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 .
Reopening of Prosecution After Appeal Brief
In view of the appeal brief filed on 3/19/2026, PROSECUTION IS HEREBY REOPENED. New grounds of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 62/328,240 filed 04/27/2016 is acknowledged.
Status of Claims
Claims 1-32, 34, 39, 46 and 61-197 are cancelled.
Claims 33, 35-38, 40-45, 47-60 and 198-209 are pending and examined on the merits.
Claim Rejections - 35 USC § 112
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.
Claim 50 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 50 recites “a window of width w”. However, the width “w” has never been defined. Claim 50 is hence indefinite.
Claim Rejections - 35 USC § 101
This rejection is maintained from a previous Office action, necessitated by claim amendments.
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 33, 35-38, 40-45, 47-60 and 198-209 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 33, 35-38, 40-45, 47-60 and 198-209 are to a method for detecting one or more genetic features in a nucleotide sequence, with a series functional steps. So a process.
Step 2A Prong One: Identification of Abstract Ideas
Claim 33 recites:
building, for each of a plurality of windows applied to the nucleotide sequence, a corresponding data structure, wherein the corresponding data structure is associated with a corresponding window in the plurality of windows and the corresponding data structure is stored with a unique pointer that points to the corresponding window;
This step recites building a corresponding data structure for each of a plurality of windows applied to the nucleotide sequence, which is interpreted as to convert the multiple-alignment sequence into a data structure. This action reads on a data manipulation that can be achieved in human mind. Therefore this step is classified into an abstract idea of mental processes
analyzing, for each of the plurality of windows, aligned reads from the nucleotide sequence for a plurality of statistical features, wherein the plurality of statistical features for each of the plurality of windows are stored in the corresponding data structure associated with the corresponding window;
This step recites data analysis on aligned sequence and acquires statistical features. This operation reads on mathematical calculations and is hence classified into an abstract idea of mathematical concepts. The acquired statistical features are stored in the data structure, which reads on a data manipulation. Therefore this step is also classified into an abstract idea of mental processes.
identifying, for each of the plurality of windows, candidates for genetic feature breakpoints;
This step recites a decision-making activity that can be achieved in the human mind. Hence this step is classified into an abstract idea of mental processes.
determining, using a trained machine learning algorithm, a presence of a genetic feature in the nucleotide sequence based on the statistical features stored in the data structure associated with the corresponding window of the plurality of windows and based on the candidates for genetic feature breakpoints, wherein the genetic feature is a structural variant selected from the group consisting of an insertion, a deletion, and an inversion;
Under a broadest reasonable interpretation (BRI), the “trained machine learning algorithm” reads on mathematics for example a linear regression algorithm, therefore the step equates to a mathematical operation with inputs and outputs. Hence this step is classified into an abstract idea of mathematical concepts.
combining, using a merging module, the genetic feature with prior reference information into a unified format to produce unified genetic feature information and excluding the nucleotide sequence and the plurality of statistical features from the unified genetic feature information; and
This steps recites data manipulations that can be achieved in human mind. Therefore this step is classified into an abstract idea of mental processes.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process of producing unified genetic feature information and excluding the nucleotide sequence and the plurality of statistical features from the unified genetic feature information; and transmitting the unified genetic feature information for storage. The first operation is classified into abstract ideas of mental processes. The second operation is classified into additional element, but is an insignificant extra-solution activity. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
(f) Transferring the unified genetic feature information for storage.
“A programmed computer” (claim 38).
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception because it is routine and conventional to perform the acts of data transmitting, data analyzing and data modeling. Transferring the unified genetic feature information for storage is an insignificant extra-solution activity. Other elements of the method include “a programmed computer” which is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Applicant’s Appeal Brief of 3/19/2026:
In the Appeal Brief filed 19 Mach 2026, Applicant argued (page 7, 3rd para) that “claims are not directed to an abstract idea and are thus allowable under 35 U.S.C. §101”. Applicant’s argument refers to Step 2A/Prong One in the 101 analysis, relating to whether claims as a whole are directed to judicial exceptions or not.
