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 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.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are under examination.
Claims 1-20 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 63/411581, filed 09/29/2022. Accordingly, each of claims 1-20 are afforded the effective filing date of the 09/29/2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 05/16/2024 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
The listing of references in the specification is not considered to be an IDS as it is not a proper IDS. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered. See pages 1-3 of the specification in the instant application.
Drawings
The Drawings submitted 09/28/2023 are accepted.
Claim Rejections - 35 USC § 112
35 U.S.C. 112(b)
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.
Claims 7-9, 12-14, and 16-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7-9, 12-14, and 16-20, limitation, recites “CNV Caller Guide”. The claims are indefinite as the term “CNV Caller Guide” is not defined in the claims or specification. The specification and drawings provide examples of what a CNV Caller guide may contain but it is not clearly defined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to system, non-transitory computer readable media, and a method, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claims 1, 10 and 15:
process sequence data that comprises: a start of a copy number variation (CNV) breakpoint, an end of the CNV breakpoint, wherein a location of the start of the CNV breakpoint in the sequence data is known, and wherein a location of the end of the CNV breakpoint in the sequence data is unknown; and based on the processing,
determine the location of the end of the CNV breakpoint in the sequence data.
Dependent claims 7, 12, and 16:
process, by a trained CNV Caller Guide, the anchor sequence and a subject candidate sequence as inputs; and
generate, from the trained CNV Caller Guide, an output that specifies whether the end of the CNV breakpoint is located on the subject candidate sequence.
Dependent claims 9, 13, and 17:
process the anchor sequence through the trained encoder and the trained multi-layer perceptron of the trained CNV Caller Guide; and
generate, from the trained CNV Caller Guide, a learned representation of the anchor sequence.
Dependent claim 14:
determine, by the trained CNV Caller Guide, a similarity between the anchor sequence and a subject candidate sequence of the plurality of candidate sequences by comparing the learned representation of the anchor sequence against the learned representation of the subject candidate sequence.
Dependent claim 18:
the trained CNV Caller Guide determines a similarity between the anchor sequence and the subject candidate sequence by comparing the learned representation of the anchor sequence against the learned representation of the subject candidate sequence.
Dependent claims 2-6, 8, 11, and 19-20 recite further steps that limit the judicial exceptions in independent claims 1, 10, and 15 and, as such, also are directed to those abstract ideas. For example, claims 2-4 further limit the sequence data of claim 1, claims 5-6 further limit the CNV breakpoint of claim 1, claim 8 further limits the CNS caller guide of claim 7, claim 11 further limits the sequence data of claim 10, and claims 19-20 further limit the distance score of claim 18.
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
The claims recite mathematical concepts of process sequence data, determine the location of the end of the CNV breakpoint, process, by a trained CNV Caller Guide, the anchor sequence and a subject candidate sequence as inputs , generate, from the trained CNV Caller Guide, an output, process the anchor sequence through the trained encoder and the trained multi-layer perceptron, generate, from the trained CNV Caller Guide, a learned representation, and determine, by the trained CNV Caller Guide, a similarity.
Therefore, claims 1, 10, and 15 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
The claims include non-abstract computing elements. For example, independent claim 1 includes a computing system and claim 10 includes non-transitory computer readable storage medium.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons.
Steps directed to additional non-abstract elements of “a computing system and non-transitory computer readable storage medium do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claims 1 and 10 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [118-126]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
A. Claim(s) 1, 10, and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nord et al. (Nord, Alex S., et al. "Accurate and exact CNV identification from targeted high-throughput sequence data." BMC genomics 12.1 (2011), newly cited).
Claim 1 is directed to a system comprising: at least one processor; and a non-transitory computer readable storage medium storing instructions.
Nord discloses all analysis was done using the R platform http://www.r-project.org with custom scripts, which are available at request [p. 9, col. 1, par. 2] which reads on a system as software inheritably needs a system (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium).
Claim 10 is directed to non-transitory computer readable storage medium.
Nord discloses all analysis was done using the R platform http://www.r-project.org with custom scripts, which are available at request [p. 9, col. 1, par. 2] which reads on an algorithm (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium).
