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 .
Priority
Acknowledgment is made of applicant’s prior application 63/336,870 with filing date April 29th 2022 and PCT/US23/20161 with filing date April 27th 2023.
The effective filing date is April 29th, 2022.
Election/Restrictions
Claims 13-17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on August 25th, 2026.
Information Disclosure Statement
The information disclosure statements filed 04/28/2023, 01/17/2025, 04/20/2026 and 08/25/2026 have been considered.
Status of Claims
Claims 1-27 are currently pending.
Claims 13-17 are withdrawn.
Claims 1-12 and 18-27 are examined on the merits.
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 1-12 and 18-27 are rejected under 35 U.S.C. 101 because the 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 (claims 1-12 are drawn to a method; claims 18-27 are drawn to a system) (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 (reasonings in [brackets]):
Claims 1 and 18 state:
Generating [a…unit that generates] one or more probability rejection scores of one or more rejection labels based on the plurality of sets of weights and the expression levels; and…[which is a mathematical concept of a mathematical calculation]
assigning [a…unit that assigns] a predictive rejection classification of the biological sample of the transplant recipient based on the one or more probability rejection scores, wherein the predictive rejection classification classifies the status of the transplant…[which is a mental step, i.e. can be performed with pen and paper]
Claims 3 and 20 state:
… classifies the status of the transplant as experiencing antibody-mediated rejection (ABMR), T-cell mediated rejection (TCMR), mixed ABMR+TCMR, or no rejection.…[mental step]
Claim 4 and 21 state:
…generating[generate] a probability rejection score based on the plurality of sets of weights and the expression levels…[mathematical calculation].
Claims 6 and 23 state:
…analyze the gene expression levels of the discovery dataset for associations with the rejection classifications in the discovery dataset…[mental step]
…identify a subset of genes from the plurality of genes of the discovery dataset…[mental step]
…generate the plurality of sets of weights for the subset of genes based on the associations between the gene expression levels of the discovery dataset and the rejection classifications of the discovery dataset, wherein each set of weights is associated with one gene of the subset of genes…[mathematical calculation].
Claims 8 and 25 state:
…determining one or more computer-determined predictive rejection classifications from the validation dataset;…[mental step]
…comparing one or more of the rejection classifications in the validation dataset and the one or more computer-determined predictive rejection classifications; and…[mental step]
…determining a diagnosis accuracy based on the comparison, wherein the diagnosis accuracy is greater than a predetermined value.…[mental step]
The claims recite an abstract idea of measuring gene expression in a biological sample (See MPEP 2106.07(a)).
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 these claims require 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) 1-12 and 18-27 recite(s) an abstract idea/law of nature/natural phenomenon (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 additional elements 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:
Claim 1 states:
receiving expression levels of a plurality of genes from a biological sample of a transplant recipient
receiving a plurality of sets of weights for the plurality of genes
Claim 6 states:
receive a discovery dataset from biological samples of a discovery cohort of transplant recipients, wherein the discovery dataset comprises gene expression levels of a plurality of genes and rejection classifications
Claim 18 states:
receives expression levels of a plurality of genes from a biological sample of a transplant recipient…
receives a plurality of sets of weights for the plurality of genes;…
Claim 23 states:
receive a discovery dataset from biological samples of a discovery cohort of transplant recipients, wherein the discovery dataset comprises gene expression levels of a plurality of genes and rejection classifications…
Claim 25 states:
acquiring a validation dataset from biological samples of a validation cohort of transplant recipients, wherein the validation dataset comprises gene expression levels for a plurality of genes and rejection classifications;
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. 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. As such, claims 1-12 and 18-27 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 1 states:
receiving expression levels of a plurality of genes from a biological sample of a transplant recipient
receiving a plurality of sets of weights for the plurality of genes
Claim 6 states:
receive a discovery dataset from biological samples of a discovery cohort of transplant recipients, wherein the discovery dataset comprises gene expression levels of a plurality of genes and rejection classifications
Claim 18 states:
receives expression levels of a plurality of genes from a biological sample of a transplant recipient…
receives a plurality of sets of weights for the plurality of genes;…
Claim 23 states:
receive a discovery dataset from biological samples of a discovery cohort of transplant recipients, wherein the discovery dataset comprises gene expression levels of a plurality of genes and rejection classifications…
Claim 25 states:
acquiring a validation dataset from biological samples of a validation cohort of transplant recipients, wherein the validation dataset comprises gene expression levels for a plurality of genes and rejection classifications;
Regarding claims 1-12 and 18-27, The steps of genomic expression data and/or 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 J. Reeve et al. (American Journal of Transplantation 2013; 13: 645–655) who teaches a study of microarray results from 403 kidney transplant biopsies to derive a classifier assigning T cell-mediated rejection scores to all biopsies, and with comparative analysis with histologic assessments.