In response, Applicant’s argument is not persuasive. As discussed above over the 35 U.S. C. 101 analysis, element a) through e) are all directed to abstract ideas and element f) is classified as insignificant extra-solution activity. Therefore considered alone or as a whole, claim 33 and other claims are directed to judicial exceptions.
In the Appeal Brief, Applicant argued (page 7, last para through page 8, 1st para) that “the Examiner has incorrectly determined that elements (b) and (d) of claim 33 recite mathematical concepts and has therefore concluded that these elements recite abstract ideas. As a result, the Examiner's analysis of whether the remaining elements reflect an improvement to another technology is faulty as it is erroneously based on an inaccurate list of remaining elements”. Applicant further argues that “under the USPTO's own Guidance on Subject Matter Eligibility for Example 48, element (e) of claim 33 should likewise be considered an additional element and not a judicial exception, since the human mind could not complete element (e) of claim 33”. Applicant’s argument refers to Step 2A/Prong Two in the 101 analysis, relating to whether claims are integrated into a practical application or not due to a technical improvement.
In response, Applicant maybe referring Example 48 claim 2. Under Step 2A/Prong Two, in Example 48 claim 2, steps (f) and (g) allow the claim to reflect the improvement discussed in the disclosure by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then synthesized into separate speech waveforms in the time domain and converted into a mixed speech signal, excluding audio from the undesired source. The claimed invention solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training. These steps reflect the improvement described in the disclosure. Accordingly, the claim is directed to an improvement to existing computer technology or to the technology of speech separation, and the claim integrates the abstract idea into a practical application.
However, as discussed above over the 101 analysis, the instant element b) (“analyzing, for each of the plurality of windows, aligned reads from the nucleotide sequence for a plurality of statistical features, wherein the plurality of statistical features for each of the plurality of windows are stored in the corresponding data structure associated with the corresponding window”) recites data analysis on aligned sequence and acquires statistical features. This operation reads on mathematical calculations and is hence classified into an abstract idea of mathematical concepts. The acquired statistical features are stored in the data structure, which reads on a data manipulation. Therefore this step is also classified into an abstract idea of mental processes;
The instant element d) (“determining, using a trained machine learning algorithm, a presence of a genetic feature in the nucleotide sequence based on the statistical features stored in the data structure associated with the corresponding window of the plurality of windows and based on the candidates for genetic feature breakpoints, wherein the genetic feature is a structural variant selected from the group consisting of an insertion, a deletion, and an inversion”) reads on data analysis using mathematics for example a linear regression algorithm, therefore the step equates to a mathematical operation with inputs and outputs. Hence this step is classified into an abstract idea of mathematical concepts.
Element e) of claim 33 recites data manipulations, which can be achieved in human mind. Therefore the element e) of claim 33 is classified into an abstract idea of mental processes.
It seems unfair that similar technologies applied to different fields, one is 35 USC 101 eligible and one is not. However that is the reality. Waveform of speech is a data format human mind is not equipped to read and to analyze, while sequence alignments and mapping are traditionally readable and understandable (and then judgable) to the human mind.
Hence, the Example 48 claim 2 and the instant claim 33 is not a good comparison.
Furthermore, Applicant argue that claim 33 is “directed to an improvement to another technology, namely identifying genetic features” (page 7, last para). Since the claim 33 does not recite limitation like technology improvement, Applicant reads specification into claims. In response, Applicant’s arguments are not persuasive. “It is improper to import claim limitations from the specification” (MPEP §2111.01.II)
Even if a better data analysis is admitted, claim 33 results in a process of producing unified genetic feature information and excluding the nucleotide sequence and the plurality of statistical features from the unified genetic feature information; and transmitting the unified genetic feature information for storage. These two operations are classified into abstract ideas of mental processes. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
The comparison of Example 48 to claim 33 is not a fair comparison. Example 48 does recite additional elements that beyond data analysis; wherein claim 33 stays in data analysis and data manipulation. Element (f) is classified into an insignificant extra-solution activity. No additional element in claim 33 applied the judicial exception, captured or reflected the judicial exceptions.
In the Appeal Brief, Applicant argued (page 8, 2nd para through page 10, 2nd para) that “it is clear that elements (b) and (d) of claim 33 are not directed to mathematical concepts because these elements merely involve a judicial exception but do not recite the exception. As noted in MPEP 2106.04(a)(2), "[a] claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. V. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017)".
