DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for priority. The instant application claims benefit of priority to International Application No. PCT/CN2020/094394 filed on 6/4/2020. The claim to the benefit of priority is acknowledged. As such, the effective filing date of claims 1-20 is 6/4/2020.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/2/2022, 9/22/2023, and 1/30/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 abstract ideas without significantly more. The claims recite methods and a CRM for determining a pregnancy status of a pregnant woman. The judicial exception is not integrated into a practical application because while claims 1-20 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine,
manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically methods (Claims 1-12) and a CRM (Claims 13-20)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1, 13, and 19: Constructing a training set and selective validation set, determining predetermined parameters, and constructing the prediction model are processes of comparing/contrasting, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. The validation set and training set being composed of the specified samples, and the parameters comprising the specified information are merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 2 and 8: The pregnancy status comprising a delivery interval is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 3 and 9: The gestational period for sampling being 13 to 25 weeks is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 4 and 10: The prediction model being one of those specified is a verbal articulation of a mathematical process, which is an abstract idea, specifically a mathematical concept.
Claim 5: The parameters comprising the specified list is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 6 and 15: Determining the numerical values of the specified equation is a verbal articulation of a mathematical process, which is an abstract idea, specifically a mathematical concept.
Claims 7, 16, and 20: Determining parameters of the pregnant woman, and determining the pregnancy status are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes. Determining parameters of the pregnant woman, and determining the pregnancy status are verbal articulations of mathematical processes, which are abstract ideas, specifically mathematical concepts. The parameters comprising the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 11 and 18: The parameters comprising the specified list is merely further limiting the data itself which is an abstract idea, specifically a mental process. Determining the numerical values of the specified equation is a verbal articulation of a mathematical process, which is an abstract idea, specifically a mathematical concept.
Claim 12: L being determined by the specified equation is a verbal articulation of a mathematical process, which is an abstract idea, specifically a mathematical concept.
Claims 14 and 17: The pregnancy status comprising a delivery interval and the gestational period for sampling being 13 to 25 weeks are merely further limiting the data itself which are abstract ideas, specifically mental processes. The prediction model being one of those specified is a verbal articulation of a mathematical process, which is an abstract idea, specifically a mathematical concept.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of nonabstract elements:
Claims 13-18: A computer-readable storage medium, computer program, and processor are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MEPE 2106.04(d)(1) and MPEP 2106.05(d)].
Claims 19 and 20: An electronic device, computer-readable storage medium, a program, and processors are generic and nonspecific computer elements that do not improve the functioning of any computer or technology described herein [See MEPE 2106.04(d)(1) and MPEP 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a computer-readable storage medium, computer program, electronic device, and processor are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-10, 13-14, 16-17, and 19-20 are rejected under 35 U.S.C. 102(a)(I) as being anticipated by Ngo et al. (Science (2018) 1133-1136).
Claim 1 is directed to a method for constructing a prediction model for determining a pregnancy status of a woman.
Claim 13 is directed to a CRM which performs the method of claim 1.
Claim 19 is directed to an electronic device with a CRM which performs the method of claim 13 and thus claim 1.
Ngo et al. teaches in the abstract “Noninvasive blood tests that provide information about fetal development and gestational age could potentially improve prenatal care…we found that measurement of nine cell-free RNA (cfRNA) transcripts in maternal blood predicted gestational age with comparable accuracy to ultrasound but at substantially lower cost. In a related study of 38 women (25 full-term and 13 preterm deliveries), all at elevated risk of delivering preterm, we identified seven cfRNA transcripts that accurately classified women who delivered preterm up to 2 months in advance of labor”, on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery, using cfRNA measurements as the primary features. We trained and validated this model using data from the Danish cohort from 21women (n = 306 blood samples) for training, and from 10 women (n = 215 blood samples) for validation”, and on supplemental page 5, paragraph 6 the code used to process the data as well as the data itself are available on GitHub showing it was implemented on a computer, thereby reading on a method for constructing a prediction model for determining a pregnancy status of a pregnant woman, the method comprising: (i) constructing a training set and a selective validation set, each of the training set and the selective validation set being composed of a plurality of pregnant women samples each having a known pregnancy status; (ii) determining predetermined parameters of each pregnant woman sample in the training set, the predetermined parameters comprising a concentration of fetal cell-free nucleic acids in peripheral blood of the pregnant woman sample and a gestational age in week at which sampling for the peripheral blood of the pregnant woman sample is conducted; and (iii) constructing the prediction model based on the known pregnancy status and the predetermined parameters.
Claim 2 is directed to the method of claim 1 but further specifies that the pregnancy status comprise a delivery interval.
Ngo et al. teaches on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery…”, reading on wherein the pregnancy status comprises a delivery interval of the pregnant woman.
Claim 3 is directed to the method of claim 1 but further specifies that the gestational age of sampling is between 13 and 25.
Ngo et al. teaches in Table 1 “Gestational age was estimated using cfRNA measurements from the second (T2), third (T3), or both (T2 and T3) trimesters”, the second trimester is weeks 13-26 and thereby reads on wherein the gestational age in week at which the sampling is conducted is 13 to 25 weeks.