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 1-12 and 18-27 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.
Claim(s) 1, 3-12, 18, and 20-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by J. Sellarés et al. (Am J Transplant. 2013 Apr;13(4):971-983.)
Regarding claims 1 and 18, Sellarés et al. teaches the collection of microarray data from kidney transplant biopsies and the subsequent computational classification analysis applied to said biopsy data, stating on pg. 971:
“To develop a molecular test, we prospectively assigned diagnoses, including C4d-negative antibody-mediated rejection, to 403 indication biopsies from 315 patients, based on histology (microcirculation lesions) and donor-specific HLA antibody. We then used microarray data to develop classifiers that assigned antibody-mediated rejection scores to each biopsy.”
Sellarés et al. discloses their acquisition of biopsy samples on pg. 972, Methods; the use of microarray data reads on receiving gene expression levels (re: clm. 1, … A method for classifying a status of a transplant, the method comprising: receiving expression levels of a plurality of genes from a biological sample of a transplant recipient; receiving a plurality of sets of weights for the plurality of genes…, clm. 18, … A system for classifying a status of a transplant, the system comprising: a scoring unit that: receives expression levels of a plurality of genes from a biological sample of a transplant recipient; receives a plurality of sets of weights for the plurality of genes…)
Regarding claims 1 and 18, Sellarés et al. further teaches receiving weights, stating on pg. 973 that “Table 3 shows the 30 probe sets most frequently selected in the 1000 class comparisons, and the cell types that express each transcript, based on previous annotations or the literature. A single, final, [linear discriminant analysis](LDA) model using all 403 biopsies was built. The 20 probe sets selected by this classifier (using t-test p-values based on all 403 samples) were the same as the 20 most frequently selected probe sets in the cross-validation loops. The linear discriminant scores in the final model for the 20 probe sets are shown in Table 3.” LDA application and the coefficients applied as well as the use of probe sets used with a classifier read on weights (re: clm. 1, 18, … a plurality of sets of weights for the plurality of genes…)
Sellarés et al. further teaches generating probability rejection scores, stating in the classifier section on pg. 973 that “The classifier output is a score between 0.0 and 1.0, reflecting the probability that a biopsy is ABMR.” (re: clm. 1, 18 … one or more probability rejection scores of one or more rejection labels based on the plurality of sets of weights and the expression levels…). In the same section, Sellarés et al. further teaches assigning predictive rejection based on score, stating “We assigned biopsies above a score of 0.2 as molecular ABMR, and those below as molecular non-ABMR. This cutoff was chosen because it resulted in ∼90% specificity for diagnosing histology-DSA ABMR.” (re: clm. 1, 18 … a predictive rejection classification of the biological sample of the transplant recipient based on the one or more probability rejection scores, wherein the predictive rejection classification classifies the status of the transplant
As Sellarés et al. teaches a method and system to classify kidney transplant biopsies by their rejection score, Sellarés et al. teaches the limitations of claims 1 and 18.
Regarding claim 3 and 20, Sellarés et al. teaches “. We assigned biopsies above a score of 0.2 as molecular ABMR, and those below as molecular non ABMR. This cutoff was chosen because it resulted in ∼90% specificity for diagnosing histology-DSA ABMR…” on pg. 973 (re: clm. 3, 20, … the predictive rejection classification classifies the status of the transplant as experiencing antibody-mediated rejection (ABMR), T- cell mediated rejection (TCMR), mixed ABMR+TCMR, or no rejection). Sellarés et al. anticipates claims 3 and 20.