In response, Applicant’s arguments are not persuasive. Applicant has not argued which element that does not involve math. Although mathematical relationships, mathematical formulas or equations, or mathematical calculations are not explicitly recited in claim 33, the statistical features of element (b) is implicit for a mathematical formula. Here the aligned read sequences are the input at the left side of the formula, and features are at the right side as results or outputs. “A plurality of statical features” suggest the statistical functions applied to the inputs.
Similarly, “a trained machine learning algorithm” to identify a genetic feature of element (d) also imply a mathematical formula. Here the features are at the left side of the formula as inputs for the formula and the “presence of a genetic feature” is at the right side of the formula as result. “A trained machine learning algorithm” is the function applied to the inputs.
In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989) “It is of no moment that the algorithm is not expressed in terms of a mathematical formula. Words used in a claim operating on data to solve a problem can serve the same purpose as a formula.”
Additionally, the trained machine learning algorithm is recited in high generality, which reads on mathematical concepts. Alternatively, that trained machine learning algorithm is recited as “just apply it” analogous to Example 47 claim 2.
In Example 47 claim 2, “discretizing” in step (b) may be performed by processes including rounding, binning or clustering continuous data, which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. “Detecting” in step (d) encompasses observing a data set and performing an evaluation to identify anomalous data. “Analyzing” in step (e) encompasses making a determination about detected anomalies. Such mental observations or evaluations fall within the “mental processes” grouping of abstract ideas. In step (c), the backpropagation and gradient descent algorithms are mathematical calculations. Because the recited “training” explicitly recites performing mathematical calculations, the limitation falls within the “mathematical concepts” grouping of abstract ideas.
Therefore, for similar reasons, both element b) and element d) of claim 33 are directed to abstract idea of mathematical concepts.
In the Appeal Brief, Applicant argued (page 9, 3rd para through page 10, 3rd para) using Example 47 and 39, and drew the conclusion “it is clear that the references to statistical features in element (b) and to the use of a trained machine learning algorithm to identify a genetic feature in element (d) do not rise to the level of reciting judicial exceptions in either case”.
In response, Applicant’s arguments are not persuasive. Applicant presented the situation: “Just as the claim in Example 47 is deemed to recite a judicial exception due to its recitation of specific algorithms while the claim in Example 39 merely involves a judicial exception because it only contains passing references to broad features that are loosely connected to mathematical concepts”.
The conclusion about instant claims is erroneous. Example 39 does not recite judicial exceptions. Instant claims are more like Example 47 claim 2, but different from Example 47 claim 3. Example 47 claim 2 recites judicial exceptions but does not integrated into a practical application; wherein Example 47 claim 3 recites judicial exceptions, and also recites additional elements that integrate the judicial exceptions into a practical application.
In the Appeal Brief, Applicant argued (page 10, 3rd para through page 12, 1st para) that “Claim 33 Improves the Technical Field of Identifying Genetic Features”. Applicant further argued “the specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art," and “identifying genetic features, specifically identifying genetic features in genomic data, has several major challenges that must be overcome which are disclosed in the Specification and are well-known to those skilled in the art.”
In response, Applicant’s arguments are not persuasive. Applicant does not recite any feature to manage tremendous amount of genetic data in the claims. Again, Applicant seems wanted Examiner to read the specification into the claims. Again, “It is improper to import claim limitations from the specification” (MPEP §2111.01.II).
Even if large amount of sequencing data is analyzed under one BRI, identifying genetic features from multiple sequence alignments of next generation sequencing reads on data analysis. It is part of the judicial exceptions, but not an technological improvement. True “it is difficult to accurately identify important genetic variants within large quantities of data while at the same time identifying unique elements to be further investigated” (page 11, 2nd para), but to identify important genetic variants reads on data analysis and to generate new data from existing data. “Difficult” is not the concern of 101 analysis. The core issue lies in that Applicant does not recite additional elements to capture and to reflect the better data analytical results.
While there is no actual step of processing large amounts of genomic data, for the record, it is important to explain that analyzing a lot of data does not augment the steps being performed to analyze the data, which are abstract ideas. Computations on a lot of data 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).
