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 .
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.
Applicant's response, filed on 07/29/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of claims
Canceled:
3-6, 8-9, 17
Amended:
1, 7, 10, 11, 18
Pending:
1-2, 7, 10-16, 18
Examined:
1-2, 7, 10-16, 18
Independent:
1, 18
Priority
As detailed on the 10/14/2022 filing receipt, this application claims priority to as early as 11/29/2019.
Drawings
The drawings filed 05/27/2022 are accepted.
Withdrawn Rejections/Objections
The objection of the abstract of the disclosure in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026.
The rejection of claims 4-7 and 17 under 35 U.S.C. §112(b), Second Paragraph, in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026.
The rejection of claims 1-18 under 35 U.S.C. §101, in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026. However, a new rejection is applied in view of claim amendments.
The rejection of claims 1-3 and 12-18 under 35 U.S.C. §102(a)(2) over Ehrich, in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026.
The rejection of claims 4-5 and 8 under 35 U.S.C. §103 over Ehrich in view of Lo, in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026.
The rejection of claims 1-7 and 12-15 on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, and 7-11 of U.S. Patent No. 12163194 (App. No. 18003455), in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026. However, a new rejection is applied in view of claim amendments.
The rejection of claims 1-5 and 17-18 on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, and 12-13 of Reference App. No. 18822233, in the Office action mailed 04/29/2026 is withdrawn in view of the amendments filed 07/29/2026. However, a new rejection is applied in view of claim amendments.
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 1-2, 7, 10-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claims 1 and 18 recite: “…analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present…1) calculating the distance between the aligned nucleic acid fragments(FD) for each chromosome; 2) determining a representative distance between fragments for each bin based on the distance calculated in step i)… 4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis .”Analyzing the data, comparing the resulting value with a cut-off value to determine the presence of chromosomal abnormality, selecting and generating plots are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper (See MPEP 2106.04(a)(2) subsection III).
Claim 7 recites: “wherein the representative FD (RepFD) comprises at least one selected from the group consisting of a sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation, coefficient of variance of FD, a reciprocal thereof and a combination thereof.” This limitation is involved with the act of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 12 recites: “wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.” This limitation involves comparing data that could be practically performed in the human mind and/or with pen and paper.
Claim 13 recites: “wherein the artificial intelligence model is selected from the group consisting of a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder.” Selecting could be practically performed in the human mind and/or with pen and paper.
Claim 16 recites: “wherein the cut-off value of step d) is 0.5, and when the resulting value is 0.5 or more, it is determined that there is a chromosomal abnormality.” Determining could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Claims 1 and 18 recite: “wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps: 1) calculating the distance between the aligned nucleic acid fragments(FD) for each chromosome; 2) determining a representative distance between fragments for each bin based on the distance calculated in step i); 3) standardizing the representative distance between fragments in each chromosome determined in step ii); 4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments.”
Claim 7 recites: “wherein the representative FD (RepFD) comprises at least one selected from the group consisting of a sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation, coefficient of variance of FD, a reciprocal thereof and a combination thereof.”
Claim 10 recites: “1) setting a reference group including normal samples from which chromosomal aneuploidies are not detected; 2) calculating a mean (reference mean) and standard deviation (reference standard deviation) of RepFDs for respective chromosomes observed in the reference group; 3) conducting Z standardization by applying the reference mean and reference standard deviation calculated in step 2) to Equation 1; and 4) dividing Zchr for the chromosome of the reference group calculated in step 3) by Zchr for the chromosome for which presence of aneuploidy is to be determined.”
Claim 11 recites: “wherein the representative FD (RepFD) comprises at least one selected from the group consisting of a sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation and coefficient of variance of the fragment distance (FD), a reciprocal thereof, and a combination thereof.”
Claim 14 recites: loss function and Equations 2-3
Claim 15 recites: deep probability index (DPI)
Claims 1, 7, 12-13, 16 and 18, as indicated above, recite mental processes, such as determining, analyzing, calculating, selecting and creating plots. The claim elements as indicated above are involved with acts of evaluating, analyzing, observing and judging data. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2) subsection III). Although, claims 1 and 18 recite performing the method as part of a method executed on a computer, there are no additional limitations to indicate that anything other than a generic computer is required. However, merely requiring that the steps are carried out with a generic computer does not negate the mental nature of these steps and equates rather to merely using a computer as a tool to perform the mental process. Overall, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "mental processes" grouping of abstract ideas.