Regarding claims 4 and 21, Sellarés et al. teaches a score-based label to separate biopsies by their presentation of molecular ABMR on pg. 973 (re: clm. 4, 21 … generating a probability rejection score based on the plurality of sets of weights and the expression levels. ) and further teaches banding of AMBR scoring on pg. 979, Table 4. Table 4 discloses “The relationship between the ABMR score and histology DSA diagnoses of ABMR in late biopsies… Most late biopsies with ABMR scores <0.2 had been assessed as non-ABMR by histology-DSA (120/143 = 84%). Five of the nine ambiguous “possible ABMR” biopsies had scores <0.2 and one was between 0.2 and 0.5.” (re: clm. 4 , 21 … for each rejection label of a plurality of rejection labels, generating a probability rejection score based on the plurality of sets of weights and the expression levels.)
Sellarés et al. anticipates claims 4 and 21.
Regarding claims 5 and 22, Sellarés et al. teaches “The top 20 probe sets were used, based on a Welch’s t-test comparing C4d positive and C4d negative ABMR to all other cases (including mixed rejection, TCMR and possible ABMR). The classifier output is a score between 0.0 and 1.0, reflecting the probability that a biopsy is ABMR…” which reads as a weight for a ABMR label, as further detailed in Table 3 (re: clm. 5, 22, … herein each set of weights comprises a weight for a corresponding rejection label.
Regarding claims 6 and 23, Sellarés et al. teaches in the classifier section that “The top 20 probe sets were used, based on a Welch’s t-test comparing C4d positive and C4d negative ABMR to all other cases (including mixed rejection, TCMR and possible ABMR). The classifier output is a score between 0.0 and 1.0, reflecting the probability that a biopsy is ABMR. “ , which reads on a machine-learning model trained to receive a dataset of biological samples and analyze gene expression levels from genes and rejection classifications, and further teaches on pg. 973 (Classifier cross-validation) that “All aspects of classifier training, including gene selection, were done from scratch within each training set.”, reading on a discovery dataset ((as further detailed in diagnoses assigned in Table 2) and the identification of a subset of genes (re: clm. 6, 23, … The method of claim 1, wherein the plurality of sets of weights for the plurality of genes is from a machine-learning model trained to: receive a discovery dataset from biological samples of a discovery cohort of transplant recipients, wherein the discovery dataset comprises gene expression levels of a plurality of genes and rejection classifications; analyze the gene expression levels of the discovery dataset for associations with the rejection classifications in the discovery dataset;identify a subset of genes from the plurality of genes of the discovery dataset; and…).
Sellarés et al. further teaches weights per gene as disclosed in Table 3 (re: clm. 6, 23, …generate the plurality of sets of weights for the subset of genes based on the associations between the gene expression levels of the discovery dataset and the rejection classifications of the discovery dataset, wherein each set of weights is associated with one gene of the subset of genes.)
Sellarés et al. anticipates claims 6 and 23.
Regarding claims 7 and 24, Sellarés et al. teaches on Table 2 the Diagnoses assigned by the reference standard system based on histology, C4d staining and DSA, stating on pg. 973 “The diagnoses assigned (Table 2) included 65 ABMR (17 C4d-positive), 10 possible ABMR (all C4d-negative) and 22 mixed ABMR and TCMR (nine C4d positive).” (re: clm. 7, 24, … wherein at least some of the rejection classifications of the discovery dataset comprise antibody-mediated rejection (ABMR), T-cell mediated rejection (TCMR), mixed ABMR+TCMR rejection, or no rejection.)
Sellarés et al. anticipates claims 7 and 24.
Regarding claims 8 and 25, Sellarés et al. teaches in the classifier cross-validation section on pg. 973 repeated k-fold cross-validation wherein” The 403 biopsies (plus 8 nephrectomies) were split randomly into 10 ap proximately equal sized groups (folds)…. This was then used to predict the probability of ABMR for the biopsies in the left out fold” which reads on acquiring a validation dataset and using said dataset to derive rejection classification (re: clm. 8, 25, … acquiring a validation dataset from biological samples of a validation cohort of transplant recipients, wherein the validation dataset comprises gene expression levels for a plurality of genes and rejection classifications… determining one or more computer-determined predictive rejection classifications from the validation dataset… comparing one or more of the rejection classifications in the validation dataset and the one or more computer-determined predictive rejection classifications…)
Sellarés et al. further teaches on pg. 974 (“Relationship between the classifier scores and the histology-DSA diagnosis of ABMR”) that: “To calculate accuracy statistics comparing ABMR scores with the Reference Standard histology-DSA diagnoses, scores were considered positive for ABMR above a cut-off of 0.2. The positive scores were compared with histology DSA diagnosis of ABMR (C4d+,C4d−, and mixed). The statistics are as follows: accuracy 85%...” The accuracy percentage necessarily demands a predetermined value with which to compare against (in this case, the histology-DSA diagnosis of ABMR) (re: clm. 8, 25, … determining a diagnosis accuracy based on the comparison, wherein the diagnosis accuracy is greater than a predetermined value. )
Sellarés et al. anticipates claims 8 and 25.
