DETAILED ACTION
Applicant’s response filed 03/02/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 4, 11 and 18 are cancelled by Applicant.
Claims 1-3, 5-10, 12-17 and 19-21 are currently pending and are herein under examination.
Claims 1-3, 5-10, 12-17 and 19-21 are rejected.
Claims 1, 8 and 15 are objected.
Priority
The instant application claims domestic benefit as a 371 filing of International Application PCT/US2020/060461 filed November 13, 2020, which claims domestic benefit to U.S. Provisional Patent Application No. 62/934,663 filed November 13, 2019. The claims to domestic benefit are acknowledged. As such, the effective filing date for claims 1-3, 5-10, 12-17 and 19-21 is November 13, 2019.
Information Disclosure Statement
The IDS filed 03/13/2026 follows the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the list of references cited from this IDS is included with this Office Action.
Drawings
The objection to the drawings is withdrawn.
The drawings filed 05/10/2022 are accepted.
Specification
The objections to the specification regarding pg. 21, line 29, Supplemental Figure 1, and pg. 25, line 29 are withdrawn. However, the objection regarding pg. 22, line 1 is maintained below:
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. On specification pg. 22 reference [5] still has a hyperlink.
Response to Arguments under Specification
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive because the amended specification filed 03/02/2026 did not address the hyperlink in reference [5] in the specification on pg. 22 (pg. 10, para. 3-4 of Applicant’s remarks).
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 4-5, 8-14 and 18-19 under 35 USC 112(b) is withdrawn in view of claim amendment.
35 USC 102
The rejection of claims 1, 6-8, 13-15 and 20-21 under 35 U.S.C. 102(a)(1) as being anticipated by Anunciação et al. is withdrawn in view of claim amendment.
35 USC 103
The rejection of claims 2-3, 9-10 and 16-17 under 35 U.S.C. 103 as being unpatentable over Anunciação et al. in view of Alzahrani et al. is withdrawn in view of claim amendments.
The rejection of claims 4-5, 11, and 18 under 35 U.S.C. 103 as being unpatentable over Anunciação et al. in view of Weimer et al. is withdrawn in view of claim amendments.
The rejection of claims 12 and 19 under 35 U.S.C. 103 as being unpatentable over Anunciação et al. in view of Weimer et al. and in further view of Schinstock et al. is withdrawn in view of claim amendments.
Claim Objections
The objection to claims 1-21 are withdrawn in view of claim amendments.
Claims 1, 8 and 15 are objected to because of the following informalities:
Claim 1, line 4, should recite “applied to” to correct grammar.
Claim 8, line 6, should recite “applied to” to correct grammar.
Claim 8, line 4 should recite “generate” instead of “generating”.
Claim 15, line 5, should recite “applied to” to correct grammar.
Appropriate correction is required.
Claim interpretation
Claim 13 recites the phrase “The system of claim 8, further comprising an HLA data pre-processor for: deriving … removing … and providing …” The broadest reasonable interpretation of this phrase includes the steps of deriving, removing, and providing being an intended use of the HLA data pre-processor because of the word “for”. MPEP 2114.II recites “[A]pparatus claims cover what a device is, not what a device does.” However, in the interest of compact prosecution, claim 13 is being examined as if it required that the pre-processor be configured to perform deriving, removing, and providing.
Claim Rejections - 35 USC § 112
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.
Claims 3, 10, 17 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited as necessitated of claim amendment.
Claim 3 recites “the sum of the HLA DSA MFI values” which renders the claim indefinite. It is unclear which of the following it refers to: (i) claim 1, lines 2-5, of HLA DSA MFI values used to generate the model, (ii) claim 1, line 9, of HLA DSA MFI values for a set of patients, or (iii) claim 2 of HLA DSA MFI values of the prospective tissue recipient. Clarify which values are being referenced. Furthermore, claims 10 and 17 contain the same issue of indefiniteness as claim 3 regarding “the sum of the HLA DSA MFI values” and thus are rejected for the same reasons as claim 3. Clarify for claims 10 and 17 which values are being referenced in the claims to which they depend.
