DETAILED ACTION
The Applicant’s filing, received 06 November 2023, has been fully considered. This application is a CONTINUATION of Application # 16/826,042, now abandoned. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
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 the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claims 1 and 15, 16, and 17 are objected to.
Priority
Claims 1-20 are given benefit of the claim of priority to provisional application # 62/822,730, filed 22 March 2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09 January 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 06 November 2023 are objected to for the reasons noted below.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference #’s 108, 110, 122-1, 122-2, 122-R, 124-1-1, 124-1-2, 124-1-Y, 126-1-1-1, 126-1-1-2, 126-1-2-1, 126-1-2-2, 126-1-2-M, 126-1-1-N, 128-1, 128-R, 128-T, 130-1-1, 130-1-2, 130-1-T, 132-1-1, 132-1-2, and 132-1-T in Figure 1A;
Reference #’s 138-1, 138-2, 138-M, 138-K, 140-1-1, 140-1-2, 140-1-X, 140-2-1, 140-2-2, 140-2-Y, 142-1, 12-2, 144-1-1, 144-1-2, 144-1-K, 144-2-1, 144-2-2, 144-2-K, 146-1-1, 146-1-2, 146-1-K, 146-2-1, 146-2-2, and 146-2-K in Figure 1B;
Reference # 262 in Figure 2E;
Reference # 272 in Figure 2F;
Reference #’s 310, 312, 314, 360-1, 362-1, and 364-1, in Figure 3;
Reference #’s 302, 308, 310, 312, and 314 in Figure 4;
Reference #’s 502 and 504 in Figure 5A;
Reference #’s 506 and 508 in Figure 5B;
Reference #’s 602, 604, and 606 in Figure 6A and Figure 6B;
Reference #’s 702, 704, and 706 in Figure 7A and Figure 7B;
Reference #’s 802, 804, and 806 in Figure 8A and Figure 8B; and
Reference # 1420 in Figure 14.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Reference #’s 122, 124, 126, 130, and 132 in Figure 1A and as described in paragraph [0054] in the Specification;
Reference #’s 138, 142, 144, 148, 150, 152, 154, 156, 157, 160, 162, 164, and 166 in Figure 1B and as described in paragraph [0055] in the Specification;
Plurality of modules 152, plurality of features 154, and independent phenotype 157 in Figure 2A as described in paragraph [0058] in the Specification;
Independent phenotype 157 in Figure 2B as described in paragraphs [0064], [0069], [0070], [0071] in the Specification;
Block 258 of Figure 2F as described in paragraph [0095] in the Specification;
Main classifier 300, feature values 354, plurality of modules 352 in Figure 3 as described in paragraph [00122] in the Specification; and
Feature value 354-2, main classifier 300, modules 302-2 through 302-6, in Figure 4 as described in paragraph [00122] in the Specification.
The drawings are further objected to because not all of the reference #’s in the drawings point to the correct description in the Specification, e.g., Figure 1A indicates that reference # 136 is a variant set data store, however the Specification describes reference # 136 only as raw feature data at para. [0054].
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraphs [0054], [0056], [0057], [00112], [00131], and [00185] refer to Figure 1, however the sheets of drawings do not include a Figure 1;
Paragraphs [0057], [00130], and [00146] refer to Figure 2, however the sheets of drawings do not include a Figure 2;
Paragraph [00196] refers to Figure 5, however the sheets of drawings do not include a Figure 5;
Paragraph [00198] refers to Figure 6, however the sheets of drawings do not include a Figure 6;
Paragraph [00198] refers to Figure 7, however the sheets of drawings do not include a Figure 7;
Paragraph [00198] refers to Figure 8, however the sheets of drawings do not include a Figure 8;
Paragraph [00207] refers to Figure 11, however the sheets of drawings do not include a Figure 11; and
Paragraph [00185] refers to Figure 12 and shows reference #’s not present in Figure 12.
Appropriate correction is required.
