DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-16 are pending and are examined on the merits.
Claims 1-16 are rejected.
Priority
Acknowledgment is made of a claim for foreign priority to JP2020-172934 filed 10/14/2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
As recorded on the 08/03//2023 filing receipt, the instant application claims the benefit of priority to JP2020-172934 filed 10/14/2020. Accordingly, the effective filing date of the claimed invention is 10/14/2020. At this point in examination, all claims have been interpreted as being accorded this priority date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statements (IDS’s) submitted on 04/12/2023 and 11/25/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner. A signed copy of the corresponding 1449 form has been included with this Office action.
Drawings
The drawings filed 04/12/2023 are accepted.
Specification
The amendments to the specification filed 04/12/2023 have been accepted.
Claim rejection - 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.
Claim 13 is 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.
Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate.
Claim 13 recites a probability that a class i (1 ≤ I ≤ N) is true (true "class i" rate) and a probability that the class i is false (false "class i" rate) …” in lines 6 ad 7. The relationship is unclear between the recited "class", “true”, and “false” and the parenthetical at least because it is unclear whether the parenthetical is merely exemplary or is intended as a definition of the "class", “true”, and “false”. As such, rendering the claims indefinite.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Matter belonging to no statutory category
Claim 16 is rejected under 35 USC 101 because the claimed invention is directed to non-statutory subject matter.
Claim 16 is to "a program," which is not, in all embodiments within a BRI, interpreted as belonging to any category listed in 101. In a BRI, the claim reads on data and/or software comprising no structure other than data and/or software. The claim is not recited as a process, and the claim is not limited to any particular structure as a 101 machine or manufacture. The claim reads on transitory propagating signals which are not proper patentable subject matter because it does not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006).
None of the dependent claims remedy this rejection.
Judicial exceptions (JE) to 101 patentability
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 1-15 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
The instantly claimed invention (claim(s) 16 being representative) is directed to a computer program and does not fall within any statutory category [Step 1: NO] and thus is non-statutory, warranting a rejection for failure to claim statutory subject matter.
Claims 1-15 fall within one or more 101 statutory categories of invention, however claim 16 do not, and those claims are separately rejected above.
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim(s) 1-16 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 1 recites evaluating a likelihood that a subject belongs to a group for a classification attribute having a binary classification; the limitation evaluating a likelihood, given the plain meaning of evaluating, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of evaluating a likelihood.
Claim 1 further recites receiving a subject score for an observation value of the subject; the limitation receiving a score is considered a mental process, since human mind is capable of receiving/getting information. A claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). See 2106.04MPEP III. A.
Claim 1 further acquiring sensitivity and specificity of the subject score with the subject score as a parameter, by using a relational expression established between the sensitivity and the specificity with a score for the observation value as a parameter; the limitation acquiring sensitivity and specificity by using a relational expression is considered mathematical calculation, and as such, falls into mathematical concepts groupings of abstract ideas.
Claim 1 further recites acquiring a prior probability of an attribute of the subject; the limitation acquiring a probability is considered a mathematical calculation, as disclosed in instant specification [0027]: “…the probability (likelihood) that the subject actually belongs to a group (class) may be calculated using Bayesian statistics. The likelihood may be calculated using a conditional probability calculation formula in Bayesian statistics, including the prior probability”. As such, said limitation falls into mathematical concepts groupings of abstract ideas.
Claim 1 furhter recites acquiring a likelihood of belonging to a classification attribute specific to the subject based on the sensitivity, the specificity, and the prior probability of the subject; the limitation acquiring a likelihood based on specificity and prior probability is considered a mathematical calculation, and as such, falls into mathematical concepts groupings of abstract ideas.
Claim 2 recites acquiring a modified positive predictive value or a modified negative predictive value, respectively, for the subject score; the limitations acquiring a positive predictive value is considered a mathematical calculation, and as such, falls into mathematical concepts groupings of abstract ideas.