Claim 19 is indefinite because it depends on cancelled claim 18. Change the dependency of claim 19.
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 1-3, 5-10, 12-17 and 19-21 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.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, cl claims 1-3 and 4-7 recite a method, claims 8-10 and 12-14 recite a system, and claims 15-17 and 19--21 recite a product. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claims 1, 8 and 15 recite “generating a physical-crossmatch-outcome-data derived model by … to select weights to be applied human leukocyte antigen (HLA) donor specific antibody (DSA) mean fluorescence intensity (MFI) values, using the weights to produce a weighted sum of the HLA DSA MFI values, and minimizing an error function of predicted median channel shifts determined for the HLA DSA MFI values and true physical crossmatch median channel shifts determined from HLA DSA MFI values for a set of patients, wherein the error function counts a number of data points that are falsely identified as above or below a threshold; receiving, as inputs, HLA antibody MFI data of a prospective tissue recipient and HLA typing data of a tissue donor; generating, based on the inputs and a physical-crossmatch-outcome-data- derived model, a predicted virtual crossmatch outcome for the prospective tissue recipient; and using the predicted virtual crossmatch outcome to inform a transplant decision for the prospective tissue recipient.”
Claims 2, 9 and 16 recite “wherein the physical -outcome-data derived model comprises an optimal threshold model wherein HLA DSA MFI values of the prospective tissue recipient are summed and compared to a threshold determined empirically from physical crossmatch outcomes of a plurality of patients.”
Claims 3, 10 and 17 recite “using/use the predicted virtual crossmatch outcome to inform a transplant decision includes determining/recommending not to perform the transplant if the sum of the HLA DSA MFI values is greater than the threshold.”
Claims 5, 12 and 19 recite “using/use the predicted virtual crossmatch outcome to inform a transplant decision includes determining/recommending not to perform the transplant if a median channel shift calculated for a patient exceeds a median channel shift cutoff.”
Claims 6, 13 and 20 recite “deriving a list of recipient and donor eplet data from the recipient HLA MFI data, recipient HLA typing data, and the donor HLA typing data; removing, from the list, eplets that are common to the recipient and donor eplet data; and providing the eplets remaining in the list as the inputs to the physical-crossmatch-outcome-data-derived model.”
Claims 7, 14 and 21 recite “removing unverified eplets from the list prior to providing the eplets as the inputs to the physical- crossmatch-outcome-data-derived model.”
Claim 8 recites “… implement a physical-crossmatch-outcome-data-derived model by …”
Limitations reciting a mental process.
Claims 1-3, 5-10, 12-17 and 19-21 contain limitations recited at such a high level of generality that they equate to a mental process because they 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)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
The BRI of claims 1, 8 and 15 of selectin weight includes making a mental determination. A human can calculate a weighted sum. A human can perform the operations of a least-squares regression as disclosed in the specification on pg. 20 to minimize an error function. A human can also count how many datapoints are above or below a threshold as it requires comparing values to a threshold. Receiving data to generate a predicted virtual crossmatch using a model includes acquiring HLA MFI data and HLA typing data from a database and comparing the HLA MFI data to the HLA typing data. The comparison includes determining donor specific antibodies (DSA) by finding HLA molecules that were reactive in the HLA MFI data that are also present the donor’s HLA typing. This comparison can indicate a high risk of rejection when a DSA MFI value is above a threshold (i.e., positive). A human can then use the VXM predictions to determine whether or not to perform the transplant.
The BRI of claims 2-3, 9-10 and 16-17 includes summing HLA DSA MFI values and comparing them to a predetermined threshold, wherein a human decides not to perform the transplant when the sum of the HLA DSA MFI values is greater than the predetermined threshold.