In the Appeal Brief, Applicant argued (page 11, last para) that “The genetic features are combined with prior reference information to produce unified genetic feature information which excludes the nucleotide sequence and the statistical features. The net effect of the recited procedures of claim 33 is to consolidate genomic information into a reduced format (see para. [0064] of the Specification) which is tailored to efficiently and accurately identify genetic variants within vast quantities of genomic data to identify unique elements for further investigation”.
In response, Applicant’s argument is not persuasive. Consolidate genetic information into a reduced format and removing unnecessary data (for the next step in data analysis) reads on data manipulation and data re-organization. It is a common practice the human mind is capable to perform and actually the human mind is doing that routinely. Therefore the argument reads on an abstract idea of mental processes. Again the problem with the argument of an alleged technology improvement lies in the fact that Applicant does not recite additional elements to capture and to reflect the judicial exceptions of data analysis.
In the Appeal Brief, Applicant argued (page 12, 2nd para through page 14, penultimate para) that “Claim 33 Mirrors the Allowable Claims of Example 48, Claim 2, of the July 2024 Subject Matter Eligibility Examples”.
In response, Applicant’s argument is not persuasive. The comparison between Example 48 claim 2and instant claim 33 is discussed over pages 7-8 above. Again, The side-by-side comparison of Example 48, Claim 2 vs instant claim 33 is not a fair comparison. Particularly, regarding the Example 48, Claim 2, “Transmitting” in (h) is insignificant extra-solution activities. Steps (f) and (g) allow the claim to reflect the improvement discussed in the disclosure by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then synthesized into separate speech waveforms in the time domain and converted into a mixed speech signal, excluding audio from the undesired source. The claimed invention solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training. These steps reflect the improvement described in the disclosure. Accordingly, the claim is directed to an improvement to existing computer technology or to the technology of speech separation, and the claim integrates the abstract idea into a practical application. Hence Example 48, Claim 2 step f) recites additional elements and such an additional element captures and reflects the judicial exceptions of speech data analysis. On the contrary, claim 33 step d) reads on data analysis using mathematics for example a linear regression algorithm, therefore the step equates to a mathematical operation with inputs and outputs. This step is classified into an abstract idea of mathematical concepts.
Again, the instant claims do not recite additional elements to capture and to reflect the judicial exceptions.
In the Appeal Brief, Applicant argued (page 14, last para through page 15, penultimate para) that “Claim 33 and Example 48, Claim 2 Overcome Challenges in a Field of Technology”.
In response, Applicant’s argument is not persuasive. As discussed above, the Example 48, Claim 2 step f) is different from claim 33 step d). In Example 48, Claim 2 step f) a new speech waveform is synthesized. A challenge is overcome and speech with noise removed is provided. On the contrary, claim 33 step d) reads on data analysis, and new data is generated from the claimed data analysis work flow. It is not clear who or what benefited from such a result of identified genetic features. Furthermore, analyzing next generation sequence reads to identify possible genetic variants in samples are one of the heavily pursued area. We are not clear what, if exists instant claims different from competitors.
In the Appeal Brief, Applicant argued (page 15, last para through page 16, last para) that “Claim 33 and Example 48 Claim 2 Recite Additional Elements that Provide the Practical Application of Improving a Field of Technology”. Applicant’s argument refers to Step 2A/Prong Two, relating to whether claims are integrated into a practical application due to a technological improvement.
In response, Applicant’s argument is not persuasive. As discussed above, the Example 48, Claim 2 step f) is different from claim 33 step d). In Example 48, Claim 2 step f) a new speech waveform is synthesized. Such a process is not achievable in human mind. Hence Example 48, Claim 2 steps (f) and (g) allow the claim to reflect the improvement discussed in the disclosure by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then synthesized into separate speech waveforms in the time domain and converted into a mixed speech signal, excluding audio from the undesired source. The claimed invention solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training.
These steps reflect the improvement described in the disclosure. Accordingly, the claim is directed to an improvement to existing computer technology or to the technology of speech separation, and the claim integrates the abstract idea into a practical application.