Claim Interpretation
Claim 1 recites the limitation “processing a test sample, comprising blood from the test patient, to generate a test dataset.” This limitation is interpreted to require an active step of performing genomic sequencing to obtain the sequence reads from the nucleic acid obtained from the patient’s biological sample (Specification, paras. [0076] & [0077]).
Claim 1 further recites the limitation “processing the test dataset with a main classifier structured as a feed-forward neural network comprising an input layer and an output layer, wherein the main classifier is generated from a heterogenous repository of input data, the heterogenous repository comprising a set of datasets comprising: a first dataset comprising…, and a second dataset acquired…corresponding to the status of the condition.” This limitation is interpreted to be a product-by-process limitation with the product being the main classifier structured as a feed-forward neural network comprising an input layer and an output layer, and further interpreted to not require the process of performing the active steps of producing the product (e.g., accessing a heterogeneous repository of input data; and training the classifier).
Claim Objections
Claim 1 is objected to because of the following informalities: The limitations reciting “a first dataset comprising…” and “a second dataset acquired…” should be indented such that they are identifiable as components of the “processing the test dataset with a main classifier…” step of the claim, and therefore not have the same indentation as the other limitations.
Claim 15 is objected to because of the following informalities: The genes “CEACAMI1” and “CNA15” appear to contain typographical errors, i.e., “CEACAMI1” should be “CEACAM1” and “CNA15” should be “GNA15.”
Claim 16 is objected to because of the following informalities: The claim should depend from claim 15, and not from claim 13.
Claim 17 is objected to because of the following informalities: The gene “C11orf7” appears to contain a typographical error, i.e., “C11orf7” should be “C11orf74.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is indefinite for reciting “generating a characterization of absence, presence, or stage of the condition in the test patient upon:” in lines 3-4 and further reciting “generating an output, comprising a characterization of a set of expression values…” at lines 16-17, and therefore it is not clear as to whether the step of “generating a characterization of absence, presence, or stage…” is contingent upon the step of “generating an output, comprising…” The step of generating a characterization is interpreted to comprise the steps of “processing a test sample…”, “processing the test dataset…”, “generating an output…”, and “based on the output, treating the patient.”
Claim 1 is further indefinite for reciting “generating a characterization of absence, presence, or stage of the condition in the test patient upon:” in lines 3-4 and further reciting “based upon the output, treating the test patient” in 18 of the claim, and therefore it is not clear as to whether the step of “generating a characterization of absence, presence, or stage…” is contingent on the “based upon the output, treating the test patient” step.
Claims 2-20 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 4 recites the limitation “the set of datasets” in line three. There is insufficient antecedent basis for this limitation in the claim, because claim 1 recites “generate a test dataset” in line six (i.e., a singular dataset).
Claim 5 recites the limitation “the 3 datasets” in line two. There is insufficient antecedent basis for this limitation in the claim, because the claim recites “at least 3 datasets” in lines 1-2.
Claim 7 is indefinite for reciting “acquiring values of the first plurality of features represented in the first dataset, upon processing a sample of the…” at step (A) and “acquiring values of the second plurality of features represented in the second dataset upon processing a sample of the…” at step (B), because it is not clear as to whether the steps of acquiring values are contingent on the steps of processing the samples, and further not clear as to whether steps (C) and (D) are contingent on the steps of processing the samples. Claim 7 is interpreted as affirmatively reciting steps of processing samples at steps (A) and (B) that require the claim to perform steps (C) and (D).
Claims 8-10 are indefinite for depending from claim 7 and for not remedying the indefiniteness of claim 7.
Claim 8 is further indefinite for reciting “adjusting the expected expression value” in lines 3-4, because the claim previously recites “an expected expression value of each of the first plurality of features and the second plurality of features” and therefore it is not clear as to which expected expression value the “adjusting” step is referring to.