The BRI of claims 5, 12 and 19 includes using a least-squares model as described on specification pg. 9, line 21 – pg. 10, line 10. A human is capable of performing the calculations of a least-squares on pen and paper and deciding not to perform the transplant when a patient’s median channel shift exceeds a median channel shift cutoff.
The BRI of claims 6-7, 13-14 and 20-21 includes analyzing data to generate a list, eliminating data from the list, and using data within the list as input into the crossmatch model. A human is capable of collecting, organizing, and manipulating data as well as inputting data into a model.
Limitations reciting a mathematical concept.
Above cited claims 1-2, 8-9 and 15-16 equate to a mathematical concept because these claims are similar to the concepts of 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)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under their broadest reasonable interpretation (BRI).
The BRI of claims 1, 8 and 15 includes performing the calculations of a least squares fitting method as recited on specification pg. 9, line 21 – pg. 10, line 10. Producing a weighted sum necessitated calculations. Counting a number of data points requires addition.
The BRI of claims 2, 9 and 16 includes performing calculations because they recite summing numerical values.
Limitations reciting a natural phenomenon.
Above cited claims 1-3, 5-10, 12-17 and 19-21 equate to a natural phenomenon because they are similar to the concept of a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017), which the courts have established as a natural phenomenon. The BRI of claims 1-3, 5-10, 12-17 and 19-21 includes using recipient HLA MFI values, donor HLA typing data, and recipient HLA typing data to determine whether a transplant should be performed based upon a risk of rejection.
As such, claims 1-3, 5-10, 12-17 and 19-21 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “using computer learning”
Claim 8 recites “A system for virtual crossmatching using a physical-crossmatch-outcome-data-derived model, the system comprising: a computing platform including at least one processor; wherein the at least one processor is configured to … using computer learning:”
Claims 10 and 12 recite “wherein the at least one processor is configured to …”
Claims 9-14 recite “The system of claim 8/9/11/13 …”
Claim 13 recites “comprising an HLA data-preprocessor for: …”
Claim 14 recites “wherein the HLA data-preprocessor is configured for …”
Claim 15 recites “A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising: … using computer learning”
Claims 16-17 and 19-21 recite “The non-transitory computer readable medium of claim 15/16/18/20 …”
Claims 20-21 recite “wherein the computer-executable instructions further control the computer to perform steps comprising:”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
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). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitations in claims 8-21 of (i) a system comprising a computing platform with at least one processor, (ii) implemented by the at least one processor, (ii) an HLA data pre-processor, (iv) and a non-transitory computer readable medium having stored thereon executable instructions. These limitations are being interpreted as components of a generic computing system. This interpretation is reinforced by Figure 4. As such, these limitations equate to instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. Furthermore, MPEP 2106.05(f)(2) recites that the use of a computer in its ordinary capacity to perform task such as to receive, store, or transmit data does not integrate a judicial exception into a practical application.
Regarding limitation “using computer learning to” in claims 1, 8 and 15, this limitation also equates to mere instructions to implement an abstract idea on a generic computer. The phrase is being interpreted to mean that a computer performs the operations of a model such as a least-squares regression (i.e. mental process and mathematical concept).
As such, claims 1-3, 5-10, 12-17 and 19-21 are directed to an abstract idea and a 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). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitations in claims 1, 8 and 15 of “using computer learning” and in claims 8-21 of (i) a system comprising a computing platform with at least one processor, (ii) implemented by the at least one processor, (ii) an HLA data pre-processor, (iv) and a non-transitory computer readable medium having stored thereon executable instructions. These limitations are being interpreted as components of a generic computing system. This interpretation is reinforced by Figure 4. As such, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Regarding the above cited limitation in claims 15-21 of a non-transitory computer readable medium that stores computer-executable instructions, these limitations equate to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to mere instructions to implement an abstract idea on a computer and to WURC functions/components of a generic computer. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-3, 5-10, 12-17 and 19-21 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive.