Claims 1, 7, 10-11, 14-15 and 18 as indicated above, are mathematical concepts and/or mathematical formulas that requires performing a series of calculations. Therefore, under the broadest reasonable interpretation, the indicated claims above falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-2, 7, 10-16 and 18 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites " A computer-implemented method… a) obtaining sequence information using extracted nucleic acids from a biological sample; c) generating, by the computer system, vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model… and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model...."
Claim 2 recites “(a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof (a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method; (a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the prepared library with a next- generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.”
Claim 15 recites: "wherein the resulting value output through analysis of input vectorized data by the artificial intelligence model in step d) is a deep probability index (DPI)."
Claim 18 recites "A non-transitory computer-readable storage medium…a) obtaining sequence information using extracted nucleic acids from a biological sample; c) generating, by the computer system, vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting the generated vectorized data into a trained artificial intelligence model… and d) inputting, the generated vectorized data into a trained artificial intelligence model...."
The elements of claims 1-2, 15 and 18 as indicated above equate to insignificant extra solutional activities of data gathering and outputting. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent (See MPEP 2106.05(g)). Limitations that add insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) have been identified by the courts to not integrate a judicial exception into a practical application (MPEP 2106.04(d)(I)).
Claim 1 recites a computer-implemented method and claim 18 recites a non-transitory computer-readable storage medium and a processor. These elements of claims 1 and 18 equate to generic computer components and the computer components are invoked merely as tools to execute the abstract idea. 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 or provide significantly more. (see MPEP 2106.05(f)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-2, 7, 10-16 and 18 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites " A computer-implemented method… a) obtaining sequence information using extracted nucleic acids from a biological sample; c) generating, by the computer system, vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model… and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model...."
Claim 2 recites “(a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof (a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method; (a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the prepared library with a next- generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.”
Claim 15 recites: "wherein the resulting value output through analysis of input vectorized data by the artificial intelligence model in step d) is a deep probability index (DPI)."
Claim 18 recites "A non-transitory computer-readable storage medium…a) obtaining sequence information using extracted nucleic acids from a biological sample; c) generating, by the computer system, vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting the generated vectorized data into a trained artificial intelligence model… and d) inputting, the generated vectorized data into a trained artificial intelligence model...."
The additional elements indicated above do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Additionally, the courts have recognized that detecting DNA or enzymes in a sample, analyzing DNA to provide sequence information or detecting allelic variants and amplifying and sequencing nucleic acid sequences as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)). Shendure ("Next-generation DNA sequencing." Nature biotechnology 26.10 (2008): 1135-1145.; as cited on the 04/29/2026 892 form) also discloses that the laboratory methods of next generation sequencing, paired-end sequencing and library preparation by random fragmentation are well-known and conventional methods in the arts. Inputting vectorized data into an artificial intelligence model is also a well-known method as discussed by Karhunen ("Unsupervised deep learning: A short review." Advances in independent component analysis and learning machines (2015): 125-142.; cited on the attached 892 form). Karhunen discusses inputting vector data into multilayer perceptron networks (page 126, section 7.2), deep learning models (page 127, section 7.3) and autoencoders (page 133, section 7.7).
Limitations that add insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) have been identified by the courts to not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception. Also, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Overall, 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 or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI 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 claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 7, 10-16 and 18 are not patent eligible.
Response to 35 USC § 101 Remarks received 07/29/2026, pages 13-14
Applicant amended claims 1, 7, 10-11 and 18. Applicant canceled claims 3-6, 8-9, 17.
It is noted that Applicant’s remarks are based on amended claims.
Applicant states that amended claim 1 involving the length of the obtained nucleic acid in the number of reads cannot reasonably be performed via pen and paper and does not constitute mental processes. Applicant also states that FD is a value calculated as the distance between a specific position within one nucleic acid fragment and a specific position within another nucleic acid fragment, which is a technical feature distinct from conventional read count or fragment size. Applicant also discussed Example 8 of the specification. Applicant states that it was confirmed that even when read coverage was reduced from approximately 10,000,000 reads to approximately 5,000,000 reads, the GCW plot-based model maintained a similar level of performance in detecting trisomy 21. Therefore, Applicant asserts that the amended claim constitutes a practical application that improves the NGS-based chromosomal abnormality detection technology.