Claims 14 and 16 recite the limitation “the patient” in line one. There is insufficient antecedent basis for this limitation in the claim, because claim 1 recites “a test patient.”
Claim 20 recites the limitation “the first condition” in line one. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-20 are directed to a method (i.e., process) for evaluating a condition of a test patient.
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
generating a characterization of absence, presence, or stage of the condition in the test patient (i.e., mental processes);
processing the test data (i.e., mental processes and mathematical concepts); and
generating an output, comprising a characterization of a set of expression values of a set of features, from the output layer (i.e., mental processes and mathematical concepts).
Dependent claims 2-10, 13, 15, and 17-20 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
wherein the condition comprises an infection or sterile inflammation (i.e., mental processes).
Dependent claim 3 further recites:
wherein the input layer of the main classifier is configured to receive a set of summarizations of feature values from each of a set of feeder neural network input layers (i.e., mental processes).
Dependent claim 4 further recites:
wherein the set of feeder neural network input layers comprises a first feeder input layer for processing mRNA abundance values for a first set of genes represented in the set of datasets, and a second feeder input layer for processing mRNA abundance values for a second set of genes represented in the set of datasets (i.e., mental processes).
Dependent claim 5 further recites:
wherein the set of datasets comprises at least 3 datasets corresponding to 3 cohorts of patients, wherein each of the 3 datasets is generated using a different measurement technique (i.e., mental processes).
Dependent claim 6 further recites:
generating and training the main classifier, wherein the set of datasets used to train the main classifier comprises non-overlapping, independent datasets (i.e., mental processes and mathematical concepts).
Dependent claim 7 further recites:
(C) co-normalizing values for features present in the first dataset and the second dataset to remove an inter-dataset batch effect, wherein co-normalizing comprises implementing a co-normalization function that requires healthy controls for derivation of correction factors, thereby calculating, for each respective training subject in the first plurality of training subjects and for each respective training subject in the second plurality of training subjects, a set of co-normalized feature values (i.e., mental processes and mathematical concepts); and
(D) training the main classifier against a composite training set, to evaluate the test subject for the clinical condition (i.e., mental processes and mathematical concepts), the composite training set comprising, for each respective training subject in the first plurality of training subjects and for each respective training subject in the second plurality of training subjects:
(i) a summarization of the set of co-normalized feature values (i.e., mental processes), and
(ii) an indication of the absence, presence or stage of the condition (i.e., mental processes).
Dependent claim 8 further recites:
wherein co-normalizing feature values comprises determining an expected expression value of each of the first plurality of features and the second plurality of features and adjusting the expected expression value for modifications of mean and standard deviation attributed to execution of the first measurement technique and the second measurement technique (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
wherein co-normalizing feature values is performed iteratively, with acceptance of a third dataset generated using a third measurement technique, for training the main classifier (i.e., mental processes and mathematical concepts).
Dependent claim 10 further recites:
wherein the inter-dataset batch effect includes an additive component and a multiplicative component and wherein co-normalizing shrinks resulting parameters representing the additive component and a multiplicative component (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
wherein values of the set of features comprises expression values of a first set of genes comprising: IFI27, JUP, and LAX1 (i.e., mental processes).
Dependent claim 15 further recites:
wherein values of the set of features comprises expression values of a first set of genes comprising: CEACAMI1, ZDHHC19, C9orf95, CNA15, BATF, and C3AR1 (i.e., mental processes).
Dependent claim 17 further recites:
wherein values of the set of features comprises expression values of a first set of genes comprising: DEFA4, CD163, RGS1, PER1, HIF1A, SEPP1, C11orf7 and CIT (i.e., mental processes).
Dependent claim 18 further recites:
classifying the test patient as at risk for death within 30 days of hospital admission (i.e., mental processes).
Dependent claim 19 further recites:
wherein the first plurality of features comprises nucleic acid expression features, and wherein the second plurality of features comprises protein expression features (i.e., mental processes).
