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 .
Status of Claims
Claims 21-40 are pending.
This communication is in response to the communication filed December 16, 2025.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12,462,935. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims recite the limitations of the patented claims. The pending dependent claims are rejected for their dependency on indefinite claims.
Pending claim 21 recites:
Patented claim 1 recites:
A method, comprising.(a) amplifying at least a portion of circulating cell-free nucleic acid molecules of a sample obtained from a subject to generate amplified cell-free nucleic acid molecules;
A method comprising:…b) amplifying at least a portion of the circulating cell-free nucleic acid molecules to generate amplified cell-free nucleic acid molecules;
(b) sequencing at least a portion of the amplified cell-free nucleic acid molecules to produce a set of sequencing reads;
c) sequencing at least a portion of the amplified cell-free nucleic acid molecules to produce a set of sequencing reads;
(c) processing the set of sequencing reads to generate a probability value using a machine learning algorithm, wherein the probability value indicates a presence or absence of a plurality of nucleic acid methylation markers in the set of sequencing reads, and
d) processing each sequencing read in the set of sequencing reads to generate one or more probability values using a machine learning algorithm…wherein the one or more probability values represents a probability that the set of sequencing reads corresponds to a particular class of the different classes;
wherein the machine learning algorithm is trained using an input data set comprising one or more nucleic acid methylation markers of one or more sets of sequencing reads from at least one healthy subject and at least one diseased subject; and
wherein the machine learning algorithm is trained using an input data set comprising one or more sets of sequencing reads from subjects of different classes, and
(d) detecting a presence of a disease of the subject when the probability value exceeds a threshold value of the machine learning algorithm indicating the presence of the disease of the subject.
f) detecting a presence or an absence of at least one genetic abnormality in the subject, or fetus if the subject is pregnant, based on the classifying of e).
Pending claim 22 recites: wherein the processing does not comprise alignment of the set of sequence reads to a reference genome or reference sequence
Patented claim 2 recites: wherein the processing of d) does not include alignment of the set of sequencing reads to a reference genome or reference sequence.
Pending claim 23 recites: wherein the circulating cell-free nucleic acid molecules comprise cell-free nucleic acid molecules from a tumor.
Patented claim 3 recites:
Pending claim 24 recites: wherein the sample comprises blood or urine.
Patented claim 13 recites: wherein the biological sample is blood, plasma, serum, urine, interstitial fluid, vaginal cells, vaginal fluid, buccal cells, or saliva.
Pending claim 25 recites: wherein the blood comprises venous blood.
Patented claim 13 recites: wherein the biological sample is blood, plasma, serum, urine, interstitial fluid, vaginal cells, vaginal fluid, buccal cells, or saliva.
Pending claim 26 recites: wherein the machine learning algorithm is a deep learning algorithm.
Patented claim 4 recites: wherein the machine learning algorithm is a deep learning algorithm.
Pending claim 27 recites: wherein the deep learning algorithm comprises a feedforward neural network, a convolutional neural network, or a recurrent neural network.
Patented claim 5 recites: wherein the deep learning algorithm comprises a feedforward neural network, a convolutional neural network, or a recurrent neural network.
Pending claim 30 recites: wherein the input data set resides in a cloud-based database that is periodically or continuously updated with sets of sequencing reads, input data sets, and previously performed deep learning analysis results that are generated locally or remotely.
Patented claim 11 recites: wherein the input data set resides in a cloud-based database that is periodically or continuously updated with sets of sequencing reads, input data sets, and previously-performed deep learning analysis results that are generated locally or remotely
Pending claim 31 recites: wherein the input data set comprises simulated sequence data for healthy subjects, diseased subjects, or a combination thereof, and wherein the input data set further comprises values corresponding to personal health data for the at least one healthy subject and the at least one diseased subject, wherein the personal health data comprises the subjects' age, sex, weight, blood pressure, ultrasound markers, biochemical screening results, smoking history, history of alcohol use, family history of disease, or any combination thereof.
Patented claim 9 recites: wherein the input data set comprises personal health data for one or more control subjects, wherein the personal health data is selected from the group consisting of subject age, gestational age, sex, weight, blood pressure, number of previous offspring (if female), ultrasound markers, biochemical screening results, smoking history, history of alcohol use, family history of disease, or any combination thereof.
Pending claim 32 recites: wherein the input data set further comprises personal health data for the at least one healthy subject and the at least one diseased subject, wherein the personal health data comprises the subjects' age, sex, weight, blood pressure, ultrasound markers, biochemical screening results, smoking history, history of alcohol use, family history of disease, or any combination thereof.
Patented claim 9 recites: wherein the input data set comprises personal health data for one or more control subjects, wherein the personal health data is selected from the group consisting of subject age, gestational age, sex, weight, blood pressure, number of previous offspring (if female), ultrasound markers, biochemical screening results, smoking history, history of alcohol use, family history of disease, or any combination thereof.
Pending claim 33 recites: wherein the probability value represents a probability that a sequencing read of the set of sequencing reads corresponds to a particular methylated genomic region.
Patented claim 15 recites: wherein the one or more probability values represents a probability that a sequencing read of the set of sequencing reads corresponds to a particular genomic region
Pending claim 34 recites: wherein the machine learning algorithm generates a probability vector for each sequencing read of the set of sequencing reads.
Patented claim 16 recites: wherein the machine learning algorithm generates a probability vector for each sequencing read of the set of sequencing reads.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686
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