Claims 1, 10, and 15 are directed to process sequence data that comprises: a start of a copy number variation (CNV) breakpoint, an end of the CNV breakpoint, wherein a location of the start of the CNV breakpoint in the sequence data is known, and wherein a location of the end of the CNV breakpoint in the sequence data is unknown; and based on the processing, determine the location of the end of the CNV breakpoint in the sequence data.
Nord discloses accurate and exact CNV identification from targeted high-throughput sequence data [title]. Nord further discloses for CNVs where at least one breakpoint is within targeted sequence, exact CNV breakpoints can be identified [abstract]. Nord also discloses using a test dataset of regions that contribute to inherited breast and ovarian cancer susceptibility, we identified 10 mutations (7 known to be pathogenic and 3 benign), localizing 4 mutations to exact genomic breakpoints [p. 4, co. 2, par. 3].
B. Claim(s) 1-6, 10-11, and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhao et al. (Zhao, Min, et al. "Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives." BMC bioinformatics (2013), newly cited).
Claim 1 is directed to a system comprising: at least one processor; and a non-transitory computer readable storage medium storing instructions.
Zhao discloses split read AI based tools [p. 4, table 1] which reads on software which inheritably needs a system (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium).
Claim 10 is directed to non-transitory computer readable storage medium.
Zhao discloses split read AI based tools [p. 4, table 1] which reads on software which inheritably needs a system (e.g., instructions stored on a tangible and/or non-transitory computer readable storage medium).
Claims 1, 10, and 15 are directed to process sequence data that comprises: a start of a copy number variation (CNV) breakpoint, an end of the CNV breakpoint, wherein a location of the start of the CNV breakpoint in the sequence data is known, and wherein a location of the end of the CNV breakpoint in the sequence data is unknown; and based on the processing, determine the location of the end of the CNV breakpoint in the sequence data.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title]. Zhao further discloses split read-based approach where the method starts from read pairs in which one read from each pair is aligned to the reference genome uniquely while the other one fails to map or only partially maps to the genome [p. 8, col. 1, par. 3]. Zhao also discloses those unmapped or partially mapped reads potentially provide accurate breaking points at the single base pair level for SVs/CNVs [p. 8. col. 1, par. 3]. Zhao further discloses SR methods split the incompletely mapped reads into multiple fragments, where the first and last fragments of each split read are then aligned to the reference genome independently [p. 8, col. 1, par. 3]. Zhao also discloses this remapping step therefore provides the precise start and end positions of the insertion/deletion events [p. 8, col. 1, par. 3].
Claim 2 is directed to the system of claim 1, wherein the sequence data has an anchor sequence.
Zhao discloses SR methods start from read pairs in which one read from each pair is aligned to the reference genome uniquely while the other one fails to map or only partially maps to the genome [p. 8, col. 1, par. 3].
Claim 3 is directed to the system of claim 2, wherein the sequence data has a plurality of candidate sequences.
Zhao also discloses those unmapped or partially mapped reads potentially provide accurate breaking points at the single base pair level for SVs/CNVs [p. 8. col. 1, par. 3].
Claim 4 is directed to the system of claim 3, wherein candidate sequences in the plurality of candidate sequences are downstream to the anchor sequence.
Zhao discloses Pindel is the first SR-based method to identify breakpoints of large deletions (1 bp - 10 Kb) and medium-sized insertions (1 - 20 bps) [p. 8, col. 1 , par. 4]. Zhao further discloses starting with unmapped or partially mapped reads from read pairs, this tool utilizes a string match approach to search unique substrings in minimum (the 5’ end of the input reads) and maximum locations (the 3’ end of the input reads) in which both could be completely mapped to the genome [p. 8, col. 1, par. 4]. The anchor point and the direction dictates which direction to look for the unmapped candidate read and therefore reads on downstream of the anchor.
Claim 5 is directed to the system of claim 2, wherein the start of the CNV breakpoint is located on the anchor sequence.