On the other hand, instant claim 33 does recite additional elements that are directed to insignificant extra-solution activities. e.g., data input and output. Therefore, there is no additional elements that rely upon and capture the technical improvement in a meaningful way in instant claim 33.
Claim 33 is not integrated into a practical application due to a technological improvement.
It seems the elements are similar in the two claims compared side-by-side. However, the waveform of speech is not a traditional data form handed by the human mind, while the sequence mapping (aka sequence alignments) is traditionally handed and judged by human mind.
In the Appeal Brief, Applicant argued (page 17, 1st para) that claim 33 is eligible under Step 2B.
In response, Applicant’s argument is not persuasive. Instant claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception because it is routine and conventional to perform the acts of data transmitting, data analyzing and data modeling. Particularly, for the two additional elements identified:
(f) Transferring the unified genetic feature information for storage; and
“A programmed computer” (claim 38).
Transferring the unified genetic feature information for storage is an insignificant extra-solution activity. “A programmed computer” is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Generally speaking, analyzing the next generation sequencing reads for possible mutations/variations, is one of the most explored topic in the field of biomedical research in the recent years. Yet we don’t see any non-conventional additional elements that applied, captured and reflected the data analytical results in the instant claims.
Applicant further argue (page 17, 2nd para through page 18, 1st para) that claim 33 improves the functioning of a computer. Applicant’s argument refers to Step 2A/Prong Two, relating to whether claims are integrated into a practical application due to an improvement to the functioning of a computer or other technological improvement. Applicant then argue that
In response, Applicant’s argument is not persuasive. First the claims does not recite the data size and computing memory consumption. Apparently Applicant reads the specification into claims again. “It is improper to import claim limitations from the specification” (MPEP §2111.01.II)
Second even if instant invention had data smartly with significantly reduced size, it is common to remove unnecessary data in a data analytical work-flow, so the computer program can run more efficiently.
While there is no actual step of processing large amounts of genomic data, for the record, it is important to explain that analyzing a lot of data does not augment the steps being performed to analyze the data, which are abstract ideas. Computations on a lot of data 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).
For the above reason, the 101 rejection is maintained.
Claim Rejections - 35 USC § 103
Upon further consideration, this rejection is newly applied.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 33, 38, 40-45, 47-49, 51-55, 198, 201-203, 205, and 208-209 are rejected under 35 U.S.C. 103 as being unpatentable over Michaelson et al.:(“forestSV: structural variant discovery through statistical learning,” Nature Methods, 2012; Public before April 27, 2016. Cited on the 8/24/2023 IDS) and Danecek et al.: (“The variant call format and VCFtools,” Bioinformatics, 2011; PMCID: PMC3137218. Public before April 27, 2016. Newly cited).
Claim 33 is directed to a method for detecting a genetic feature in a nucleotide sequence. Regarding claim 33, Michaelson provides (page 819. Title and Abstract) “structural variant discovery through statistical learning” and “Detecting genomic structural variants from high-throughput sequencing data…”, which teaches detecting one or more genetic features in a nucleotide sequence.
Michaelson provides (page 819. Col 2, 3rd para) “The rows of X (and consequently the elements of Y) correspond to overlapping 100 bp windows of the genome that we call ‘subjects’ here” and (page 819. Col 2, 1st para) “feature matrix X”, which teaches (a): building, for each of a plurality of windows applied to the nucleotide sequence, a corresponding data structure, a per-window feature-matrix row. A row/record is a data structure corresponding to a genomic window.
Michaelson provides (page 819. Col 2, 3rd para) “The rows of X (and consequently the elements of Y) correspond to overlapping 100 bp windows of the genome that we call ‘subjects’ here”, which Directly maps to associating each feature row with a window. Hence it teaches a data structure associated with a corresponding window.