Dependent claim 20 further recites:
wherein the status of the first condition is a diseased condition, and wherein the first dataset comprises data from a first subportion of subjects that are free of the diseased condition, and a second subportion of subjects that exhibit the diseased condition (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., generating a characterization of a dataset is a mental process that may involve observation, evaluation, judgement, and opinion), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., co-normalizing values in different datasets to remove an inter-dataset batch effect involves performing mathematical calculations) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-6, 8-10, 13, 15, and 17-20 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
processing a test sample, comprising blood from the test patient, to generate a test dataset (interpreted to mean performing genomic sequencing);
using a main classifier structured as a feed-forward neural network comprising an input layer and an output layer, wherein the main classifier is generated from a heterogenous repository of input data, the heterogenous repository comprising a set of datasets comprising: a first dataset comprising a first plurality of features comprising values corresponding to a status of the condition, and a second dataset acquired independently of the first dataset and comprising a second plurality of features different from the first plurality of features and comprising values corresponding to the status of the condition; and
based upon the output, treating the test patient.
The additional elements in dependent claims 7, 11, 12, 14, and 16 include:
(A) for each respective training subject in a first plurality of training subjects: acquiring values of the first plurality of features represented in the first dataset, upon processing a sample of the respective training subject with a first measurement technique, wherein the first dataset comprises, for each respective training subject in the first plurality of training subjects an indication of the absence, presence or stage of the condition (claim 7);
(B) for each respective training subject in a second plurality of training subjects independent of the first plurality of training subjects: acquiring values of the second plurality of features represented in the second dataset upon processing a sample of the respective training subject with a second measurement technique different from the first measurement technique, wherein the second dataset comprises, for each respective training subject in the second plurality of training subjects an indication of the absence, presence or stage of the condition (claim 7);
generating the first dataset using a first measurement technique, and generating the second dataset using a second measurement technique (claim 11);
the first measurement technique comprises RNAseq for a first cohort, and wherein the second measurement technique comprises using DNA microarrays for a second cohort different from the first cohort (claim 12);
treating the patient comprises treating the test patient for a viral infection (claim 14); and
treating the patient comprises treating the test patient for sepsis (claim 16).
The additional elements of processing a test sample, comprising blood from the test patient, to generate a test dataset (claim 1); generating the first dataset using a first measurement technique, and generating the second dataset using a second measurement technique (claim 11); and the first measurement technique comprises RNAseq for a first cohort, and wherein the second measurement technique comprises using DNA microarrays for a second cohort different from the first cohort (claim 12); are steps in the process of gathering data for use in the claimed process, and therefore do not add more than insignificant extra-solution activities to the judicial exceptions (MPEP 2106.05(g)).
The additional element of using a main classifier structured as a feed-forward neural network comprising an input layer and an output layer (claim 1) provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)), and merely confines the use of the abstract idea to the particular technological environment of neural networks (MPEP 2106.05(h)).
Furthermore, the claim 1 additional elements of the main classifier is generated from a heterogenous repository of input data, the heterogenous repository comprising a set of datasets comprising: a first dataset comprising a first plurality of features comprising values corresponding to a status of the condition, and a second dataset acquired independently of the first dataset and comprising a second plurality of features different from the first plurality of features and comprising values corresponding to the status of the condition; merely serve to further limit the foregoing additional element of a main classifier structured as a feed-forward neural network, and therefore do not integrate the judicial exceptions into a practical application.