Applicant argues that a human cannot perform “using computer learning to select weight to be applied to HLA DSA MFI values” or “wherein the error function counts a number of data points that are falsely identified as above or below a threshold” (pg. 11, last para. – pg. 12, para. 1). Applicant’s argument is not persuasive because:
The phrase “using computer learning” equates to mere instructions to implement an abstract idea on a generic computer. MPEP 2106.04(a)(2).III.C recites that a claim still recites a mental process even if performed on a computer.
Specification pg. 20 discloses that the minimization function
PNG
media_image1.png
96
182
media_image1.png
Greyscale
can be used to determine weights. A human can perform the calculations of this minimization function and make a determination of which weights to select. A human can then count datapoints above or a below a threshold merely by making comparisons.
Moreover, even if these limitations did not recite a mental process, they would still recite a mathematical concept under their BRI in light of the specification.
It is also noted that instant claim 1 does not require selecting weights based on them minimizing an error function. Rather claim 1 recites minimizing an error of predicted and true median channel shifts determined from HLA DSA MFI values, wherein the values are not recited to be associated with selected weights.
Applicant argues that the claims are similar to McRO because a physician would not have used the data driven model to inform a transplant decision (pg. 12, para. 1). Applicant’s argument is not persuasive because:
The creation and use of the model in claim 1 recites an abstract idea. This is not similar to the claimed rules in McRO, which were not identified as reciting a mental process. Because the creation and use of the model in claim 1 recites an abstract idea, it cannot confer an improvement. MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-8, 13-15 and 20-21 are rejected under 35 USC 103 for being unpatentable over Anunciação et al. (“Anunciação”; Transplant immunology 33, no. 3 (2015): 153-158; previously cited on PTO892 mailed 11/28/2025) in view of Weimer et al. (“Weimer”; Human Immunology 79 (2018): 17; published online 31 August 2018; previously cited on PTO892 mailed 11/28/2025) and Draelos (“Measuring Performance: The Confusion Matrix”; published online 17 Feb. 2019; newly cited).
This rejection is newly recited in view of claim amendment.
Claims 1, 8 and 15:
A method for virtual crossmatching using a physical-crossmatch-outcome-data-derived model, the method comprising:
A system for virtual crossmatching using a physical-crossmatch- outcome-data-derived model, the system comprising: a computing platform including at least one processor; a physical-crossmatch-outcome-data-derived model implemented by the at least one processor for:
A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising:
Anunciação discloses EpVix (physical-crossmatch-outcome-data-derived model), a cloud-based tool for epitope reactivity analysis and epitope virtual crossmatching to identify low immunogenic risk donors for sensitized recipients (title). EpVix predicts negative crossmatches and is a cloud-based implementing software such as HLAMatchmaker (abstract). A cloud-based software inherently requires a computer with a processor and non-transitory computer readable media to store the software.
generating a physical-crossmatch-outcome-data derived model by using computer learning to select weights to be applied human leukocyte antigen (HLA) donor specific antibody (DSA) mean fluorescence intensity (MFI) values, using the weights to produce a weighted sum of the HLA DSA MFI values, and minimizing an error function of predicted median channel shifts determined for the HLA DSA MFI values and true physical crossmatch median channel shifts determined from HLA DSA MFI values for a set of patients,
Anunciação discloses EpVix (abstract) and shows in Figure 2 HLA DSA MFI values sorted by an MFI cutoff (pg. 154, sec. 3.2.2, para. 3).
However, Anunciação does not use computer learning to select weights nor calculate a weighted sum of HLA DSA MFI values. Anunciação does not minimize an error function of predicted and true median channel shifts (MCS) determined from HLA DSA MFI values.