In response, Applicants' remarks have been fully considered and are not persuasive. As discussed in the 101 claim rejections section above, claim 1 recites both mental processes and mathematical concepts. For instance, the process of analyzing data and comparing the results with a cutoff value is a mental process that can be performed by the human mind with or without pen and paper. There is no indication in the claims that the amount of data is too complicated or too large to be performed by the human mind and/or with pen and paper. Claim 1 also recites mathematical concepts, such as calculating a distribution of aligned nucleic acid fragments. Regarding FD, FD is a value that is obtained as a result of the calculation step. The calculation step is a mathematical concept and the value equates to a number which is a conventional and well-known form of data. From the asserted improvement, it is not clear how the claimed invention improves over existing technology and Example 8 of the specification does not provide this explanation. Therefore, Applicant’s remarks are not persuasive and a 101 claim rejections is applied as discussed above.
Regarding 35 USC 102/103 -- no art rejection applied
No prior art is applied to claims 1-2, 7, 10-16 and 18. Close art is Ehrich (as cited on the 04/29/2026 "Notice of References Cited" form 892). Ehrich teaches obtaining and aligning sequence reads with a reference genome. Ehrich also teaches inputting vectorized data into an artificial intelligence model. However, Ehrich does not teach "wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps: 1) calculating the distance between the aligned nucleic acid fragments(FD) for each chromosome; 2) determining a representative distance between fragments for each bin based on the distance calculated in step i); 3) standardizing the representative distance between fragments in each chromosome determined in step ii); 4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments," in claims 1 and 18 and it is not clear that any combinable art of record would have rendered the claims obvious.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 7, 10-16 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, and 7-11 of U.S. Patent No. 12163194 (App. No. 18003455). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are directed towards generating vectorized data using nucleic acid fragments based on the aligned sequence reads and inputting the generated vectorized data into a trained artificial intelligence model for data analysis to determine whether the chromosome is normal or abnormal. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of U.S. Patent No. 12163194 (App. No. 18003455) that anticipate the claims of the instant application.
Instant app. 17780813, claim set 05/27/2022
Reference app. 18003455, claim set 08/15/2024, patent #12163194
Claims 1 and 18
Claims 1, 8 and 15
1. (Currently Amended) A computer-implemented method of detecting a chromosomal abnormality based on artificial intelligence, the method comprising: a) obtaining sequence information using extracted nucleic acids from a biological sample; b) aligning the obtained sequence information (reads) with a reference genome database, wherein the length of the sequence information (reads) in step b) is 5 to 5000 bp, wherein the number of sequence information (reads) in step b) is at least 5000; c) generating, by the computer system, vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model, analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present, wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps:
1) calculating the distance between the aligned nucleic acid fragments (FD) for each chromosome;
2) determining a representative distance between fragments for each bin based on the distance calculated in step i);
3) standardizing the representative distance between fragments in each chromosome determined in step ii);
4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and
5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments.
2. (Original) The method according to claim 1, wherein step (a) comprises: (a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof; (a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method; (a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the prepared library with a next-generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
18. (Currently Amended) A non-transitory computer-readable storage medium including an instruction configured to be executed by a processor for detecting a chromosomal abnormality through the following steps comprising: a) obtaining sequence information using extracted nucleic acids from a biological sample; b) aligning the obtained sequence information (reads) with a reference genome database, wherein the length of the sequence information (reads) in step b) is 5 to 5000 bp, wherein the number of sequence information (reads) in step b) is at least 5000; c) generating vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting the generated vectorized data into a trained artificial intelligence model, analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present, wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps:
1) calculating the distance between the aligned nucleic acid fragments (FD) for each chromosome;
2) determining a representative distance between fragments for each bin based on the distance calculated in step i);
3) standardizing the representative distance between fragments in each chromosome determined in step ii);
4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments.