Claim 4 is directed to the method of claim 1 but further specifies that the prediction model be one of those specified.
Ngo et al. teaches on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery, using cfRNA measurements as the primary features. We trained and validated this model using data from the Danish cohort from 21women (n = 306 blood samples) for training, and from 10 women (n = 215 blood samples) for validation”, reading on wherein the prediction model is at least one of a linear regression model, a logistic regression model, or a random forest.
Claim 7 is directed to a method of determining a pregnancy status by determining parameters and then determining the pregnancy status using the prediction model of claim 1.
Claim 16 is directed to a CRM which performs the method of claim 7.
Claim 20 is directed to an electronic device with a CRM which performs the method of claim 16 and thus claim 7.
Ngo et al. teaches in the abstract “Noninvasive blood tests that provide information about fetal development and gestational age could potentially improve prenatal care…we found that measurement of nine cell-free RNA (cfRNA) transcripts in maternal blood predicted gestational age with comparable accuracy to ultrasound but at substantially lower cost. In a related study of 38 women (25 full-term and 13 preterm deliveries), all at elevated risk of delivering preterm, we identified seven cfRNA transcripts that accurately classified women who delivered preterm up to 2 months in advance of labor”, on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery, using cfRNA measurements as the primary features. We trained and validated thismodel using data from the Danish cohort from 21women (n = 306 blood samples) for training, and from 10 women (n = 215 blood samples) for validation”, on supplemental page 4, paragraph 1 “Recursive feature selection (a modification of best subset selection) and model training were performed in parallel in R using the caret package”, and on supplemental page 5, paragraph 6 the code used to process the data as well as the data itself are available on GitHub showing it was implemented on a computer, thereby reading on a method for determining a pregnancy status of a pregnant woman, comprising: (1) determining predetermined parameters of the pregnant woman, the predetermined parameters comprising a concentration of fetal cell-free nucleic acids in peripheral blood of the pregnant woman and a gestational age in week at which sampling for the peripheral blood of the pregnant woman is conducted; and (2) determining the pregnancy status of the pregnant woman based on the predetermined parameters and the prediction model constructed by the method according to claim 1.
Claim 8 is directed to the method of claim 7 but further specifies that the pregnancy status comprise a delivery interval.
Ngo et al. teaches on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery…”, reading on wherein the pregnancy status comprises a delivery interval of the pregnant woman.
Claim 9 is directed to the method of claim 8 and thus claim 7, but further specifies that the gestational age of sampling is between 13 and 25.
Ngo et al. teaches in Table 1 “Gestational age was estimated using cfRNA measurements from the second (T2), third (T3), or both (T2 and T3) trimesters”, the second trimester is weeks 13-26 and thereby reads on wherein the gestational age in week at which the sampling is conducted is 13 to 25 weeks.
Claim 10 is directed to the method of claim 8 and thus claim 7, but further specifies that the prediction model be one of those specified.
Ngo et al. teaches on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery, using cfRNA measurements as the primary features. We trained and validated this model using data from the Danish cohort from 21women (n = 306 blood samples) for training, and from 10 women (n = 215 blood samples) for validation”, reading on wherein the prediction model is at least one of a linear regression model, a logistic regression model, or a random forest.
Claim 14 is directed to the CRM of claim 13 but further specifies that the pregnancy status comprise a delivery interval, that the gestational age of sampling is between 13 and 25, or that the prediction model be one of those specified.
Claim 17 is directed to the CRM of claim 16 but further specifies that the pregnancy status comprise a delivery interval, that the gestational age of sampling is between 13 and 25, or that the prediction model be one of those specified.
Ngo et al. teaches on page 1, column 3, paragraph 3 “We then built a random forest model to predict time from sample collection until delivery, using cfRNA measurements as the primary features. We trained and validated this model using data from the Danish cohort from 21women (n = 306 blood samples) for training, and from 10 women (n = 215 blood samples) for validation”, and in Table 1 “Gestational age was estimated using cfRNA measurements from the second (T2), third (T3), or both (T2 and T3) trimesters”, thereby reading on wherein the method further satisfies any one or more of the following conditions: the pregnancy status comprises a delivery interval of the pregnant woman; the gestational age in week at which the sampling is conducted is 13 to 25 weeks; or the prediction model is at least one of a linear regression model, a logistic regression model, or a random forest.
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.
Claims 5-6, 11-12, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ngo et al. (Science (2018) 1133-1136) as applied to claims 1-4, 7-10, 13-14, 16-17, and 19-20 above, and further in view of De Silva et al. (BMC pregnancy and childbirth (2017) 1-10).
Claim 5 is directed to the method of claim 4 and thus claim 1, but further specifies parameters to include any from the specified list.
Ngo et al. teaches the methods and CRM of claims 1-4, 7-10, 13-14, 16-17, and 19-20 as previously described.
Ngo et al. does not teach determining the values of the parameters set forth in the regression equation set forth.