The additional elements in claim 7 of (A) for each respective training subject in a first plurality of training subjects: acquiring values of the first plurality of features represented in the first dataset, upon processing a sample of the respective training subject with a first measurement technique, wherein the first dataset comprises, for each respective training subject in the first plurality of training subjects an indication of the absence, presence or stage of the condition; and (B) for each respective training subject in a second plurality of training subjects independent of the first plurality of training subjects: acquiring values of the second plurality of features represented in the second dataset upon processing a sample of the respective training subject with a second measurement technique different from the first measurement technique, wherein the second dataset comprises, for each respective training subject in the second plurality of training subjects an indication of the absence, presence or stage of the condition; are steps in the process of gathering data for use in the claimed process, and therefore do not add more than insignificant extra-solution activities to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of based upon the output, treating the test patient (claim 1); treating the patient comprises treating the test patient for a viral infection (claim 14); and treating the patient comprises treating the test patient for sepsis (claim 16); do not amount to more than a recitation of the words “apply it” and therefore do not amount to more than mere instructions to implement an abstract idea, because the claims fail to recite details of how a solution (i.e., the treatment) to a problem is accomplished, and also because the generality of the application of the judicial exception does not provide more than a broad applicability to the treatment step (MPEP 2106.05(f)).
Furthermore, the additional elements of based upon the output, treating the test patient (claim 1); treating the patient comprises treating the test patient for a viral infection (claim 14); and treating the patient comprises treating the test patient for sepsis (claim 16); do not affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition, because the treatment step must be “particular,” i.e., specifically identified (MPEP 2106.05(d)(2)).
Thus, the additionally recited elements merely invoke a computer as a tool (i.e., using a neural network), and/or amount to insignificant extra-solution activity, and/or do not amount to more than mere instructions to apply an exception, and/or do not affirmatively recite a particular treatment, and as such, when all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-6, 8-10, 13, 15, and 17-20 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claim 1 and dependent claims 7, 11, 12, 14, and 16 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A: Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of using a main classifier structured as a feed-forward neural network comprising an input layer and an output layer (claim 1) (i.e., computers and/or computer components); and acquiring data (claim 7); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of processing a test sample, comprising blood from the test patient, to generate a test dataset (claim 1) (interpreted to mean performing genomic sequencing); comprising generating the first dataset using a first measurement technique, and generating the second dataset using a second measurement technique (claim 11); and wherein the first measurement technique comprises RNAseq for a first cohort, and wherein the second measurement technique comprises using DNA microarrays for a second cohort different from the first cohort (claim 12); are conventional. Evidence for the conventionality is shown by Ong et al. (Journal of Developmental Origins of Health and Disease, 2015, Vol. 6(1), pp. 10-16). Ong et al. reviews computational and statistical methods in DNA methylation analyses and interpretation, and the challenges of gene-environment interaction analyses, and multiple genome-wide molecular data integration (Title; Abstract; and page 10, col. 2, para. 2). Ong et al. shows using Infinium450K data from blood of individuals of different ages (page 12, col. 2, para. 3); and further shows integration of multiple genome-wide molecular data sets allowing one to explore the causal relationships between the different layers of biological control, e.g., exploring the causal relationship between genotype, DNA methylation and gene expression by combining data from RNA-seq, SNP genotyping and the Infinium450K array performed on umbilical cords of newborn infants (page 13, col. 1, paras. 2-3).
The additional elements of treating the test patient (claim 1); treating the patient comprises treating the test patient for a viral infection (claim 14) and treating the patient comprises treating the test patient for sepsis (claim 16); are conventional. Evidence for the conventionality is shown by Plunkett et al. (BMJ, 2015, Vol. 350:h3017, pp. 1-12, as cited in the Information Disclosure Statement (IDS) received 09 January 2024). Plunkett et al. reviews sepsis in children (Title; and Abstract) and shows antibiotic therapy (page 8, col. 1, paras. 8-9 through col. 2, paras. 1-7; and Box 6), and further shows antiviral therapy (page 8, col. 2, para. 10 through page 9, col. 1, para. 1).
Therefore, when taken alone, all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as a combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
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.
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.
Claims 1-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (Science Translational Medicine, 2016, Vol. 8, No. 346, pp. 1-12, as cited in the Information Disclosure Statement (IDS) received 09 January 2024) in view of Ahn et al. (IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, Madrid, Spain, pp. 1748-1752, doi:10.1109/BIBM.2018.8621108) in view of Khatri et al. (WO 2018/004806).
