2
PSTPIP2
SERPINB9
SHARPIN
SLC6A8
SNX3
SPSB2
SPTB
STAT2
STOM
SWAP70
TFR2
THRA
TICAM2
TNFSF13
TSTA3
UBB
XIST
YARS
) (re: clm. 42, the reference set of transcriptome profiles and/or transcriptome profile of the subject for whom the determination of PID or susceptibility to PID is to be made includes at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, or all 500 of the genes listed in Table 1, Table 2, or Tables 1 and 2,wherein Table 1 is…).
Claim(s) 3, 4, 5, 7, 8, 12, 13, 14, 15, 16, 20 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Keerthikumar et al. in view of Lentaigne et al. in view of Orange et al. as applied to claims 1, 2, 5, 21, 22, 32, and 33 above, and in view of Bonilla et al (J Allergy Clin Immunol. 2015 Nov;136(5):1186-205.e1-78).
Keerthikumar et al. in view of Lentaigne et al. in view of Orange et al. are applied to claims 1, 2, 5, 21, 22, 32, and 33.
Regarding claim 41, Bonilla et al. discloses therapies administered for specific PIDs (p. 1205.e15, Table E6, re: clm. 41, …administering to the subject a therapy specific to the PID).
Regarding claim 3, Keerthikumar et al. discloses measuring the transcriptome profile of a subject from reference data via application of a (SVM) and LOO error measurement (p. 347, para. 2.5, re: clm. 3, ...measuring the transcriptome profile of the subject…).
Regarding claim 4, Keerthikumar et al. teaches this element via use of data from the RefDIC database (pg. 347), which is equivalent to a measurement of a transcriptomic matrix from reference subjects, (re: clm. 4, measuring the transcriptome profiles of the reference subjects…).
Regarding claim 5, The limitation “machine learning approaches” will be interpreted as the application of any machine learning algorithm (re: clm. 5, … or machine learning approaches.)
Keerthikumar et al. teaches this element via use of a support vector machine (re: clm 5., the linear mixed model is… machine learning approaches.)
Regarding claim 7, as previously stated, Keerthikumar et al. et al. further discloses on pg. 346, Methods, 2.1, measuring RNA sequences related to PID via use of ‘Resource of Asian PDIs (RAPIDs) , which hosts information on sequences and expression at the mRNA and protein levels of genes reported to be involved in PID patients, including mutation data and annotations, and therefore details transcriptomic profiles of samples from said patients which may be used to curate a transcriptomic matrix containing a plurality of such profiles (p. 346, para. 2.1, re: clm. 7, measuring a RNA sequence mutation profile of the subject for whom the determination of PID or susceptibility to PID is to be made…)
Regarding claim 8, As previously stated, Keerthikumar et al. discloses a PID prediction equation (Title, methods), and on pg. 346, Methods, 2.1 discloses measuring RNA sequences related to PID via use of ‘Resource of Asian PDIs (RAPIDs) , which hosts information on sequences and expression at the mRNA and protein levels of genes reported to be involved in PID patients, including mutation data and annotations, and therefore details transcriptomic profiles of samples from said patients which may be used to curate a transcriptomic matrix containing a plurality of such profiles Keerthikumar et al. therefore teaches on measuring an RNA sequence mutation profile and analyzing a biological sample (re: Clm. 8, reference set further comprises a RNA sequence mutation profile… PID prediction equation…)
Regarding claim 8, as previously stated in regard to independent claim 1 from which claim 8 depends on, the combination of a linear mixed model as taught by Lentangne et al. for a linear model as taught by Keerthikumar et al. would be obvious (re: clm. 8, …linear mixed model…).
Regarding claim 8, combining the methods of Keerthikumar et al., Orange et al. (which teaches measuring a subject) and Lentangne et al. with the teachings of Bonilla et al., would make dependent claim 41 (from which claim 8 depends on) obvious (re: clm. 8, … a RNA sequence mutation profile of the subject to the PID prediction equation).
Regarding claim 12, Keerthikumar et al. discloses on pg. 346, Methods, 2.1, measuring RNA sequences related to PID via use of ‘Resource of Asian PDIs (RAPIDs), which hosts information on sequences and expression at the mRNA and protein levels of genes reported to be involved in PID patients, including mutation data and annotations, and therefore details transcriptomic profiles of samples from said patients which may be used to curate a transcriptomic matrix containing a plurality of such profiles. Keerthikumar et al. therefore teaches on the element measuring an RNA sequence mutation profile and analyzing a biological sample (p. 346, para. 2.1, re: clm. 12, … a RNA sequence of a PID gene comprising a known mutation resulting in a PID).