De Silva et al. teaches in the abstract “We modelled the probability of delivery within 7 days of admission to hospital among women presenting with threatened preterm birth, using routinely collected clinical characteristics…Logistic regression was undertaken to create a predictive model that was assessed for its calibration capacity, stratification ability, and classification accuracy (ROC curve)… Significant predictors of early delivery included maternal age, parity, gestational age at admission, smoking, preterm labour, prolapsed membranes, preterm pre-labour rupture of membranes, and antepartum haemorrhage… We propose a useful tool to improve prediction of delivery within 7 days after admission among women with threatened preterm birth. This information is important for optimal corticosteroid treatment”, reading on wherein the predetermined parameters further comprise a height, a body weight, and/or an age of the pregnant woman sample.
It would have been obvious at the time of first filing to have modified the teachings of Ngo et al. for the method of claims 1-4, with the teachings of De Silva et al. for the use maternal age in predicting timing of delivery as the latter teaches in the abstract “The area under the ROC curve was 0.724 (95% CI 0.706–0.742)” and on page 7, column 2, paragraph 2 ” The model had a fair predictive accuracy (AUC of 0.73), good calibration capacity and stratification ability, and it was internally validated using a bootstrap method. Although the predictive accuracy of our model was recognized to be fair, it is based on information collected in routine clinical care and it would significantly improve optimal steroid treatment on admission, (from 47.9% to 62.1% in our cohort) by decreasing the number of women who do not but should have received steroids (false negative rate)”. One would have had a reasonable expectation of success given that both papers are directed to predicting delivery timing with only the model framework slightly changing from a random forest in the first to a logistic regression in the second, with both using similar data. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 6 is directed to the method of claim 1 but further specifies determining the values of the parameters set forth in the regression equation set forth.
Claim 11 is directed to the method of claim 10 and thus claim 7, but further specifies determining the values of the parameters set forth in the regression equation set forth.
Claim 15 is directed to the method of claim 13 and thus claim 1, but further specifies determining the values of the parameters set forth in the regression equation set forth.
Claim 18 is directed to the method of claim 16 and thus claim 7, but further specifies determining the values of the parameters set forth in the regression equation set forth.
Ngo et al. teaches the methods and CRM of claims 1-4, 7-10, 13-14, 16-17, and 19-20 as previously described.
Ngo et al. does not teach determining the values of the parameters set forth in the regression equation set forth.
De Silva et al. teaches in the abstract “We modelled the probability of delivery within 7 days of admission to hospital among women presenting with threatened preterm birth, using routinely collected clinical characteristics…Logistic regression was undertaken to create a predictive model that was assessed for its calibration capacity, stratification ability, and classification accuracy (ROC curve)… Significant predictors of early delivery included maternal age, parity, gestational age at admission, smoking, preterm labour, prolapsed membranes, preterm pre-labour rupture of membranes, and antepartum haemorrhage… We propose a useful tool to improve prediction of delivery within 7 days after admission among women with threatened preterm birth. This information is important for optimal corticosteroid treatment”, reading on determining, by using the training set and the selective validation set, numerical values of β0, βicff,βisample,βiheight,βiweight,βiage,andεi for the following formula: li = β0 + βicffxicff + βisamplexisample + βiheightxiheight + βiweightxiweight + βiagexiage + εi, where i=1,…,p, wherein i represents a serial number of the pregnant woman sample in the training set; li is a value determined for the known pregnancy status of the pregnant woman sample No.i, wherein li is 1 for the pregnant woman sample with premature delivery, and li is 0 for the pregnant woman sample with full-term delivery; xicff represents the concentration of fetal cell-free nucleic acids for the pregnant woman sample No.i; xi sample represents the gestational age in week at which the sampling for the peripheral blood of the pregnant woman sample No.i is conducted; xi height represents a height of the pregnant woman sample No.i; xi weight represents a body weight of the pregnant woman sample No.i; xi age represents an age of the pregnant woman sample No.i; and εi represents a sequencing error of the peripheral blood of the pregnant woman sample No.i.
Claim 12 is directed to the method of claim 11 but further specifies determining the output of the logistic equation set forth.
Ngo et al. teaches the methods and CRM of claims 1-4, 7-10, 13-14, 16-17, and 19-20 as previously described.
Ngo et al. does not teach determining the output of the logistic equation set forth.
De Silva et al. teaches in the abstract “We modelled the probability of delivery within 7 days of admission to hospital among women presenting with threatened preterm birth, using routinely collected clinical characteristics…Logistic regression was undertaken to create a predictive model that was assessed for its calibration capacity, stratification ability, and classification accuracy (ROC curve)… Significant predictors of early delivery included maternal age, parity, gestational age at admission, smoking, preterm labour, prolapsed membranes, preterm pre-labour rupture of membranes, and antepartum haemorrhage… We propose a useful tool to improve prediction of delivery within 7 days after admission among women with threatened preterm birth. This information is important for optimal corticosteroid treatment”, and it is inherent to regressions that a transformation function is used, specifically the logistic function, to take a continuous variable and map it to an output between 0 and 1, thereby reading on wherein l is determined based on the following formula: l = log bp1 - p , wherein, b is a base number of log and is generally a constant e; and p is the probability of premature delivery of the pregnant woman.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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