However, Michaelson does not disclose a “corresponding data structure” with a unique pointer that pointes to the corresponding window. Danecek discloses (page 2157, Fig.1) VCF file, a data structure associated with a corresponding window with a unique pointer (in the reference: “index”). Danecek further provides (page 2158, col 1, last para through col 2, 1st para) “Compression and indexing Given the large number of variant sites in the human genome and the number of individuals the 1000 Genomes
Project aims to sequence, VCF files are usually stored in a compact binary form, compressed by bgzip, a program which utilizes the zlib-compatible BGZF library. Files compressed by bgzip can be decompressed by the standard gunzip and zcat utilities. Fast random access can be achieved by indexing genomic position using tabix, a generic indexer for TAB-delimited files”, which teaches a unique pointer that index the specific window contains the VCF file. Because the “pointer” recited in the claim reads on the “indexer” here, and a VCF file is a data structure.
Michaelson provides (page 820. Col 1, Figure 1 legend) “BAM files are given as input. The data from the BAM files are used to construct a new feature matrix X’”, which teaches (b) deriving the feature matrix from BAM data by genomic windows, because BAM files contain aligned sequencing reads.
Michaelson provides (page 822. Col 1, 3rd para) “The columns of the matrix are the features themselves (15 total), evaluated at each of the three scales (for a total of 45 columns)”, which teaches multiple features per window row (data structure).
Michaelson discuss split-read signals (page 819, col 1, 2nd para) as “other methods rely on split-read signals (reads that span a breakpoint)”, and uses segmentation to generate final SV calls (page 819, col 1, 3rd para, “The final variant calls are then generated by a simple segmentation routine that merges consecutive subjects of the same predicted class into a single event”), which suggests (c) breakpoint-related signals and event boundaries. Although the exact phrase “candidate breakpoints” is not quoted from Michaelson, segmentation of window-level classes into SV events necessarily identifies transition boundaries/candidate breakpoints. Breakpoints are inherent part of the genetic features.
Michaelson provides (page 819. Col 1, 1st para) “We trained a Random Forest classifier to partition this space in a way that optimizes the classification of deletions, duplications”, and (page 820, col 1, Figure 1 legend) “The classifier provides a mapping from X′ to predicted structural variant classes, Y′”, which teaches (d) a trained ML classifier using the feature matrix to determine SV classes/calls.
Michaelson expressly teaches deletions, which is one member of the claimed “structural variant” group.
Michaelson provides (page 819. Col 1, 3rd para) “The final variant calls are then generated by a simple segmentation routine that merges consecutive subjects of the same predicted class into a single event”, Michaelson teaches (e) merging window-level SV predictions.
Michaelson Does not teach a unified format for SV calls.
Danecek provides (page 2156, section “Abstract”) “The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations”. Danecek teaches a standard unified variant format with annotations, such as reference-based annotation (page 2167, Fig 1) .
Michaelson teaches merging window-level SV predictions. Danecek teaches (f) a standard unified variant format with annotations. Combining these supports outputting merged SV calls with reference/annotation information in VCF-like format.
Danecek describes (page 2167, Fig 1) VCF as storing variant/polymorphism data and annotations; no raw BAM reads or the full per-window feature matrix in the VCF file. VCF output normally reports variant coordinates, alleles/type, quality and annotations, not the raw nucleotide sequence or all intermediate statistical features.
Danecek provides (page 2156, section “Abstract”) “VCF is usually stored in a compressed manner and can be indexed for fast data retrieval”, which teaches (f) Outputting/storing/transmitting called variant information would have been conventional in bioinformatics pipelines.
Regarding claim 38, Michaelson provides (page 819. Col 1, 1st para) “We trained a Random Forest classifier to partition this space in a way that optimizes the classification of deletions, duplications”, and (page 820. Col 1, Figure 1 legend) “BAM files are given as input. The data from the BAM files are used to construct a new feature matrix X’”. Programmed-computer implementation is inherent/expressly suggested by software pipelines.
Regarding claim 40, Michaelson provides (page 819. Col 1, 1st para) “We trained a Random Forest classifier to partition this space in a way that optimizes the classification of deletions, duplications”, which teaches the trained ML algorithm comprises random forest.
Regarding claims 41-45, 48, and 203, Michaelson provides (page 819. Col 1, 3rd para) “the rows of X (and consequently the elements of Y) correspond to overlapping 100-base-pair (bp) windows of the genome that we call ‘subjects’ here” and (page 822, col 1, 3rd para) “the next row in the matrix is offset by 50 bp, and the features are evaluated again, and so on”, which teaches a 50-bp offset and windowed analysis. Exact “does not include portions outside the moving window” would be routine alternatives, but Michaelson itself uses overlapping windows.