Regarding claims 9 and 26, Sellarés et al. teaches on Table 3 CXCL11 (re: clm. 9, 26, … wherein at least one gene of the plurality of genes comprises a gene identified from a group consisting of…CXCL11…)
Sellarés et al. anticipates claims 9 and 26.
Regarding claims 10 and 27, Sellarés et al. teaches “We studied 403 prospectively collected indication biopsies obtained 6 days to 35 years posttransplant from 315 consenting kidney transplant recipients. “ in the Results section (re: clm. 10, 27, … the transplant recipient received a transplant comprising one or more of: a kidney transplant, a heart transplant, a lung transplant, a pancreas transplant, a liver transplant, an intestinal transplant, or a vascularized composite allograft transplant.)
Sellarés et al. anticipates claims 10 and 27.
Regarding claims 11 and 12, Sellarés et al. states in the introduction “Antibody-mediated rejection (ABMR) has emerged as a key problem in kidney transplantation and a major cause of late graft loss (1, 2, 3). The potential of antibodies against MHC to reject allografts was demonstrated by Peter Gorer, who described MHC antibodies and showed that they were cytotoxic and could reject mouse tumor allografts (4, 5, 60”, and further teaches “We studied 403 prospectively collected indication biopsies obtained 6 days to 35 years posttransplant from 315 consenting kidney transplant recipients. “ in the Results section, which reads on a cohort of patients with kidney transplants (allograph; re: clm. 11, the transplant recipient received a transplant that is an allograft or a xenograft…clm. 12, …the biological sample is an organ tissue sample.)
Sellarés et al. anticipates claims 11 and 12.
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) 2 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sellarés et al. as applied to claims 1, 3-12, 18, and 20-27 above in view of J. Reeve et al. (American Journal of Transplantation 2013; 13: 645–655).
Sellarés et al. is applied to claims 1, 3-12, 18, and 20-27 above.
Regarding claims 2 and 19, Sellarés et al. lists genes associated with transplant status in Table 3 but does not explicitly disclose their association (re: clm. 2, 19, … at least one of the plurality of genes is associated with one or more of: immune cell activation, organ-specific defense against pathogens, regulation of tissue and cellular processes, or transcription regulation…)
Reeve et al. discloses Molecular Diagnosis of T Cell-Mediated Rejection in Human Kidney Transplant Biopsies in which Reeve et al. used microarray results from 403 kidney transplant biopsies to derive a classifier assigning T cell-mediated rejection scores to all biopsies, and in Table 4, pg. 650 discloses molecules selected in 1000 class comparisons in the classifier algorithm including CD28 antigen, an immune cell activation gene (re: clm. 2, 19, …, … at least one of the plurality of genes is associated with one or more of: immune cell activation, organ-specific defense against pathogens, regulation of tissue and cellular processes, or transcription regulation…)
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 Sellarés et al. and Reeve et al., the Examiner concludes that the combination of the kidney biopsy transplant analysis method as taught by Reeve et al. to the same of Sellarés et al. is applying a known technique to a known method with no more than a predictable outcome of a gene analyzed within said analysis being associated with immune cell activation. One of skill would have had a reasonable expectation of success at applying the method of Reeve et al. to the method of Sellarés et al. as Reeve et al. provides all the necessary instructions or elements. The combination would have been prima facie obvious to one of ordinary skill in the art at the time of filing, absent evidence to the contrary.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN T STUBBS whose telephone number is (571)272-0340. The examiner can normally be reached M-F 8-5 EST.
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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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