Claim 3 recites evaluating a likelihood that a subject is positive or negative for a clinical examination having a binary classification; the limitation evaluating a likelihood, given the plain meaning of evaluating, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of evaluating a likelihood.
Claim 3 further recites receiving a subject score for a clinical examination of the subject (mental process of receiving/getting data).
Claim 3 further recites acquiring sensitivity and specificity of the subject score, with the subject score as the parameter (mathematical calculation/ mathematical concepts).
Claim 3 further recites acquiring prevalence of an attribute of the subject (mathematical calculation/ mathematical concepts).
Claim 3 further recites acquiring a likelihood that the subject is positive or negative based on the sensitivity, the specificity, and the prevalence of the subject (mathematical calculation/ mathematical concepts).
Claim 4 recites acquiring a modified positive predictive value or a modified negative predictive value, respectively, for the subject score (mathematical calculation/ mathematical concepts).
Claim 13 recites evaluating a likelihood that a subject belongs to a class for a classification attribute having an N-class classification; the limitation evaluating a likelihood, given the plain meaning of evaluating, encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of evaluating a likelihood.
Claim 13 furhter recites receiving a subject score for an observation value of the subject (mental process of receiving/getting data).
Claim 13 further recites acquiring, in an N-class classification obtained for a score for the observation value, a probability that a class i (1 i N) is true (true "class i" rate) and a probability that the class i is false (false "class i" rate) of the subject score with the subject score as a parameter, by using a relational expression established between the true "class i" rate and the false "class i" rate, with the score as a parameter (mathematical calculation/ mathematical concepts).
Claim 13 further recites acquiring a prior probability of an attribute of the subject (mathematical calculation/ mathematical concepts).
Claim 13 further recites acquiring a likelihood of belonging to the class i specific to the subject based on the true "class i" rate, the false "class i" rate, and the prior probability of the subject (mathematical calculation/ mathematical concepts).
Claim 14 recites acquiring a conditional probability value of the subject score based on Bayesian statistics (mathematical calculation/ mathematical concepts).
Claim 15 recites acquiring a true class i predictive value or a false class i predictive value for the subject score (mathematical calculation/ mathematical concepts).
Claims 5-12 provides additional information about the recited abstract ideas.
The identified claims recite a law of nature, a natural phenomenon (product of nature) or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claim(s) 1-16 include the following. The remaining claims do not recite any additional elements.
Claim 16 recites a program for causing a computer to execute the method.
The additional element of a program for causing a computer to execute the method are generic computer components and/or processes. There are no limitations that indicate that the program or a computer require anything other than generic computing systems. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application.
MPEP 2106.04(d). I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, Prong 1 above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, Prong 2, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination, it is not yet the case that any of the Step 2A, Prong 2 considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
In conclusion regarding Prong 2, claims 1-16 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The additional elements of claim(s) 1-16 include the following. The remaining claims do not recite any additional elements.
Claim 16 recites a program for causing a computer to execute the method.
The additional element of a program for causing a computer to execute the method are conventional computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 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 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bossuyt (Clinical performance characteristics of a laboratory test. A practical approach in the autoimmune laboratory, Autoimmunity Reviews 8 (2009) 543–548; as cited in in the 11/25/2024 IDS form) in view of Webb (Bayes’ formula: a powerful but counterintuitive tool for medical decision-making, BJA Education, 20(6): 208e213(2020); as cited in the attached 892 form)
Regarding claim 1, Bossuyt teaches that the likelihood ratio of a specific test result for a disease (for example, a method of evaluating a likelihood) is the likelihood of the test result in diseased individuals divided by the likelihood of the test result in diseased control individuals. Bossuyt further discloses that the likelihood ratios of anti-CCP for RA and disease controls are illustrated in Fig. 2. Bossuyt further discloses post-test probability as a function of pre-test probability and test results (for example, a priori probability) (fig. 2). Bossuyt further discloses that the positive likelihood ratio corresponds to sensitivity / [1 − specificity] and the negative likelihood ratio corresponds to specificity / [1 − sensitivity] (for example, acquiring sensitivity and specificity). Likelihood ratios can be used to calculate post-test probabilities using the following formula (which is a variant of Bayes' theorem): Post-test odds = pre-test odds × likelihood ratio: P(DlT)/P(NDlT)=P(D)/P(ND)⁎P(TlD)/P(TlND) (for example, a relational expression between sensitivity and specificity). Bossuyt further teaches calculation of post-test probabilities based on Bayes rule (pg. 546, col. 1, para. 2; Fig. 1); Reading on limitations of a method for evaluating a likelihood that a subject belongs to a group for a classification attribute having a binary classification; acquiring sensitivity and specificity of the subject score with the subject score as a parameter, by using a relational expression established between the sensitivity and the specificity with a score for the observation value as a parameter; acquiring a prior probability of an attribute of the subject; and acquiring a likelihood of belonging to a classification attribute specific to the subject based on the sensitivity, the specificity, and the prior probability of the subject.
