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 01/29/2026, has 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:
1-12, 15-21
New:
24-41
Amended:
13-14, 22-23
Pending:
13-14, 22-41
Withdrawn:
none
Examined:
13-14, 22-41
New Independent:
24, 39
Allowable:
none
Priority
As detailed on the 07/09/2019 filing receipt, this application claims priority to as early as 06/20/2018.
Withdrawn Rejections/Objections
The nucleotide and/or amino acid sequence disclosures requirement for Drawings at Figures 3-5, in the Office action mailed 09/25/2025 is withdrawn because of amended specification filed 06/20/2023.
The interpretation of claims 7, 12, 15 and 16 under 35 U.S.C. §112(f), in the Office action mailed 09/25/2025 is withdrawn in view of amendments and cancellation of claims in the claim set filed 01/29/2026.
The rejection of claims 7-18 and 22-23 under 35 U.S.C. §112(a), in the Office action mailed 09/25/2025 is withdrawn in view of the amendments and cancellation of claims in the claim set filed 01/29/2026.
The rejection of claims 7-18 and 22-23 under 35 U.S.C. §112(b), Second Paragraph, in the Office action mailed 09/25/2025 is withdrawn in view of the amendments and cancellation of claims in the claim set filed 01/29/2026. However, a new rejection is applied.
The rejection of claims 7-18 and 22-23 under 35 U.S.C. §101, in the Office action mailed 09/25/2025 is withdrawn in view of the amendments and cancellation of claims in the claim set filed 01/29/2026. However, a new rejection is applied.
Claim Rejections - 35 USC § 112/b-indefiniteness
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-14 and 22-41 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 24 and 39 recite “receiving, by a computer, the DNA sequence; extracting, by the computer, repeat regions in the DNA sequence…” and “evaluating, by the computer… estimating, by the computer… generating, by the computer…” The computer system cannot directly recite process steps such as receiving, extracting, evaluating, estimating, or generating because a single claim which claims both an apparatus and the method steps of using the apparatus is indefinite (see MPEP 2173.05(p).II)
Dependent claims are rejected for being dependent on rejected claims.
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 13-14 and 22-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 24 is directed to a 101 process, here a "method for implementing a computer-based classification system," with process steps such as "receiving…, extracting…"
Independent claim 39 is directed to a 101 process, here a "method for implementing a computer-based classification system," with process steps such as "receiving…, extracting…"
[Step 1: claims 13-14 and 22-41: YES]
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:
Independent Claim 24 recites: "…evaluating, by the computer, a respective consensus pattern as an estimated non-duplicated and non-mutated starting sequence of nucleotides that can result in the repeat region when subjected to any one of a plurality of sets of mutational events; for each repeat region, estimating, by the computer, a plurality of sequences of duplication events and mutation events defining the plurality of sets of mutational events, and identifying one of the sequences having a lowest energy cost; … labeling the data object with the respective consensus pattern, thereby obtaining a labeled data object…generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions; and aligning, via a computer-based dynamic programming alignment algorithm, the mutation profile by: inserting placeholder objects to provide a dimension of the mutation profile that is equal to a set dimension for all mutation profiles input to the classification system; arranging an order of each labeled data object and the placeholder objects within the mutation profile based on a best alignment score obtained by recursively calculating, for each possible arranging order… generating an aligned vector of numerals of the mutation profile... classify a mutation profile of an individual…classifying said mutation profile by evaluating distances between the vector of numerals of the mutation profile of the individual and the vectors of numerals of the reference mutation profiles associated with occurrence of the conditions associated with genomic factors to compute probabilities of occurrence of said conditions in the individual…reporting a risk propensity, comprising: selecting, based on a ranking of the computed probabilities of occurrence, a subset of the conditions to provide one or more targeted conditions for screening; and generating, by the computer, a risk propensity report based on the one or more targeted conditions in the individual, said report configured to enable selection of providing one or more targeted protocols for screening and detection of the one or more targeted conditions in the individual." The limitations are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Independent Claim 39 recites: "…evaluating, by the computer, a respective consensus pattern as an estimated non-duplicated and non-mutated starting sequence of nucleotides that can result in the repeat region when subjected to any one of a plurality of sets of mutational events; for each repeat region, estimating, by the computer, a plurality of sequences of duplication events and mutation events defining the plurality of sets of mutational events, and identifying one of the sequences having a lowest energy cost; … labeling the data object with the respective consensus pattern, thereby obtaining a labeled data object…generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions; and aligning, via a computer-based dynamic programming alignment algorithm, the mutation profile by: inserting placeholder objects to provide a dimension of the