Weimer discloses two data-driven algorithms (DDA) that predict flow cytometric (FXM) outcomes using HLA MFI values and donor HLA typing (Aim section). Weimer teaches “The second DDA applied a least-squares regression model to the HLA locus-specific data to predict the actual T or B cell MCS” (minimizing an error function of predicted median channel shifts determined for the HLA DSA MFI values and true physical crossmatch median channel shifts determined from HLA DSA MFI values for a set of patients) (Methods section). Weimer further teaches “The least-squares model increased accuracy to 93.6% and 97.2% for T and B cell MCS, respectively. Class I DSA influenced T and B cell MCS more than class II. The relative importance of an individual HLA locus on T cell prediction was found to be HLA-B > -A > -C. In the least-squares fitting, B64 had a large positive effect while A34 had a large negative effect on T cell MCS” (using computer learning to select weights to be applied HLA DSA MFI values; using the weights to produce a weighted sum of the HLA DSA MFI values) (Results section).
It would have been prima facie to have modified Anunciação’s EpVix algorithm by using a least-squares model to generate a weighted sum of HLA DSA MFI values that minimizes the distance between predicted and true MCS by applying a weight to each antibody against a particular HLA allele group because Weimer states that Class I DSA influenced T and B cell MCS more than class II and the relative importance of an individual HLA locus on T cell prediction was found to be HLA-B > -A > -C (Results section).
One of ordinary skill in the art would have had a reasonable expectation of success for using least-squares to calculate a weighted sum of HLA DSA MFI values that minimizes the distance between predicted and true MCS when generating a virtual crossmatch because Weimer states that the method achieved high accuracy for their virtual crossmatch (Results section).
wherein the error function counts a number of data points that are falsely identified as above or below a threshold;
Anunciação discloses in Figure 1 HLA DSA MFI values (data points), and Weimer discloses the least-squares regression (error function) (Results section). However, Anunciação and Weimer do not count a number of data points falsely identified as above or below a threshold.
Draelos measures model prediction performance using a confusion matrix for (pg. 1, para. 1). The confusion matrix contains counts of false positives and false negatives (Figure on pg. 1). The confusion matrix determines if an output is above a numerical decision threshold. For example, a probability smaller than 0.5 is part of negative class while above 0.5 is part of positive class. The decision threshold is necessary to calculate the confusion matrix (pg. 3, sec. Decision thresholds).
It would have been prima facie obvious to calculate a confusion matrix, as taught by Draelos, from the least-squares model of Anunciação and Weimer used to predict actual T and B cell MCS. Motivation is provided by Draelos who recites “By looking at a confusion matrix, you can gain a better understanding of the strengths and weakness of your model, and you can better compare two alternative models to understand which one is better for your application” (pg. 1, para. 1). One of ordinary skill would have recognized that the confusion would allow better understand of the MCS predictions. There would have been a reasonable expectation of success to calculate a confusion matrix for least-squares because it can be used for classification tasks.
receiving, as inputs, HLA antibody MFI data of a prospective tissue recipient and HLA typing data of a tissue donor;
Anunciação teaches that EpVix takes as input a recipient’s histocompatibility exam results such as HLA typing and LSA panels, wherein the LSA panel produce HLA MFI values (pg. 154, sec. 3.2.1, para. 1) (Figure 1) as well as a donor’s HLA typing data (pg. 154, sec. 3.2.2, para. 3).
generating, based on the inputs and a physical-crossmatch-outcome-data- derived model, a predicted virtual crossmatch outcome for the prospective tissue recipient; and
Anunciação teaches that the EpViX software performs a virtual crossmatch of the donor's HLA against each recipient's serum (pg. 154, sec. 3.2.2, para. 3). Figure 2 shows that a red epitope-based virtual crossmatch (EvXM) “indicates that the recipient's serum shows DSA with reactive epitopes and an MFI value above five thousand, thereby excluding the recipient” (pg. 154, sec. 3.2.2, para. 3).
using the predicted virtual crossmatch outcome to inform a transplant decision for the prospective tissue recipient.
Anunciação teaches that EpVix helps transplants teams make safer and smarter decision (pg. 165, col. 1, para. 1), particularly for organ donor allocation (pg. 157, col. 1, para. 3). A representative case is described in section 3.2.4, where a female patient was identified as a match based upon the EvXM.