Regarding claim 13, the applicant’s specification discloses that a metagenome profile is the vector of counts of sequenced reads that align to a collection of 16S rRNA sequences or other available or generated reference sequence sets in a database (Spec, p. 47). Keerthikumar et al. teaches the element of claim 13 via training a SVM to classify PID on p. 347:
“To train the SVM, two types of data sets were generated—the positive data set consists of all the known PID genes, whereas the negative data set contained genes where no immune/hematopoietic system abnormalities were described due to mouse knockouts, knockins or spontaneous mutations reported for the mouse orthologs in the MGI database. On the basis of these criteria, 148 PID genes were in the positive data set and 3162 genes were in the negative data set. Test data set contains 36, 677 genes encoded by the human genome [which are classified via 69 features (disclosed in supplementary table S1) associated with PID]”.
In regard to claim 14, the applicant’s specification discloses that a metagenome profile is the vector of counts of sequenced reads that align to a collection of 16S rRNA sequences or other available or generated reference sequence sets in a database (Spec, p. 47). Keerthikumar et al. teaches training a SVM to classify PID on p. 347:
“To train the SVM, two types of data sets were generated—the positive data set consists of all the known PID genes, whereas the negative data set contained genes where no immune/hematopoietic system abnormalities were described due to mouse knockouts, knockins or spontaneous mutations reported for the mouse orthologs in the MGI database. On the basis of these criteria, 148 PID genes were in the positive data set and 3162 genes were in the negative data set. Test data set contains 36 677 genes encoded by the human genome [which are classified via 69 features (disclosed in supplementary table S1) associated with PID]”. Keerthikumar et al. further discloses on pg. 346, Methods, 2.1, measuring RNA sequences related to PID via use of ‘Resource of Asian PDIs (RAPIDs) , which hosts information on sequences and expression at the mRNA and protein levels of genes reported to be involved in PID patients (re: clm. 14, …the metagenome profile of the subject for whom the determination of PID or susceptibility to PID is to be made). Therefore, Keerthikumar et al. teaches a reference set comprising a metagenome profile (gene sequences as an available reference set), and further teaches measuring or determining the metagenome profile of the subject (re: clm. 14, … measuring or determining the metagenome profile of the subject for whom the determination of PID or susceptibility to PID is to be made).
Regarding claim 15, and as previously described as applied to claim 41, the combination of Lentaigne et al., who teaches a linear mixed regression model as applied to a study of platelet size in patients with transcription factor IKZF5 and those without (pg. 2076, Fig. 2) with Keerthikumar et al., who teaches a reference set comprising a metagenome profile (p. 347) related to PID (re: …PID prediction equation…) provides the element of claim 15 (re: …wherein the reference set further comprises a metagenome profile and the linear mixed model is used to fit the transcriptome profile and a metagenome profile of the subject to the PID prediction equation…).
Regarding claim 20, Orange et al. teaches on identifying if a subject has CVID using DNA samples (re: clm. 20, …the profile of the subject for whom the determination of PID or susceptibility to PID is to be made is determined or measure from analysing a biological sample previously obtained from the subject.)
As previously stated, the methods of Keerthikumar et al., Orange et al. and Lentangne et al. make the independent claim 1 (from which claim 41 depends on) obvious as Keethikumar et al. discloses a linear classifier, Lentangne et al. teaches a linear mixed model, and Orange et al. discloses a relationship between single nucleotide polymorphisms and expressed genes (relating transcriptomic measures to genomic in their discussion). Applying the KSR standard to the therapy administration as taught by Bonilla et al. represents Some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, with no more than a predictable outcome of a method of predicting candidate PID genes using transcriptome profiles derived from patients with PID signatures and SNPs from CVID patient data, with therapy administrations to patients with PID transcriptomic signatures.
One would have been motivated to combine the methods of Keerthikumar et al., Orange et al. and Lentangne et al. with the therapeutics of Bonilla et al. as the resultant PID analysis pipeline and algorithm would be improved by the clinically relevant treatment pipeline for subjects with PID transcriptomic signatures as taught by Bonilla et al.