Further regarding claim 1, Boosuyt does not teach receiving a subject score from an observation and acquiring sensitivity and specificity with the score, Webb teaches applying Bayes’ formula to disease testing. Webb further teaches calculating the probability of P(D+|T+), the probability of having the disease given a positive test, and having the disease given a positive test, and P(D-|T-), the probability of not having the disease given a negative test. P(D+|T+) is called the positive predictive value (PPV) and P(D-|T-) is called the negative predictive value (NPV) (pg. 209, col. 2, para. 2). Webb further teaches calculating the probability of having the disease given a positive and negative test by acquiring specificity and sensitivity and calculating a prior probability/prevalence (pg. 210, cols. 1and 2, equations (7-9)). Webb further discloses that the ‘prior probability’ is the disease prevalence (pg. 208, key points).
Regarding claim 2, Bossuyt discloses that the positive and negative predictive values depend on the prevalence of the disease in a defined population. The predictive values can be calculated based on the disease prevalence in a population and the likelihoods of the assay (p. 546, col. 2, para. 3); Webb discloses that the PPV is adjusted PPV in equation 7 (pg. 210, cl. 1); reading on limitations of acquiring a modified positive predictive value or a modified negative predictive value, respectively, for the subject score.
Regarding claim 3, Bossuyt teaches that the likelihood ratio of a specific test result for a disease (for example, a method of evaluating a likelihood) is the likelihood of the test result in diseased individuals divided by the likelihood of the test result in diseased control individuals. Bossuyt further discloses that the likelihood ratios of anti-CCP for RA and disease controls are illustrated in Fig. 2. Bossuyt further discloses post-test probability as a function of pre-test probability and test results (for example, a priori probability) (fig. 2). Bossuyt further discloses that the positive likelihood ratio corresponds to sensitivity / [1 − specificity] and the negative likelihood ratio corresponds to specificity / [1 − sensitivity] (for example, acquiring sensitivity and specificity). Likelihood ratios can be used to calculate post-test probabilities using the following formula (which is a variant of Bayes' theorem): Post-test odds = pre-test odds × likelihood ratio: P(DlT)/P(NDlT)=P(D)/P(ND)⁎P(TlD)/P(TlND) (for example, a relational expression between sensitivity and specificity). Bossuyt further teaches calculation of post-test probabilities based on Bayes rule (pg. 546, col. 1, para. 2; Fig. 1); Reading on limitations of a method for evaluating a likelihood that a subject belongs to a group for a classification attribute having a binary classification; by using a relational expression established between sensitivity and specificity with a score for the clinical examination as a parameter. acquiring sensitivity and specificity of the subject score, with the subject score as the parameter; acquiring prevalence of an attribute of the subject; and acquiring a likelihood that the subject is positive or negative based on the sensitivity, the specificity, and the prevalence of the subject.