mutation profile that is equal to a set dimension for all mutation profiles input to the classification system; arranging an order of each labeled data object and the placeholder objects within the mutation profile based on a best alignment score obtained by recursively calculating, for each possible arranging order… generating an aligned vector of numerals of the mutation profile... generate a risk propensity for the target condition for the individual …classifying said mutation profile by evaluating distances between the aligned vector of numerals of the mutation profile of the individual and the vectors of numerals of the reference mutation profiles to compute a probability of occurrence of the target condition in the individual …4) generating a report configured to enable selection of a protocol for screening or detection of the target condition in the individual, the report comprising the generated risk propensity and information for selecting one or more targeted protocols for screening and detection." The limitations are acts of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 14 recites: "combining the risk propensity with other risk propensities to create a multiple condition risk propensity." Combining the risk propensities is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 22 recites: "…determining a classification accuracy for the first condition against the second condition; and determining a condition distance based on the classification accuracy." Determining is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 26 recites: "...identifying the sequence having the lowest energy cost comprises selecting, from the plurality of sequences, a sequence that minimizes a sum of a number of duplication events and a number of mutation events in the sequence." Identifying, selecting and summing are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 27 recites: "wherein selecting the sequence that minimizes the sum comprises applying different weights to mutation events of different types including substitutions, insertions, and deletions." Selecting is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 28 recites: "…identifying the sequence having the lowest energy cost further comprises selecting, from among the multiple sequences, a sequence having a highest likelihood measure based on the duplication events and the mutation events of the sequence." Identifying is an act of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 29 recites: "wherein arranging the order of each labeled data object and the placeholder objects comprises selecting an arranging order that yields a lowest cumulative alignment score as the best alignment score." Arranging and selecting are acts of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 32 recites: "wherein the normalized edit distance between two consensus patterns is a minimal number of insertions, deletions, and substitutions required to transform one of the two consensus patterns into the other of the two consensus patterns, divided by an average length of the two consensus patterns." Dividing is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 33 recites: "wherein, for each repeat region in step 1), estimating the plurality of sequences of duplication events and mutation events comprises constructing, by the computer, a history estimation graph that represents candidate sequences of duplication events and mutation events as paths in the history estimation graph." Estimating is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 34 recites: "wherein identifying the sequence having the lowest energy cost comprises assigning a weight to each path of the history estimation graph based on one or more factors selected from among likelihood measures associated with constituent duplication events and mutation events, event-type-dependent costs, and path length." Identifying is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claims 37 and 40 recite: "wherein aligning the mutation profile comprises aligning the labeled data objects corresponding to the plurality of repeat regions." Aligning labeled data objects to repeat regions is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Claim 38 recites: "wherein estimating the plurality of sequences of duplication events and mutation events comprises enumerating multiple candidate sequences of duplication events and mutation events for each of the plurality of repeat regions." Estimating is an act of evaluating, analyzing and judging data that could be practically performed in the human mind and/or with pen and paper.
Mathematical concepts recited include:
Independent Claim 24 recites: "…recursively calculating, for each possible arranging order…2) training a machine learning model of the computer-based classification system on reference mutation profiles… building respective reference mutation profiles each according to the computer-based algorithm of step 1); and training the machine learning model on vectors of numerals of the reference mutation profiles thereby producing a classifier of the computer-based classification system that classifies based on the conditions; 3) using the classifier to classify a mutation profile of an individual, comprising: … building a corresponding mutation profile according to the computer-based algorithm of step 1)…;... evaluating distances between the vector of numerals of the mutation profile of the individual and the vectors of numerals of the reference mutation profiles associated with occurrence of the conditions associated with genomic factors to compute probabilities of occurrence of said conditions in the individual…." Training the machine learning models, evaluating distances between the vector of numerals and to compute probabilities of occurrence are mathematical concepts and/or formulas.