Regarding claim 1, Sweeney et al. shows a robust classification decision model for discrimination of bacterial and viral infections using a multicohort analysis and independent cohorts (Title; and Abstract); and datasets generated from test samples that are whole blood (Table 1 and Table 2).
Regarding claim 1, Sweeney et al. does not show processing the test dataset with a main classifier structured as a feed-forward neural network comprising an input layer and an output layer, wherein the main classifier is generated from a heterogenous repository of input data, the heterogenous repository comprising a set of datasets comprising: a first dataset comprising a first plurality of features comprising values corresponding to a status of the condition, and a second dataset acquired independently of the first dataset and comprising a second plurality of features different from the first plurality of features and comprising values corresponding to the status of the condition; generating an output, comprising a characterization of a set of expression values of a set of features, from the output layer; and based upon the output, treating the test patient.
Regarding claim 1, Ahn et al. shows a deep neural network-based classification model using a feed-forward network architecture for the identification of cancer and normal samples (page 1750, col. 1, para. 1); a heterogenous data set for training the classification model comprising statuses of the condition (Table II.); and generating an output of expression profiles for classification of cancer (page 1751, col. 2, paras. 2-3; and Fig. 5).
Regarding claim 1, Sweeney et al. in view of Ahn et al. does not show based upon the output, treating the test patient.
Regarding claim 1, Khatri et al. shows biomarkers and methods of using them for aiding diagnosis, prognosis, and treatment of critically ill patients with sepsis, severe trauma, or burns (Title; and Abstract). Khatri et al. further shows diagnosing and treating a patient having an infection, the method comprising: a) obtaining a biological sample from the patient; b) measuring levels of expression biomarkers; and c) administering a sepsis treatment if the patient is diagnosed with sepsis (page 22, lines 10-23).
Regarding claim 2, Sweeney et al. further shows an integrated antibiotics decision model that can discriminate between patients with severe acute infections and those with inflammation (page 10, col. 1, para. 3).
Regarding claim 7, Sweeney et al. further shows the limitations recited in step (C) of the claim, i.e., the COCONUT method which co-normalizes control samples from different cohorts to allow for direct comparison of diseased samples from those same cohorts and to obtain batch-corrected data (page 9, col.1, paras. 4-5 through col. 2, para. 7).
Regarding claim 9, Sweeney et al. further shows a co-normalizing method that is solved iteratively (page 9, col. 2, para. 2).
Regarding claim 10, Sweeney et al. further shows correcting for location and scale of each gene by first solving an ordinary least-squares model for gene expression and then shrinking the resulting parameters using an empirical Bayes estimator, solved iteratively, and with additive and multiplicative batch effects (page 9, col. 2, paras. 2-7).
Regarding claim 13, Sweeney et al. further shows a gene set optimized for diagnosis [higher in viral infections (IFI27, JUP, and LAX1)] (page 2, col. 2, para. 1).
Regarding claim 20, Sweeney et al. further shows data sets that comprise subsets of data representing diseased and healthy conditions (page 2, Table 1).