Regarding claim 47, Michaelson provides (page 822, col 1, 3rd para) “the columns of the matrix are the features themselves (15 total), evaluated at each of the three scales (for a total of 45 columns)”, which teaches at least five statistical features.
Regarding claim 49, Michaelson provides (page 822, col 1, 2nd para) “the weights may be logical (binary) or continuous. In the work described here, weights are extracted from the BAM files and convey information on such attributes as outlier read pairs, base content, mapping quality, CIGAR operations, and mapping flags”, which suggests base content, CIGAR operations, mapping flags and strand-related features. Because the CIGAR operation is about alignment/match/mismatch, insertion/deletion and hard/soft clippings.
Regarding claim 51, "a whereby (wherein) clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited" (MPEP 2111.04 § I). Claim 51 is just descriptions of the intended confidence result of the method, so it doesn't have any patentable weight.
Regarding claim 52, "a whereby (wherein) clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited" (MPEP 2111.04 § I). Claim 51 is just descriptions of the intended accuracy, so it doesn't have any patentable weight.
Regarding claim 53, "a whereby (wherein) clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited" (MPEP 2111.04 § I). Claim 53 is just descriptions of the intended specificity result of the method, so it doesn't have any patentable weight.
Regarding claim 54, "a whereby (wherein) clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited" (MPEP 2111.04 § I). Claim 54 is just descriptions of the intended sensitivity result of the method, so it doesn't have any patentable weight.
Regarding claim 55, Michaelson discuss split-read signals (page 819, col 1, 2nd para) as “other methods rely on split-read signals (reads that span a breakpoint)”, and uses segmentation to generate final SV calls (page 819, col 1, 3rd para, “The final variant calls are then generated by a simple segmentation routine that merges consecutive subjects of the same predicted class into a single event”), which suggests (c) breakpoint-related signals and event boundaries. Although the exact phrase “candidate breakpoints” is not quoted from Michaelson, segmentation of window-level classes into SV events necessarily identifies transition boundaries/candidate breakpoints. Hence start/end points are inherent part of the genetic features.
Regarding claims 201-202, Michaelson provides (page 822, col 1, Figure 1 legend) “the data from the BAM files are used to construct a new feature matrix X′. The classifier provides a mapping from X′ to predicted structural variant classes, Y′”.
Michaelson’s classifier applies learned rules to features rather than direct base-by-base comparison to training sample bases, which teaches determine/analyze statistical features without comparing bases of query sequence to bases of training samples.
Regarding claim 205, Michaelson provides (page 819, col 1, Section Abstract) “Detecting genomic structural variants from high-throughput sequencing data”, which teaches the nucleotide sequence comprises genomic data.
Regarding claim 208, Michaelson provides (page 822, col 1, Figure 1 legend) “the data from the BAM files are used to construct a new feature matrix X′. The classifier provides a mapping from X′ to predicted structural variant classes, Y′”. Michaelson’s classifier applies learned rules to statistical features of derived statistics, that do not include base ordered sequence reads.
Regarding claim 209, Michaelson discuss split-read signals (page 819, col 1, 2nd para) as “other methods rely on split-read signals (reads that span a breakpoint)”, and uses segmentation to generate final SV calls (page 819, col 1, 3rd para, “The final variant calls are then generated by a simple segmentation routine that merges consecutive subjects of the same predicted class into a single event”), which suggests (c) breakpoint-related signals and event boundaries. Although the exact phrase “candidate breakpoints” is not quoted from Michaelson, segmentation of window-level classes into SV events necessarily identifies transition boundaries/candidate breakpoints. Breakpoints are inherent part of the genetic features.
It would have been prima facie obvious to a person of ordinary skill in art to modify Michaelson’s forestSV pipeline to use Danecek’s reference-profile/database information, and the structural-variant calls in the VCF format. The motivation would have been to use known bioinformatics pipeline practices for representing genomic-window results, to incorporate reference and annotation information useful for interpreting genetic features, and to store/communicate variant calls in a standardized format compatible with downstream tools.