One of ordinary skill in the art of sequence analysis before the effective filing date of the claimed invention would have had a reasonable expectation of success of combining the methods of Keerthikumar et al., Orange et al. and Lentangne et al. with the teachings of Bonilla et al. because Keerthikumar et al. discloses PID-genes which are relatable to patient-subjects ready for treatment using the therapeutic considerations as Bonilla et al. discloses. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Claim 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Keerthikumar et al. in view of Lentaigne et al. in view of Orange et al. in view of Bonilla et al. as applied to 1-5, 7, 8, 12, 13, 14, 15, 16, 20-22, 32, 33, 41, and 42 above, and in view of Park et al. (PLoS One. 2013 Sep 17;8(9):e74893).
Keerthikumar et al., Lentaigne et al., Orange et al., and Bonilla et al. are applied to claims 1-5, 7, 8, 12, 13, 14, 15, 16, 20-22, 32, 33, 41, and 42.
Regarding claims 16 and 18, Park et al. discloses a whole blood transcriptome analysis of patients with CVID, a PID (Abstract). Park et al. discloses on pg. 2 in their material and methods:
“Blood was taken before the interval intravenous immune globulin (Ig) infusions, or between subcutaneous Ig administrations. The training set included 59 patients (29 females, 30 males) age 11 through 88 (mean age, 44.7 years) and 21 healthy adult volunteers. The test set included 32 CVID patients, 16 males and females (age 11 to 69, mean age, 42 years), and 15 control samples. For clinical and immunologic data, baseline laboratory results before initiating Ig replacement were used; other test results were those done at the time of diagnosis or the first clinic visit (Table 1).”
As peripheral blood mononuclear cells are an inherent component of blood samples, Park therefore teaches on a transcriptome profile of subjects with CVID derived from peripheral blood mononuclear cells (re: clm. 16, …the transcriptome profile or sequence mutation profile is obtained from blood comprising peripheral blood mononuclear cells…).
Regarding claim 18, Park et al. states:
“For clinical and immunologic data, baseline laboratory results before initiating Ig replacement were used; other test results were those done at the time of diagnosis or the first clinic visit (Table 1)” (pg. 2, Table 1). It would be reasonable and well within the capability for Park et al. to obtain a mouth, nose, or throat swab, or a saliva, faeces or skin sample during the “baseline laboratory results” used prior to Ig replacement as Park et al. states on pg. 2, and Table 1 (re: clm. 18, …wherein the metagenome profile is obtained from a mouth swab, nose swab, throat swab, saliva, faeces, or skin).
As previously stated, the methods of Keerthikumar et al., Orange et al. and Lentangne et al. make the independent claim 1 (from which claim 41, 16, and 18 (through claim 13) depends on) obvious as Keethikumar et al. discloses a linear classifier, Lentangne et al. teaches a linear mixed model, Orange et al. discloses a relationship between single nucleotide polymorphisms and expressed genes (relating transcriptomic measures to genomic in their discussion), and Bonilla et al. teaches a therapy.
Applying the KSR standard to the whole blood transcriptome analysis of patients with CVID as taught by Park et al. represents Some Teaching, suggestion or motivation in the prior art that would have lead one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention, with no more than a predictable outcome of a method of predicting candidate PID genes using transcriptome profiles and metagenome profiles derived from blood comprising peripheral blood mononuclear cells and a mouth, nose, throat swab, saliva, faeces or skin sample obtained from baseline laboratory results from patients with a PID.
One would have been motivated to combine the methods of Keerthikumar et al., Orange et al., Lentangne et al., Bonilla et al., and Park et al. as the resultant PID analysis pipeline and algorithm would be made more efficient by the clinically relevant sample procurement technique as taught by Park et al.
One of ordinary skill in the art of sequence analysis before the effective filing date of the claimed invention would have had a reasonable expectation of success of combining the methods of Keerthikumar et al., Orange et al., Lentangne et al., Bonilla et al. with the teachings of Park et al. because Bonilla et al. discloses clinical treatments for PID subjects which would reasonably allow for samples to be obtained during the clinically-related procedure of treatment as Park et al. discloses. Therefore, the invention would have been prima facie obvious to one of skill in the art at the time of filing of the application, absent evidence to the contrary.
Conclusion
No claims are allowed.
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/J.T.S./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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