Independent Claim 39 recites: "…recursively calculating, for each possible arranging order…2) training a machine learning model for the target condition… building respective reference mutation profiles each according to the computer-based algorithm of step 1); and training the machine learning model on vectors of numerals of the reference mutation profiles, thereby producing a classifier configured to evaluate likelihood of the target condition; 3) using the classifier to generate a risk propensity for the target condition for the individual, comprising, comprising: … building a corresponding mutation profile according to the computer-based algorithm of step 1)…;... evaluating distances between the aligned vector of numerals of the mutation profile of the individual and the vectors of numerals of the reference mutation profiles to compute a probability of occurrence of the target condition in the individual…." Training the machine learning models, evaluating distances between the vector of numerals and to compute probabilities of occurrence are mathematical concepts and/or formulas.
Claim 22 recites: "building the classifier, wherein a first condition and a second condition are classified by the classifier; determining a classification accuracy for the first condition against the second condition; and determining a condition distance based on the classification accuracy."
Claim 26 recites: "identifying the sequence having the lowest energy cost comprises selecting, from the plurality of sequences, a sequence that minimizes a sum of a number of duplication events and a number of mutation events in the sequence."
Claim 27 recites: "wherein selecting the sequence that minimizes the sum comprises applying different weights to mutation events of different types including substitutions, insertions, and deletions."
Claim 29 recites: "wherein arranging the order of each labeled data object and the placeholder objects comprises selecting an arranging order that yields a lowest cumulative alignment score as the best alignment score."
Claim 30 recites: "wherein the cumulative alignment score comprises a sum of the normalized edit distances calculated for the consensus patterns of the mutation profile relative to the respective consensus patterns of the mutation profile input to the classification system, including the normalized edit distances associated with the placeholder objects."
Claim 31 recites: "wherein the normalized edit distance associated with the missing pattern represented by each of the placeholder objects is set to a fixed non-zero constant value for all mutation profiles input to the classification system."
Claim 32 recites: "wherein the normalized edit distance between two consensus patterns is a minimal number of insertions, deletions, and substitutions required to transform one of the two consensus patterns into the other of the two consensus patterns, divided by an average length of the two consensus patterns."
Claim 33 recites: "wherein, for each repeat region in step 1), estimating the plurality of sequences of duplication events and mutation events comprises constructing, by the computer, a history estimation graph that represents candidate sequences of duplication events and mutation events as paths in the history estimation graph."
Law of nature recited include:
Claims 24 and 39 recite a correlation between DNA mutation sequences and a risk of disease, which is a law of nature because it describes a consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the DNA in the body and the risk of developing disease.
Claims 14, 22, 24, 26-29, 32-34, 37-40 as indicated above recite mental processes. For instance, independent claims 24 and/or 39 are involved with evaluating a respective consensus pattern; estimating a plurality of sequences of duplication events and mutation; identifying one of the sequences having a lowest energy cost; labeling the data object; generating a mutation profile of the DNA sequence; aligning the mutation profile; recursively calculating; generating an aligned vector of numerals of the mutation profile; classifying mutation profiles; evaluating distances between the vector of numerals of the mutation profile of the individual and the vectors of numerals of the reference mutation profiles associated with occurrence of the conditions associated with genomic factors to compute probabilities of occurrence of said conditions in the individual; reporting a risk propensity; selecting a subset of the conditions; and generating a risk propensity report based on the one or more targeted conditions in the individual; providing one or more targeted protocols for screening and detection of the one or more targeted conditions in the individual, which are 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 24 and 39 recite performing the method by 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. Therefore, 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 22, 24, 26-27, 29-33 and 39 recite mathematical concepts and formulas as indicated above. For instance, claims 24 and 39 recite training a machine learning model, evaluating distances between the vector of numerals and to compute probabilities of occurrence are mathematical concepts and/or formulas are mathematical concepts and/or formulas that falls under the “mathematical concepts” grouping of abstract ideas. Also, the court found that training machine learning is directed to abstract ideas of mathematical concepts at step one of Alice as determined by the court in Recentive Analytics, Inc. V. Fox Corp. In Recentive Analytics, Inc. V. Fox Corp.,
The claim limitations of claims 24 and 39 as indicated above recite a correlation between DNA mutation sequences and a risk of disease, which is a law of nature because it describes a consequence of natural processes in the human body, e.g., the naturally-occurring relationship between the DNA in the body and the risk of developing disease.