Further regarding claim 3, Boosuyt does not teach receiving a subject score from an observation and acquiring sensitivity and specificity with the score, Webb teaches applying Bayes’ formula to disease testing. Webb further teaches calculating the probability of P(D+|T+), the probability of having the disease given a positive test, and having the disease given a positive test, and P(D-|T-), the probability of not having the disease given a negative test. P(D+|T+) is called the positive predictive value (PPV) and P(D-|T-) is called the negative predictive value (NPV) (pg. 209, col. 2, para. 2). Webb further teaches calculating the probability of having the disease given a positive and negative test by acquiring specificity and sensitivity and calculating a prior probability/prevalence (pg. 210, cols. 1and 2, equations (7-9)). Webb further discloses that the ‘prior probability’ is the disease prevalence (pg. 208, key points).
Regarding claim 4, Bossuyt discloses that the positive and negative predictive values depend on the prevalence of the disease in a defined population. The predictive values can be calculated based on the disease prevalence in a population and the likelihoods of the assay (p. 546, col. 2, para. 3); Webb discloses that the PPV is adjusted PPV in equation 7 (pg. 210, cl. 1); reading on limitations off acquiring a modified positive predictive value or a modified negative predictive value, respectively, for the subject score.
Regarding claim 5, Boosuyt discloses that the test is a laboratory test (pg. 545, col. 1, para. 1). Webb discloses a blood test to diagnose to diagnose heparin-induced thrombocytopenia (pg. 211, col. 1, last two para.); reading on limitations of wherein the clinical examination is a biological examination.
Regarding claim 13, Webb teaches calculating the probability of having the disease given a positive and negative test by acquiring specificity and sensitivity and calculating a prior probability/prevalence (pg. 210, cols. 1and 2, equations (7-9)). Webb further discloses that the ‘prior probability’ is the disease prevalence (pg. 208, key points).
Webb further teaches calculating a posterior probability using Bayes’ theorem, which adjusts the prior probability based on the test’s true or false class rate, With the Bayesian approach, data(samples) are obtained and used to update a prior probability with a posterior probability. Typically, the prior probabilities of two competing hypotheses (H1 andH0) (for example, N-class, where N=2) are compared using the odds ratio form of Bayes’ formula (pg. 212, col. 2, para. 1 and 2).
Webb further teaches applying Bayes’ formula to hypothesis testing and further teaches that as a conditional probability, a P-value is P(data|H0), the probability of the observed outcome (i.e. the data) given the null hypothesis is true. P-values are not P(H0|data), the probability the null hypothesis is true (i.e. the study hypothesis is false) given the observed outcome (pg. 211, col. 2, last para.).
Webb further discloses that Alpha is the probability of a positive test (i.e. P<= 0.05) given the null hypothesis is true, which as a conditional probability is P(T+|H0). Alpha is also the probability of a type I statistical error (pg. 212, col. 1, para. 2); reading on limitations of a method for evaluating a likelihood that a subject belongs to a class for a classification attribute having an N-class classification, the method comprising: receiving a subject score for an observation value of the subject; acquiring, in an N-class classification obtained for a score for the observation value, a probability that a class i is true and a probability that the class i is false of the subject score with the subject score as a parameter, by using a relational expression established between the true "class i" rate and the false "class i" rate, with the score as a parameter; acquiring a prior probability of an attribute of the subject; and acquiring a likelihood of belonging to the class i specific to the subject based on the true "class i" rate, the false "class i" rate, and the prior probability of the subject.
Regarding claim 14, Webb discloses acquiring a conditional probability value of the subject score based on Bayesian statistics (pgs. 211-212; subsection: Applying Bayes’ formula to hypothesis testing; last para.); reading on limitations of acquiring a conditional probability value of the subject score based on Bayesian statistics.
Regarding claim 15, Webb discloses updating a prior probability with a posterior probability, where the ‘posterior probability’ is the positive predictive value (pg. 212, col. 2, para. 1; pg. 208, key points: forth bullet); reading on limitations of wherein the acquiring of a likelihood of belonging to the class i specific to the subject based on the true "class i" rate, the false "class i" rate, and the prior probability of the subject comprises acquiring a true class i predictive value or a false class i predictive value for the subject score.