Sweeney et al. does not show the input layer of the main classifier is configured to receive a set of summarizations of feature values from each of a set of feeder neural network input layers (claim 3); the set of feeder neural network input layers comprises a first feeder input layer for processing mRNA abundance values for a first set of genes represented in the set of datasets, and a second feeder input layer for processing mRNA abundance values for a second set of genes represented in the set of datasets (claim 4); the set of datasets comprises at least 3 datasets corresponding to 3 cohorts of patients, wherein each of the 3 datasets is generated using a different measurement technique (claim 5); generating and training the main classifier, wherein the set of datasets used to train the main classifier comprises non-overlapping, independent datasets (claim 6); steps (A), (B), and (D) (claim 7); co-normalizing feature values comprises determining an expected expression value of each of the first plurality of features and the second plurality of features and adjusting the expected expression value for modifications of mean and standard deviation attributed to execution of the first measurement technique and the second measurement technique (claim 8); a third dataset generated using a third measurement technique, for training the main classifier (claim 9); generating the first dataset using a first measurement technique, and generating the second dataset using a second measurement technique (claim 11); or the first measurement technique comprises RNAseq for a first cohort, and wherein the second measurement technique comprises using DNA microarrays for a second cohort different from the first cohort (claim 12); treating the patient comprises treating the test patient for a viral infection (claim 14); treating the patient comprises treating the test patient for sepsis (claim 16); values of the set of features comprises expression values of a first set of genes comprising: DEFA4, CD163, RGS1, PER1, HIF1A, SEPP1, C11orf7 and CIT (claim 17); classifying the test patient as at risk for death within 30 days of hospital admission (claim 18); and the first plurality of features comprises nucleic acid expression features, and wherein the second plurality of features comprises protein expression features (claim 19).
Regarding claim 3, Ahn et al. shows manually curated data from multiple datasets (page 1749, Methods: Sections A & B).
Regarding claim 4, Ahn et al. shows different data sources used in the study from different platforms comprising different sets of gene expression data (page 1749, Table I; and Fig. 1).
Regarding claim 6, Ahn et al. shows training data from different databases comprising different platforms (page 1749, Tables I, II, and III).
Regarding claim 7, steps (A), (B), and (D), Ahn et al. shows training data from different datasets generated from different gene expression platforms and comprising normal and diseased conditions (page 1749, Tables I, II, and III; and Fig. 1).
Regarding claim 8, Ahn et al. shows performing within-sample standardization using the mean and standard deviation of gene expression values (page 1749, col. 2, para. 1).
Regarding claim 9, Ahn et al. shows training a classifier model (page 1749, col. 2, Section B.).
Regarding claims 11 and 12, Ahn et al. shows different datasets generated from different gene expression measuring platforms, i.e., microarray and RNA sequencing (page 1749, Table I).
Sweeney et al. in view of Ahn et al. do not show the set of datasets comprises at least 3 datasets corresponding to 3 cohorts of patients, wherein each of the 3 datasets is generated using a different measurement technique (claim 5); a third dataset generated using a third measurement technique (claim 9); treating the patient comprises treating the test patient for a viral infection (claim 14); treating the patient comprises treating the test patient for sepsis (claim 16); values of the set of features comprises expression values of a first set of genes comprising: DEFA4, CD163, RGS1, PER1, HIF1A, SEPP1, C11orf7 and CIT (claim 17); classifying the test patient as at risk for death within 30 days of hospital admission (claim 18); and the first plurality of features comprises nucleic acid expression features, and wherein the second plurality of features comprises protein expression features (claim 19).
Regarding claim 5, Khatri et al. further shows an integrated multi-cohort meta-analysis framework to analyze multiple gene expression datasets to identify a set of genes that can predict mortality in patients with sepsis using data from two different gene expression microarray public repositories in addition to datasets from Glue Grant (page 43, lines 10-30 through page 44, lines 1-23).
Regarding claim 9, Khatri et al. further shows that expression levels of biomarkers are determined by measuring polynucleotide levels, and that the levels of transcripts of specific biomarker genes can be determined from the amount of mRNA or polynucleotides derived therefrom, and that polynucleotides can be detected and quantitated by a variety of methods including microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, and serial analysis of gene expression (SAGE) (page 23, lines 30-31 through page 24, lines 1-5).
Regarding claim 14, Khatri et al. further shows a critically ill patient diagnosed with a viral infection is further administered a therapeutically effective dose of an antiviral agent (page 19, lines 27-31).
Regarding claim 16, Khatri et al. further shows administering a sepsis treatment comprising antimicrobial therapy, supportive care, or an immune-modulating therapy if the patient is diagnosed with sepsis (page 5, lines 27-28).