1. (Currently amended) A method of generating a cancer diagnosis and predicting a type of cancer based on artificial intelligence, the method comprising:
(a) extracting nucleic acids from a biological sample to obtain sequence information;
(b) aligning the sequence information consisting of reads with a reference genome database, wherein the sequence information comprises at least 5000 reads;
(c) generating, by a computer system, vectorized data using nucleic acid fragments derived from the aligned sequence information consisting of reads, wherein the vectorized data is a Grand Canyon plot (GC plot) that is generated by; a process including the following steps: c-i) dividing chromosomes into predetermined bins; c-ii) calculating a distance between nucleic acid fragments (fragment distance, FD) aligned in each bin: c-iii) determining a representative distance between fragments (RepFD) of each bin based on the FD calculated in each bin; c-iv) dividing the RepFD calculated in step iii) by a representative total nucleic acid fragment distance to conduct normalization; and c-v) creating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step c-iv) on a Y-axis; (d) inputting, by the computer system, the generated vectorized data to a trained artificial intelligence models- to generate a resulting output value; (e) processing, by the computer system, the resulting output value to generate a cancer diagnosis of whether there is cancer in the biological sample; and (f) when the cancer diagnosis is that cancer is present in the biological sample, predicting from said resulting output value a type of cancer that is present in the biological sample.
8. (Original) The method according to claim 1, wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.
15. (Currently amended) A non-transitory computer-readable storage medium comprising instructions stored and structurally configured to store data and to accomplish the following steps, for diagnosing cancer and predicting a type of cancer:(a) extracting nucleic acids from a biological sample to obtain sequence information; (b) aligning the obtained sequence information consisting of reads with a reference genome database, wherein the sequence information comprises at least 5000 reads;(c) generating, by a computer system, vectorized data using nucleic acid fragments derived from the aligned sequence information consisting of reads, wherein the vectorized data is a Grand Canyon plot (GC plot) that is generated by; a process including the following steps: c-i) dividing chromosomes into predetermined bins; c-ii) calculating a distance between nucleic acid fragments (fragment distance, FD) aligned in each bin, c-iii) determining a representative distance between fragments (RepFD) of each bin based on the FD calculated in each bin; c-iv) dividing the RepFD calculated in step iii) by a representative total nucleic acid fragment distance to conduct normalization and c-v) creating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step c-iv) on a Y-axis;(d) inputting the generated vectorized data to a trained artificial intelligence model, analyzing a resulting output value, and comparing the resulting output value with a cut-off value to determine whether or not there is cancer; and (e) analyzing the output result and thereby predicting the type of cancer.
[Claim 2] The method according to claim 1, wherein step (a) comprises:
(a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof;
(a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method;
(a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method;
(a-iv) reacting the prepared library with a next- generation sequencer; and
(a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
2. (Original) The method according to claim 1, wherein step (a) comprises: (a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof;(a-ii) removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method to obtain purified nucleic acids; (a-iii) producing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the produced library with a next-generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
[Claim 7] The method according to claim 6,
wherein the representative FD (RepFD) comprises at least one selected from the group consisting of a sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation, coefficient of variance of FD, a reciprocal thereof and a combination thereof.
7. (Currently amended) The method according to claim 1 [[6]], wherein the RepFD comprises at least one selected from the group consisting of a sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation, coefficient of variance of FD, a reciprocal thereof and a combination thereof.
[Claim 12] The method according to claim 1, wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.
8. (Original) The method according to claim 1, wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.
[Claim 13] The method according to claim 12, wherein the artificial intelligence model is selected from the group consisting of a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder.
9. (Original) The method according to claim 8, wherein the artificial intelligence model is selected from the group consisting of a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and an autoencoder.
[Claim 14] The method according to claim 12, wherein, when the artificial intelligence model is CNN and learns binary classification, loss function is represented by Equation 2 below, and
when the artificial intelligence model is CNN and learns multi-class classification, loss function is represented by Equation 3 below.
10. (Currently amended) The method according to claim 9, wherein, when the artificial intelligence model is a CNN and learns binary classification, a loss function is represented by Equation 1 below: Equation 1:
[Claim 15] The method according to claim 1, wherein the resulting value output through analysis of input vectorized data by the artificial intelligence model in step d) is a deep probability index (DPI).
11. (Currently amended) The method according to claim 1, wherein the resulting value output value through analysis of input vectorized data by the artificial intelligence model in step (d) is a deep probability index (DPI).
Claims 1-2, 7, 10-16 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, and 12-13 of Reference App. No. 18822233. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are directed towards generating vectorized data using nucleic acid fragments based on the aligned sequence reads and inputting the generated vectorized data into a trained artificial intelligence model for data analysis to determine whether the chromosome is normal or abnormal. Overall, the difference is that the claims of the instant application are broader in scope than the claims of the reference application and thus the instant claims are anticipated by the reference application (see MPEP 804.II.B.2). See table below for a mapping of the claims of Reference App. No. 18822233 that anticipate the claims of the instant application.