Rationale for combining Bossuyt and Webb
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Bossuyt and Webb, the examiner concludes that this combination represents the use of known techniques to improve similar methods. Both Bossuyt and Webb are directed to statistical analysis of test results. Bossuyt disclosed acquiring sensitivity and specificity from discrete intervals. In the same field of research, Webb provided receiving a subject score from an observation and acquiring sensitivity and specificity. Combining the statistical analysis of determining a post-test probability of Bossuyt with the with the pre-test probability of Webb, as disclosed by Webb, would have allowed for calculating the true probability of a subject belonging to a classification. This combination would have been expected to have provided a more meaningful interpretation of the results. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bossuyt in view of Webb, as applied o claims 1-5 and 13-15 above, and further in view of Singh (US20210285042A1; as cited in the attached 892 form).
Claims 6-10 depends from claims 1 and 3 with extra limitations liquid biopsy examination of urine or blood and RNA examination. Limitations of claims 1 and 3 have been taught in the above rejections.
Regarding claims 6-10, Boosuyt discloses that the test is a laboratory test (pg. 545, col. 1, para. 1). Webb discloses a blood test to diagnose to diagnose heparin-induced thrombocytopenia (pg. 211, col. 1, last two para.).
Boosuyt and Webb do not expressly teach that the examination is a liquid biopsy from urine or blood, and clinical examination is a genetic examination, where the genetic information is RNA by examining a gene from urine.
Singh teaches an allelic variant calling method using a prior genotype probability at the allelic position, where likelihoods are computed using a combination of these conditional probabilities and the prior genotype probability... From this, a determination is made as to whether the likelihoods support a variant call at the allelic position, where a particular sensitivity and/or specificity, and their relative usefulness as a diagnostic tool can be measured using ROC-AUC statistics (abstract) [0110] [0060] [0062]. Singh further discloses that the classification is a binary classification [0085]. Singh furhter discloses that the first biological sample is a liquid biological sample (e.g., of the test subject) and each respective nucleic acid fragment sequence in the first plurality of nucleic acid fragment sequences represents all or a portion of a respective cell-free nucleic acid molecule in a population of cell-free nucleic acid molecules in the liquid biological sample. For instance, in some embodiments, the first biological sample comprises or consists of blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal, saliva, sweat, tears, pleural fluid, pericardial fluid, or peritoneal fluid of the subject. In such embodiments, the first biological sample may include the blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal, saliva, sweat, tears, pleural fluid, pericardial fluid, or peritoneal fluid of the subject as well as other components (e.g., solid tissues, etc.) of the subject [0117] and that the terms “nucleic acid” and “nucleic acid molecule refers to ribonucleic acid (RNA, e.g., message RNA (mRNA), short inhibitory RNA (siRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), microRNA, RNA highly expressed by the fetus or placenta, and the like), and/or DNA or RNA analogs (e.g., containing base analogs, sugar analogs and/or a non-native backbone and the like), RNA/DNA hybrids [0064]. Singh further discloses a gene panel to analyze specific regions of the genome [0232]. Singh further discloses using whole-exome sequencing 0232].
Rationale for combining Bossuyt, Webb, and Singh
Applying the KSR standard to Bossuyt, Webb, and Singh, the examiner concludes that this combination represents applying known technique to a known method. Bossuyt, Webb, and Singh are directed to statistical analysis of test results. Bossuyt and Webb disclosed acquiring sensitivity and specificity of a subject score from an observation value. In the same field of research, Singh provided calculating specificity, and sensitivity and a prior probability for different types of examination. Combining the known statistical analysis of determining a post-test probability of Bossuyt and Webb with the with the known examination settings of Singh would have allowed for calculating the true probability of a subject belonging to a classification. This combination would have been expected to have provided a more meaningful interpretation of various type of results. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bossuyt in view of Webb, as applied o claims 1-5 and 13-15 above, and further in view of Bahri (WO 2026035956 A1; as cited in the attached 892 form).