One would reasonably expect success for the modification as Michaelson, and Danecek both concern next-generation sequencing analysis and variant detection/reporting; Michaelson already outputs merged SV calls, Danecek reference-aided output files, and the known standardized storage format for variant calls.
Claims 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Michaelson and Danecek, as applied to claims 33, 38, 40-45, 47-49, 51-55, 198, 201-203, 205, and 208-209 above, and in further view of Viovy et al.: ("Methods and Compositions for Assaying Mutations and/or Large Scale Alterations in Nucleic Acids and Their Uses in Diagnosis of Genetic Diseases and Cancers", US 20070161032 A1, 2007-07-12. Cited on the 10/24/2018 IDS).
With respect to claim 35, combined Michaelson and Danecek disclosed a method for detecting one or more genetic features in a nucleotide sequence, Michaelson and Danecek do not disclose wherein the genetic feature is from about 6 base pairs (bp) to about 50 bp in length. Viovy discloses wherein the genetic feature is from about 6 base pairs (bp) to about 50 bp in length (mutations may range from 10 bps to 10 kbps; paragraphs [0165]-[0166], [0169]).
With respect to claim 36, combined Michaelson and Danecek disclosed a method for detecting one or more genetic features in a nucleotide sequence, Michaelson and Danecek do not disclose wherein the genetic feature is from about 50 base pairs (bp) to about 500 bp in length. Viovy discloses wherein the genetic feature is from about 50 base pairs (bp) to about 500 bp in length (mutations may range from 10 bps to 10 kbps; paragraphs [0165]-[0166], [0169]).
With respect to claim 37, combined Michaelson and Danecek disclosed a method for detecting one or more genetic features in a nucleotide sequence, Michaelson and Danecek do not disclose wherein the genetic feature is greater than about 500 base pairs in length. Viovy discloses wherein the genetic feature is from longer than 500 base pairs (bp) in length (mutations may range from 10 bps to 10 kbps; paragraphs [0165]-[0166], [0169]).
It would have been Prima Facie Case of Obviousness “teaching-to-modifying” (“Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention” (MPEP § 2143 I.G.)) to one of ordinary skill in art at the time of the invention to modify the combined Michaelson and Danecek method for detecting one or more genetic features in a nucleotide sequence, to include detecting genetic features in different size ranges, as taught by Viovy, in order to provide an improved method for detecting alterations in a nucleic acid segment of various length.
On would reasonably expect success, Because Michaelson, Danecek and Viovy are all about detecting alterations in a nucleic acid segment, and Michaelson and Danecek already applied their forestSV model to call structural variants successfully.
Claims 56 and 58-60 are rejected under 35 U.S.C. 103 as being unpatentable over Michaelson and Danecek, as applied to claims 33, 38, 40-45, 47-49, 51-55, 198, 201-203, 205, and 208-209 above, and in further view of Leggett et al.: ("Identifying and classifying trait linked polymorphisms in non-reference species by walking colored de Bruijn graphs." PloS one 8.3 (2013): e60058. Newly cited).
Regarding claims 56 and 58-60, neither Michaelson nor Danecek teaches graph alignment. Leggett teaches graph sequence alignment (page 2, Fig. 1), which teaches graph aligned reads (page 2, Fig. 1); regions with no alternative paths (page 2, Fig. 1A upper part); regions with no bubbles (page 2, Fig. 1A upper part); and regions with at least one alternative path or bubble (page 2, Fig. 1A lower part, 1B, 1C).
It would have been a Prima Facie Case of Obviousness “teaching-to-modifying” (“Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention” (MPEP § 2143 I.G.)) to one of ordinary skill in art at the time of the invention to modify the combined Michaelson and Danecek method for detecting one or more genetic features in a nucleotide sequence, to include detecting genetic features based on graph alignment, as taught by Leggett in order to provide an improved method for extracting statistical features from alignments.
On would reasonably expect success, Because Michaelson, Danecek and Leggett are all about detecting alterations in a nucleic acid segment, and Michaelson and Danecek already extract statistical features based on BAM formatted alignments successfully.
Conclusion
No claims are allowed.
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/GL/
Patent Examiner
Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
Prosecution Insights