Instant app. 17780813, claim set 05/27/2022
Reference app. 18822233, claim set 09/01/2024,
Claims 1, 2 and 18
Claims 1-6
1. (Currently Amended) A computer-implemented method of detecting a chromosomal abnormality based on artificial intelligence, the method comprising: a) obtaining sequence information using extracted nucleic acids from a biological sample; b) aligning the obtained sequence information (reads) with a reference genome database, wherein the length of the sequence information (reads) in step b) is 5 to 5000 bp, wherein the number of sequence information (reads) in step b) is at least 5000; c) generating, by the computer system,vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting, by the computer system, the generated vectorized data into a trained artificial intelligence model, analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present, wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps:
1) calculating the distance between the aligned nucleic acid fragments (FD) for each chromosome;
2) determining a representative distance between fragments for each bin based on the distance calculated in step i);
3) standardizing the representative distance between fragments in each chromosome determined in step ii);
4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and
5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments.
2. (Original) The method according to claim 1, wherein step (a) comprises: (a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof; (a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method; (a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the prepared library with a next-generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
[Claim 2] The method according to claim 1, wherein step (a) comprises:
(a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof;
(a-ii) obtaining purified nucleic acids by removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method;
(a-iii) preparing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method;
(a-iv) reacting the prepared library with a next- generation sequencer; and
(a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
18. (Currently Amended) A non-transitory computer-readable storage medium including an instruction configured to be executed by a processor for detecting a chromosomal abnormality through the following steps comprising: a) obtaining sequence information using extracted nucleic acids from a biological sample; b) aligning the obtained sequence information (reads) with a reference genome database, wherein the length of the sequence information (reads) in step b) is 5 to 5000 bp, wherein the number of sequence information (reads) in step b) is at least 5000;c) generating vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting the generated vectorized data into a trained artificial intelligence model, analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present, wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps:
1) calculating the distance between the aligned nucleic acid fragments(FD) for each chromosome;
2) determining a representative distance between fragments for each bin based on the distance calculated in step i);
3) standardizing the representative distance between fragments in each chromosome determined in step ii);
4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy isto be determined as control chromosomes; and 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment and a specific position inside at least one of the i+1' to nth nucleic acid fragments.
1. A method for diagnosing cancer and predicting a type of cancer based on artificial intelligence, the method comprising: (a) extracting nucleic acids from a biological sample to obtain sequence information; (b) aligning the sequence information (reads) with a reference genome database; (c) generating vectorized data using nucleic acid fragments based on the aligned sequence information (reads); (d) inputting the generated vectorized data to a trained artificial intelligence model, analyzing the resulting output value, and comparing the resulting output value with a cut-off value to determine whether there is cancer; and (e) predicting the type of cancer through comparison of the output value.
2. The method according to claim 1, wherein step (a) comprises: (a-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, oral cells, amniotic fluid containing placental cells or fetal cells, tissue cells, or a mixture thereof; (a-ii) removing proteins, fats, and other residues from the obtained nucleic acids using a salting-out method, a column chromatography method, or a bead method to obtain purified nucleic acids; (a-iii) producing a single-end sequencing or paired-end sequencing library for the purified nucleic acids or nucleic acids randomly fragmented by an enzymatic digestion, pulverization, or hydroshear method; (a-iv) reacting the produced library with a next-generation sequencer; and (a-v) obtaining sequence information (reads) of the nucleic acids in the next-generation sequencer.
3. The method according to claim 1, wherein the vectorized data of step (c) is a Grand Canyon plot (GC plot).
4. The method according to claim 3, wherein the GC plot is characterized in that the vectorized data is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the count of nucleic acid fragments in each bin.
5. The method according to claim 4, wherein the calculating the distribution of the aligned sequence information in each chromosome bin based on the count of nucleic acid fragments is performed using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) determining the count of nucleic acid fragments aligned in each bin; iii) dividing the determined count of nucleic acid fragments in each bin by a total number of nucleic acid fragments in the sample to conduct normalization; and iv) creating a GC plot with an order of respective bins on an X-axis and a normalized value calculated in step iii) on a Y-axis.