Claim 5:
The broadest reasonable interpretation of claim 5 includes it not being required because it is a contingent limitation. Claim 5 is not performed when the MCS calculated for a patient does not exceed a MCS cutoff. See MPEP 2111.04(II) regarding contingent limitations in a method claim. As such, claim 5 is rejected because it is not required and because claim 4, to which it depends, is rejected above.
Claims 6, 13 and 20:
Anunciação uses as input recipient HLA typing and LSA panels (recipient HLA MFI data and recipient HLA typing data) (pg. 154, sec. 3.2.1, para. 1) (Figure 1) and donor HLA typing data (donor HLA typing) (pg. 154, sec. 3.2.2, para. 3) (Figure 2). Figure 2 shows a list of eplets derived from the inputs (a list of recipient and donor eplet data). EpViX produces a list of non-self eplets of each allele tested in the LSAB (removing, from the list, eplets that are common to the recipient and donor eplet data) (pg. 154, sec. 3.2.1, para. 1) (Figure 1). Figure 2 shows the eplets that are not common between a donor and recipient, identified as DSA, are inputted into the EpViX algorithm (pg. 154, sec. 3.2.2, para. 3) (pg. 154, sec. 3.2.4, para. 1).
Claims 7, 14 and 21:
The broadest reasonable interpretation of claims 7, 14 and 21 includes removing eplets from the list that do not exceed an MFI threshold (i.e., unverified). Anunciação teaches that the list of eplets can be filtered based upon an MFI value not exceeding a threshold, which is indicated by a blue number in the MFI column (Figures 1 and 2). Figure 1 shows that eplets identified as blue have an MFI value lower than the cutoff. The unacceptable filter in Figure 1 filters out of the blue eplets (unverified) and keeps the red and black eplets that are reactive (pg. 154, sec. 3.2.1, para. 1).
Claims 2-3, 9-10 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Anunciação et al. (“Anunciação”; Transplant immunology 33, no. 3 (2015): 153-158; previously cited on PTO892 mailed 11/28/2025) in view of Weimer et al. (“Weimer”; Human Immunology 79 (2018): 17; published online 31 August 2018; previously cited on PTO892 mailed 11/28/2025) and Draelos (“Measuring Performance: The Confusion Matrix”; published online 17 Feb. 2019; newly cited), and in further view of Alzahrani et al. (“Alzahrani”; In Transplantation Proceedings, vol. 51, no. 2, pp. 488-491. Elsevier, 2019; previously cited on PTO892 mailed 11/28/2025).
This rejection is newly recited in view of claim amendment.
The limitations of claims 1, 8 and 15 have been taught in the rejection above by Anunciação, Weimer and Draelos.
Claims 2, 9 and 16:
The following limitation is a product by process limitation: “a threshold determined empirically from physical crossmatch outcomes of a plurality of patients”. MPEP 2113.I recites “The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process.” In the instant case, the product is the “threshold” and the process previously performed to derive the product is “determined empirically from physical crossmatch outcomes of a plurality of patient”. As such, a reference teaching a threshold for summed HLA DSA MFI values will read on this product by process limitation.
Anunciação uses an LSAB panel that tested anti-HLA antibodies against different HLA molecules, which have associated sets of eplets (Figure 1) (pg. 154, sec. 3.2.1, para. 1). Figure 2 shows HLA DSA MIF values (HLA DSA MFI values of the prosecutive tissue recipient). Anunciação also teaches a threshold for MFI in Figure 1 (optimal threshold model).
However, Anunciação does not sum and compare HLA DSA MFI values to a threshold.
Alzahrani evaluates concordance between actual flow crossmatches (aFXMs) and virtual flow crossmatches (vFXMs) (abstract). Alzahrani teaches “A sum of multiple weak antibodies (MFI < 2000) that yield MFI of > 3000 was used to predict positivity for patients with multiple antibodies” (pg. 489, col. 1, para. 3).