Claims 11 and 12 depends from claims 1, 3, and 8 with extra limitations of genetic examination comprises examining a nucleic acid contained in an exosome derived from urine. Limitations of claims 1, 3 and 8 have been taught in the above rejections.
Regarding claims 11 and 12, Bahri teaches a binary classification predictive model that provides a likelihood of belonging to a classification, where the biological sample is from urine [0042-0043] [ [0144], where the sample may be selected based on predetermined proteins associated with extracellular exosomes [0157]. Bahri furhter teaches evaluating the performance of the model based on the one or more of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) metrics, and area under the ROC curve (AUC) [0197]; reading on limitations of wherein the genetic examination comprises examining a nucleic acid contained in an exosome derived from urine.
Rationale for combining Bossuyt, Webb, and Bahri
Applying the KSR standard to Bossuyt, Webb, and Bahri, the examiner concludes that this combination represents applying known technique to a known method. Bossuyt, Webb, and Bahri are directed to statistical analysis of test results. Bossuyt and Webb disclosed acquiring sensitivity and specificity of a subject score from an observation value. In the same field of research, Bahri provided calculating specificity, and sensitivity and a prior probability for exosomal genetic examination from urine of the subject. Combining the known statistical analysis of determining a post-test probability of Bossuyt and Webb with the with the known examination settings of Bahri would have allowed for calculating the true probability of a subject belonging to a classification. This combination would have been expected to have provided a more meaningful interpretation of various type of results. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Bossuyt in view of Webb, as applied o claims 1-5 and 13-15 above, and further in view of Brownlee (A Gentle Introduction to Bayes Theorem for Machine Learning, Machine Learning Mastery; htpps://machinelearningmastery.com/bayes-theorem-for-machine-learning/ December 4, 2019, pages: 43; as cited in the attached 892 form).
Claim 16 depends from claim1 with extra limitations of a program for causing a computer to execute the method steps of claim 1. Limitations of claim 1 have been taught in the above rejections.
Regarding claim 16, Bossuyt and Webb do not expressly teach a program for causing a computer to execute the method steps of claim 1. It is important to note that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. See MPEP 2144.04. Nevertheless, Brownlee teaches using Bayes theorem for machine learning, which inherently discloses a program to cause computer to execute the method steps. Brownlee further teaches using joint and conditional probabilities (pg. 2), posterior probabilities (pg. 3) and gives a diagnostic test scenario (pg. 4) of two (or more) simultaneous events using machine learning; reading on limitations of a program for causing a computer to execute the method according to claim 1.
Rationale for combining Bossuyt, Webb, and Brownlee
Applying the KSR standard to Bossuyt, Webb, and Brownlee, the examiner concludes that this combination represents applying known technique to a known method. Bossuyt, Webb, and Brownlee are directed to statistical analysis of test results. Bossuyt and Webb disclosed acquiring sensitivity and specificity of a subject score from an observation value. In the same field of research, Brownlee provided calculating specificity, and sensitivity and a prior probability in a machine learning setting, which inherently discloses a program to execute the process steps. Combining the known statistical analysis of determining a post-test probability of Bossuyt and Webb with the with the known computer settings of Brownlee would have allowed for calculating the true probability of a subject belonging to a classification. This combination would have been expected to have provided a more meaningful interpretation of various type of results. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Prior art made of record and not relied upon
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Moskowitsz, ("Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes." Biostatistics 5, no. 1 (2004): 113-127; as cited in the 04/12/2023 IDS form). Moskowitsz teaches a method of evaluating a likelihood that a subject belongs to a binary outcome (abstract; pg. 4, para. 1). Moskovitz further teaches estimating the relative predictive probability to examine the accuracy of the test (pg. 6, last para.)
Conclusion
No claims are allowed.
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/G.S./Examiner, Art Unit 1686
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686
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