Regarding claims 17 and 18, Khatri et al. further shows a method for determining mortality risk and treating a patient suspected of having a life-threatening condition by first analyzing the levels of expression and wherein increased levels of expression of the DEFA4, CD163, PER1, RGS1, HIF1A, SEPP1, C11orf74, and CIT biomarkers compared to the reference value ranges for the biomarkers for a control subject indicate that the patient is at high risk of mortality within 30 days (page 3, lines 12-24).
Regarding claim 19, Khatri et al. further shows that patient data is analyzed by one or more methods including multi-dimensional protein identification technology (MUDPIT) technology (page 6, lines 8-12).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Sweeney et al. by incorporating methods for deep learning-based classification of patient conditions using gene expression data as shown by Ahn et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the method of Sweeney et al. with the method of Ahn et al. because Ahn et al. explains that deep learning has proven to show outstanding performance in resolving recognition and classification problems (Abstract), and shows a method for building a universal classifier by deep learning of gene expression data stored in large public databases. This modification would have had a reasonable expectation of success given that both Sweeney et al. and Ahn et al. disclose using gene expression data from multiple gene expression repositories to generate multicohort analysis frameworks to analyze multiple gene expression datasets to identify biomarkers that can classify patients according to a condition, e.g., patients that are healthy versus patients with cancer or with bacterial or viral infections.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Sweeney et al. in view of Ahn et al. by incorporating biomarkers and methods of using them for prognosis of mortality in critically ill patients with sepsis, as shown by Khatri et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the method of Sweeney et al. in view of Ahn et al. with the method of Khatri et al. because Khatri et al. shows using biomarkers identified from gene expression analyses for determining mortality risk and treating a patient suspected of having a life-threatening condition such as sepsis. This modification would have had a reasonable expectation of success given that both Sweeney et al. in view of Ahn et al. and Khatri et al. disclose using gene expression levels of biomarkers to classify a patient according to a risk or a condition.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sweeney et al. (hereinafter Sweeney et al. (2016)) in view of Ahn et al. in view of Khatri et al. as applied to claims 1-14 and 16-20 above, and further in view of Sweeney et al. (hereinafter Sweeney et al. (2015)) (Science Translational Medicine, 2015, Vol. 7, No. 287, pp. 1-15, as cited in the Information Disclosure Statement (IDS) received 09 January 2024).
Sweeney et al. (2016) in view of Ahn et al. in view of Khatri et al. as applied to claims 1-14 and 16-20 above, do not show values of the set of features comprises expression values of a first set of genes comprising: CEACAMI1, ZDHHC19, C9orf95, CNA15, BATF, and C3AR1 (claim 15).
Regarding claim 15, Sweeney et al. (2015) shows an 11-gene set that separates SIRS/trauma from sepsis that includes the genes CEACAM1, ZDHHC19, C9ORF95, GNA15, BATF, and C3AR1 (page 5, Table 3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the methods shown by Sweeney et al. (2016) in view of Ahn et al. in view of Khatri et al. as applied to claims 1-14 and 16-20 above, by incorporating a diagnostic gene set that distinguishes sterile inflammation from infectious inflammation as shown by Sweeney et al. (2015) and discussed above. One of ordinary skill in the art would have been motivated to combine the method of Sweeney et al. (2016) in view of Ahn et al. in view of Khatri et al. as applied to claims 1-14 and 16-20 above, with the methods of Sweeney et al. (2015) because Sweeney et al. (2015) explains that although many studies of gene expression in sepsis had been published, distinguishing sepsis form a sterile systemic inflammatory response syndrome (SIRS) was still largely up to clinical suspicion, and therefore the diagnostic 11-gene set of Sweeney et al. (2015) combined with the machine learning classifier shown by Sweeney et al. (2016) in view of Ahn et al. in view of Khatri et al. as applied to claims 1-14 and 16-20 above, would have had a reasonable expectation of success given that they both disclose classification of conditions using expression diagno