It would have been prima facie obvious to have modified Anunciação’s EpViX by summing multiple weak antibodies that together yield MFI > 3000 because Alzahrani states that this predicts positivity for patients with multiple weak antibodies (pg. 489, col. 1, para. 3). There would have been a reasonable expectation of success to modify the EpViX algorithm to sum and compare DSA MFI values to a threshold because Anunciação shows in Figure 2 DSAs with associated MFI values, which could then be summed and compared to a threshold as taught by Alzahrani.
Claims 3, 10 and 17:
Anunciação uses EpViX for organ allocation (pg. 154, sec. 3.2), and states that when a recipient’s serum shows DSA with reactive epitopes and an MFI > 5,000, then the patient is not a match (pg. 154, sec. 3.2.2, para. 3). However, Anunciação does not teach summing the HLA DSA MFI values to inform transplant decision.
Alzahrani teaches “A sum of multiple weak antibodies (MFI ≤ 2000) that yield MFI of ≥3000 was used to predict positivity for patients with multiple antibodies” (pg. 489, col. 1, para. 3).
It would have been prima facie obvious to have determined that a patient is not a match in Anunciação when a sum of weak DSA MFI values exceeds a threshold because the patient would have been identified as positive, as taught by Alzahrani. One of ordinary skill in the art would have had a reasonable expectation of success for deciding not to perform a transplant on a patient with a cumulative DSA MFI value that exceeds a threshold because exceeding the threshold indicates a potential transplant rejection.
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Anunciação et al. (“Anunciação”; Transplant immunology 33, no. 3 (2015): 153-158; previously cited on PTO892 mailed 11/28/2025) in view of Weimer et al. (“Weimer”; Human Immunology 79 (2018): 17; published online 31 August 2018; previously cited on PTO892 mailed 11/28/2025) and Draelos (“Measuring Performance: The Confusion Matrix”; published online 17 Feb. 2019; newly cited), and in further view of Schinstock et al. (“Schinstock”; Transplantation 101, no. 10 (2017): 2429-2439; previously cited on PTO892 mailed 11/28/2025).
This rejection is newly recited in view of claim amendment.
The limitations of claims 8 and 15 have been taught in the rejection above by Anunciação Weimer and Draelos.
Claims 12 and 19:
Anunciação uses EpViX for organ allocation (pg. 154, sec. 3.2), and states that when a recipient’s serum shows DSA with reactive epitopes and an MFI > 5,000, then the patient is not a match (pg. 154, sec. 3.2.2, para. 3). Weimer applies a least-squares regression model to HLA locus-specific data to predict the actual T or B cell MCS (Methods).
However, Anunciação Weimer and Draelos do not perform a transplant if a MCS calculated for a patient exceeds a MCS cutoff.
Schinstock compares the outcomes of 954 patients transplanted with varied levels
of baseline DSA detected by single antigen beads and B flow cytometric crossmatch (abstract). Schinstock states “A mean channel fluorescence shift greater than 106 for was considered positive for B cells” (pg. 2430, col. 1, last para.)
It would have been prima facie obvious to have determined that a recipient is not a transplant match using predicted and true MCS, as taught by Anunciação and Weimer, when the recipient’s predicted MCS exceeds a MCS greater than 106 because Schinstock states that this indicates a recipient is positive for B cells (pg. 2430, col. 1, last para.). One of ordinary skill in the art would have had a reasonable expectation of success for deciding not to perform a transplant on a recipient with a predicted MCS that exceeds a 106 cutoff because exceeding the threshold indicates a potential transplant rejection.
Response to Arguments under 35 USC 103
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive.
Applicant argues that Weimer does not use an error function that minimizes a count of errors that fall outside a threshold range (pg. 13-14). Examiner agrees. However, a new ground of rejection has been made, in view of claim amendment, that relies upon newly cited reference Draelos to teach this new limitation.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah A. Auger whose telephone number is (703)756-4518. The examiner can normally be reached M-F 7:30-4:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
Prosecution Insights