6. The method according to claim 1, wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.
Claims 18
Claims 6 and 12-13
18. (Currently Amended) A non-transitory computer-readable storage medium including an instruction configured to be executed by a processor for detecting a chromosomal abnormality through the following steps comprising: a) obtaining sequence information using extracted nucleic acids from a biological sample; b) aligning the obtained sequence information (reads) with a reference genome database, wherein the length of the sequence information (reads) in step b) is 5 to 5000 bp, wherein the number of sequence information (reads) in step b) is at least 5000; c) generating vectorized data using nucleic acid fragments based on the aligned sequence information (reads); and d) inputting the generated vectorized data into a trained artificial intelligence model, analyzing the data, and comparing the resulting value with a cut-off value to determine whether or not a chromosomal abnormality is present, wherein the vectorized data of step c) is a grand canyon plot (GC plot) or a genomic castle wall plot (GCW plot), wherein the GC plot is generated by calculating a distribution of aligned nucleic acid fragments in each chromosome bin based on the distance between the nucleic acid fragments using a process including the following steps: i) dividing chromosomes into predetermined bins; ii) calculating the distance between nucleic acid fragments (fragment distance, FD) aligned in each bin iii) determining a representative distance between fragments (RepFD) of each bin based on the fragment distance calculated in each bin; iv) normalizing RepFD by dividing the representative distance between fragments (RepFD) calculated in step iii) by a representative total nucleic acid fragment distance; and v) generating a GC plot with the order of respective bins on an X-axis and the normalized value calculated in step iv) on a Y-axis; wherein the GCW plot is created using a process including the following steps:
1) calculating the distance between the aligned nucleic acid fragments (FD) for each chromosome;
2) determining a representative distance between fragments for each bin based on the distance calculated in step i);
3) standardizing the representative distance between fragments in each chromosome determined in step ii);
4) selecting a portion of chromosomes excluding chromosomes for which presence of aneuploidy is to be determined as control chromosomes; and 5) generating a GCW plot with the control chromosomes and the chromosomes for which presence of aneuploidy is to be determined sequentially and alternately aligned on the X axis and the standardized calculated values of the respective chromosomes on the Y axis wherein the FD is calculated as a distance between a specific position inside the it nucleic acid fragment
12. A device for diagnosing cancer and predicting a type of cancer based on artificial intelligence, the device comprising: a decoder configured to extract nucleic acids from a biological sample and decode sequence information; an aligner configured to align the decoded sequence with a reference genome database; a data generator configured to generate vectorized data using nucleic acid fragments based on aligned sequence information (reads); a cancer diagnostic unit configured to input the generated vectorized data into a trained artificial intelligence model, analyze the data, and compare the resulting value with a cut-off value to thereby determine whether or not there is cancer; and a cancer type predictor to analyze the output result and thereby predict the type of cancer.
13. A computer-readable storage medium including an instruction configured to be executed by a processor for diagnosing cancer and predicting a type of cancer through the following steps comprising: (a) extracting nucleic acids from a biological sample to obtain sequence information; (b) aligning the obtained sequence information (reads) with a reference genome database; (c) generating vectorized data using nucleic acid fragments based on the aligned sequence information (reads); (d) inputting the generated vectorized data to a trained artificial intelligence model, analyzing the resulting output value, and comparing the resulting output value with a cut-off value to determine whether or not there is cancer; and (e) analyzing the output result and thereby predicting the type of cancer.
6. The method according to claim 1, wherein the artificial intelligence model of step (d) is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes.
Response to nonstatutory double patenting rejection Remarks received 07/29/2026, pages 18-19
Applicant states that the claims of the reference patent and reference application are directed to cancer diagnosis and prediction, whereas the amended claims presented in the present response are directed to detection of chromosomal abnormalities.
In response, Applicants' remarks have been fully considered and are not persuasive. Although the instant set of claims is directed towards the detection of chromosomal abnormalities and the reference applications are directed towards cancer diagnosis and prediction, both sets of claims recite claim limitations that are involved in performing the same or similar processes and detecting chromosomal abnormalities. For instance, claim 8 of U.S. Patent No. 12163194 (App. No. 18003455) and claim 6 of Reference app. 18822233 recite an artificial intelligence model that is trained to distinguish between vectorized data of normal chromosomes and vectorized data of abnormal chromosomes. Therefore, the instant claims are anticipated by the reference applications.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