As such, claims 13-14 and 22-41 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:
Independent Claim 24 recites "…computer-based classification system… a computer-based algorithm …receiving, by a computer, the DNA sequence; extracting, by the computer, repeat regions in the DNA sequence… generating, by the computer, a data object comprising numerals representative of a number of duplication events and a number of mutation events in the sequence identified with the lowest energy cost… obtaining a labeled data object… generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions… generating an aligned vector of numerals of the mutation profile… obtaining reference DNA sequences from a plurality of populations each corresponding to a condition of the conditions and building respective reference mutation profiles… obtaining a DNA sequence of an individual and building a corresponding mutation profile according to the computer-based algorithm of step 1); inputting the vector of numerals of the mutation profile of the individual to the classifier…generating, by the computer, a risk propensity report based on the one or more targeted conditions in the individual…"
Independent Claim 39 recites "…computer-based classification system… a computer-based algorithm …receiving, by a computer, the DNA sequence; extracting, by the computer, repeat regions in the DNA sequence… generating, by the computer, a data object comprising numerals representative of a number of duplication events and a number of mutation events in the sequence identified with the lowest energy cost… obtaining a labeled data object… generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions… generating an aligned vector of numerals of the mutation profile… obtaining reference DNA sequences from one or more reference populations having the target condition and one or more reference populations of individuals not having the target condition; building respective reference mutation profiles for the reference DNA sequences according to the computer-based algorithm of step 1)… obtaining a DNA sequence of an individual and building a corresponding mutation profile according to the computer-based algorithm of step 1); inputting the vector of numerals of the mutation profile of the individual to the classifier…4) generating a report configured to enable selection of a protocol for screening or detection of the target condition in the individual, the report comprising the generated risk propensity and information for selecting one or more targeted protocols for screening and detection."
The computer recited in independent claims 24 and 39 equate to a generic computer environment that are used to implement 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)). Furthermore, claims 24 and 39 indicated above, for instance, include receiving the DNA sequence, generating a data object, obtaining a labeled data object, generating a mutation profile and generating a risk propensity report that equate to mere data gathering and outputting via generic computer components. Receiving data at a computer or outputting data, amount to insignificant extra-solution activity (See MPEP 2106.05(g)). The process of generating a report is providing information consisting of protocols or instructions, which the courts have indicated may not be sufficient to show an improvement in computer-functionality as in Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018). The report generated is also post-solution activity as in the example provided by MPEP 2106.05(g), paragraph 1, of 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. The report generated is based on analyzed data. This post-solution activity is an element that is not integrated into the claim as a whole. (MPEP 2106.05(g)). 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.
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:
Independent Claim 24 recites "…computer-based classification system… a computer-based algorithm …receiving, by a computer, the DNA sequence; extracting, by the computer, repeat regions in the DNA sequence… generating, by the computer, a data object comprising numerals representative of a number of duplication events and a number of mutation events in the sequence identified with the lowest energy cost… obtaining a labeled data object… generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions… generating an aligned vector of numerals of the mutation profile… obtaining reference DNA sequences from a plurality of populations each corresponding to a condition of the conditions and building respective reference mutation profiles… obtaining a DNA sequence of an individual and building a corresponding mutation profile according to the computer-based algorithm of step 1); inputting the vector of numerals of the mutation profile of the individual to the classifier…generating, by the computer, a risk propensity report based on the one or more targeted conditions in the individual…"
Independent Claim 39 recites "…computer-based classification system… a computer-based algorithm …receiving, by a computer, the DNA sequence; extracting, by the computer, repeat regions in the DNA sequence… generating, by the computer, a data object comprising numerals representative of a number of duplication events and a number of mutation events in the sequence identified with the lowest energy cost… obtaining a labeled data object… generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions… generating an aligned vector of numerals of the mutation profile… obtaining reference DNA sequences from one or more reference populations having the target condition and one or more reference populations of individuals not having the target condition; building respective reference mutation profiles for the reference DNA sequences according to the computer-based algorithm of step 1)… obtaining a DNA sequence of an individual and building a corresponding mutation profile according to the computer-based algorithm of step 1); inputting the vector of numerals of the mutation profile of the individual to the classifier…4) generating a report configured to enable selection of a protocol for screening or detection of the target condition in the individual, the report comprising the generated risk propensity and information for selecting one or more targeted protocols for screening and detection."
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 mere data gathering activities, which are insignificant extra solutional activities. These elements are conventional data gathering/input elements, and/or conventional post-processing or output elements, which amounts to insignificant extra solution activity (MPEP 2106.05(g)). As disclosed inf MPEP 2106.05(d)(ll)(v), generating a specific mutation profile and utilizing the profile in machine learning analysis are conventional activities because the courts have recognized that analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546 is 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)(ll)(v)). Also, obtaining genetic data for the analysis of historical mutational events is a known method as disclosed by Fan ("A brief review of short tandem repeat mutation." Genomics, proteomics & bioinformatics 5.1 (2007): 7-14.; cited on the 09/25/2025 892 form) and Anisimova ("Statistical approaches to detecting and analyzing tandem repeats in genomic sequences." Frontiers in bioengineering and biotechnology 3 (2015): 31.; cited on the attached 892 form). Fan discloses, “Using the population approach, the common evolutionary origin of STRs can be detected and the mutation events can be traced back many generations” (Fan, page 8, col. 1, para. 5) and Anisimova discloses in Fig. 1 (page 2) tandem repeat duplication histories. 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)). 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). Also, 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 13-14 and 22-41 are not patent eligible.
Response to 35 USC § 101 Remarks received 01/29/2026, pages 20-26
Applicant amended claims 13-14 and 22-23. Applicant canceled claims 1-12 and 15-21. Applicant newly added claims 24-41.
It is noted that Applicant’s remarks are based on amended and newly added claims.
Under Step 2A, Prong One of the 101 analysis, Applicant asserts that the present independent claims do not recite a mental process because the structure and ordering of the claimed steps require computer-implemented operations that cannot practically be carried out mentally.
Applicant refers to USPTO’s Example 1 and Example 3. Applicant states that in the USPTO’s Example 1 and Example 3, the Office determined that the claims do not recite a mental process, which is similar to the claimed subject matter. Applicant provides a comparison of the claimed subject matter with those of Examples 1 and 3. Applicant indicates that Example 1 is involved with GPS Pseudo range Calculation and Example 3 is involved with Multi-Step Data Transformation.
Under Step 2A, Prong Two of the 101 analysis, Applicant asserts that the present independent claims do not recite a recite a judicial exception and the claims integrate any such exception into a practical application under MPEP §2106.04(d). Applicant states that the claims require a computer to generate, align, and operate on enforced numerical representations before any classification or reporting occurs. Applicant states that these constraints integrate the claimed operations into a practical application rather than appending insignificant extra-solution activity. Applicant further states that the independent claims require the computer to (i) numerically encode repeat regions into labeled data objects comprising numerals, (ii) align those objects to a set dimension using placeholder objects and defined alignment rules, and (iii) maintain an ordered dependency in which classification occurs only after alignment is complete.
Applicant also states that the claimed subject matter is in contrast with population-reconstruction approaches such as FAN, which rely on reconstruction across multiple DNA sequences from a large population because the present independent claims require numerical representation and alignment derived from a single DNA sequence prior to classification.
In response, Applicant’s arguments are not persuasive. As indicated above in the 101 rejections section, the claims recite the abstract ideas of mental processes, mathematical concepts and/or formulas and law of nature. Although, claims 24 and 39 recite performing the method by 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. It is acknowledged that such computations performed mentally, or with paper and pencil, would take considerable time and effort, but that is, of course, the singular purpose of computers and computer networks, to perform large numbers of calculations, via algorithms, rapidly, and without error (assuming no error in user input). Although a general-purpose computer can perform calculations at a rate and accuracy that can far outstrip the mental performance of a skilled artisan, the nature of the activity is essentially the same, and constitutes an abstract idea. See Bancorp Serves., L.L. C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266,1278 (Fed. Cir. 2012) (holding that “the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”); see also See SiRF Tech., Inc. v. Int’l Trade Comm ’n, 601 F.3d 1319,1333 (Fed. Cir. 2010) (holding that: In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations).Therefore, 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.
Regarding USPTO Examples, it is noted that USPTO Example 1 is not involved with GPS Pseudo Range Calculation and Example 3 is not involved with Multi-Step Data Transformation as indicated by applicant. The USPTO Example 1 is titled Isolating and Removing Malicious Code from Electronic Messages and Example 3 is titled Digital Image Processing. It is believed that Applicant is referring to USPTO Example 4 titled Global Positioning System for Applicant’s Example 1. However, it is unclear which USPTO Example matches Applicant’s Example 3. Therefore, comments cannot be made for Applicant’s assertion that the claimed subject matter is similar to Example 3.
Regarding Example 4 titled Global Positioning System, the court found that the combination of elements impose meaningful limits in that the mathematical operations are applied to improve an existing technology (global positioning) by improving the signal-acquisition sensitivity of the receiver to extend the usefulness of the technology into weak-signal environments and providing the location information for display on the mobile device. In the instant case, the claimed subject matter does not appear to improve an existing technology or the functioning of the computer. The claimed subject matter includes training a machine learning model with mutation profiles to generate a risk propensity report to enable selection of one or more targeted protocols. The outputted risk propensity report contains one or more targeted protocols that could be selected, which equates to mere instructions to apply an exception (See MPEP 2106.05(f)). The generated report allows for the selection of protocols that could be selected from a list. Overall, the report is providing and displaying information consisting of protocols or instructions, which the courts have indicated may not be sufficient to show an improvement in computer-functionality as in Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018). In Interval Licensing LLC v. AOL, Inc., the claims provide instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, (see MPEP 2106.05(a)). Also, generating a report is post-solution activity as in the example provided by MPEP 2106.05(g), paragraph 1, of 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. The report generated is based on analyzed data. This post-solution activity is an element that is not integrated into the claim as a whole. (MPEP 2106.05(g)).
Regarding the cited reference, Fan, it is acknowledged that Fan analyzes a population of sequences to determine mutation events. However, Fan is applicable to the instant claimed subject matter because step 2 of independent claims 24 recites “obtaining reference DNA sequences from a plurality of populations each corresponding to a condition of the conditions and building respective reference mutation profiles” and step 2 of independent claim 39 recites “obtaining reference DNA sequences from one or more reference populations having the target condition and one or more reference populations of individuals not having the target condition.” It was also found that Anisimova discloses in Fig. 1 (page 2) tandem repeat duplication histories.
Regarding 35 USC 102 or 35 USC 103 -- No Art Rejection Applied
No prior art is applied to claims 13-14 and 22-41.
Closest prior art for independent claims 24 and 39, for example are Jaitly (as cited on the 02/17/2023 "Notice of References Cited" form 892) and Hause (as cited on the 02/17/2023 "Notice of References Cited" form 892). Jaitly teaches methods for determining mutation histories and Hause teaches methods for running the classifier on a mutation profile of the individual such that a risk propensity for the target condition is generated. Jaitly and Hause do not teach the instant combination of steps of “evaluating, by the computer, a respective consensus pattern as an estimated non-duplicated and non-mutated starting sequence of nucleotides that can result in the repeat region when subjected to any one of a plurality of sets of mutational events; for each repeat region, estimating, by the computer, a plurality of sequences of duplication events and mutation events defining the plurality of sets of mutational events, and identifying one of the sequences having a lowest energy cost; for each repeat region, generating, by the computer, a data object comprising numerals representative of a number of duplication events and a number of mutation events in the sequence identified with the lowest energy cost, and labeling the data object with the respective consensus pattern, thereby obtaining a labeled data object; generating, by the computer, a mutation profile of the DNA sequence comprising the labeled data object of each repeat region of the repeat regions; and aligning, via a computer-based dynamic programming alignment algorithm, the mutation profile by: inserting placeholder objects to provide a dimension of the mutation profile that is equal to a set dimension for all mutation profiles input to the classification system; arranging an order of each labeled data object and the placeholder objects within the mutation profile based on a best alignment score obtained by recursively calculating, for each possible arranging order, a normalized edit distance between each consensus pattern of the mutation profile and a respective consensus pattern of a mutation profile input to the classification system, wherein the normalized edit distance associated with a missing pattern represented by each of the placeholder objects is assumed constant and non-zero in value; and based on the arranging, generating an aligned vector of numerals of the mutation profile, the aligned vector comprising the numerals of the labeled data objects and zero-valued numerals placed at positions corresponding to the placeholder objects as determined by the best alignment score” in claims independent claims 24 and 39. It is not clear that any combinable art of record would have rendered the claims obvious.
No prior art rejections are applied to dependent claims 13-14, 22-23, 25-38 and 40-41 because of their dependence on independent claims 24 and 39.
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
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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