Zhao discloses SR methods start from read pairs in which one read from each pair is aligned to the reference genome uniquely while the other one fails to map or only partially maps to the genome [p. 8, col. 1, par. 3] which reads on the start breakpoint being in the anchor.
Claim 6 is directed to the system of claim 3, wherein the end of the CNV breakpoint is located on one or more of the plurality of candidate sequences.
Zhao further discloses SR methods split the incompletely mapped reads into multiple fragments, where the first and last fragments of each split read are then aligned to the reference genome independently [p. 8, col. 1, par. 3]. Zhao also discloses this remapping step therefore provides the precise start and end positions of the insertion/deletion events [p. 8, col. 1, par. 3].
Claim 11 is directed to the non-transitory computer readable storage medium of claim 10, wherein the sequence data has an anchor sequence and a plurality of candidate sequences.
Zhao discloses SR methods start from read pairs in which one read from each pair is aligned to the reference genome uniquely while the other one fails to map or only partially maps to the genome [p. 8, col. 1, par. 3]. Zhao further discloses those unmapped or partially mapped reads potentially provide accurate breaking points at the single base pair level for SVs/CNVs [p. 8. col. 1, par. 3].
Claim Rejections - 35 USC § 103
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.
Claim(s) 7-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao as applied to claims 1, 10, and 15 above in view of Wang et al.(Wang, Shaoqiang, et al. "svBreak: a new approach for the detection of structural variant breakpoints based on convolutional neural network." BioMed Research International 2022.1 (2022), newly cited)..
Claims 7, 12, and 16 are directed to process, by a trained CNV Caller Guide, the anchor sequence and a subject candidate sequence as inputs; and generate, from the trained CNV Caller Guide, an output that specifies whether the end of the CNV breakpoint is located on the subject candidate sequence.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title] but is silent on a trained CNV Caller Guide.
However, Wang discloses svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network [title]. Wang also discloses starting with an initial input of a reference genome and a sequencing sample [p. 3, col. 1, par. 2]. Wang further discloses the four primary steps include (1) extracting twelve distinctive features related to SV breakpoints, (2) establishing a CNN model for the analysis of the extracted features, (3) training the neural network by using labeled SV breakpoints from synthetic or real NGS data, and (4) predicting the seven common types of SV breakpoints based on the trained CNN model [p. 3, col. 1, par. 2]. Wang also discloses determining which type of SV breakpoint the genome site t belongs to depends on the largest score among the seven scores where the seven scores display zeros, then the genome site t is not an SV breakpoint [p. 3, col. 2, par. 2- p. 4, col. 1, par. 1].
Claim 8 is directed to the system of claim 7, wherein the trained CNV Caller Guide comprises a trained encoder and a trained multi-layer perceptron.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title] but is silent on a trained CNV Caller Guide comprises a trained encoder and a trained multi-layer perceptron.
However, Wang discloses the CNN model is a multilayer perceptron with a deep learning model [p. 4, col. 1, par. 2]. Wang discloses a trained CNN model is a multilayer perceptron, a trained autoencoder would be a straightforward alternative to the CNN that the skilled person would use in accordance to circumstances.
Claims 9, 13, and 17 are directed to further storing instructions that, when executed by the at least one processor, cause the system to: process the anchor sequence through the trained encoder and the trained multi-layer perceptron of the trained CNV Caller Guide; and generate, from the trained CNV Caller Guide, a learned representation of the anchor sequence.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title] but is silent on a learned representation of the anchor sequence.
However, Wang discloses svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints [abstract] which reads on a learned representation of the anchor sequence.
Claims 14 and 18 are directed to determine, by the trained CNV Caller Guide, a similarity between the anchor sequence and a subject candidate sequence of the plurality of candidate sequences by comparing the learned representation of the anchor sequence against the learned representation of the subject candidate sequence.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title] but is silent on a similarity.
However, Wang discloses its feature values are mainly obtained from the comparison information of split reads and the insertion distance of paired-end sequencing reads [p. 7, col. 1, par. 2] which reads on a similarity.
Claim 19 is directed to the computer-implemented method of claim 18, wherein the trained CNV Caller Guide measures the similarity using a distance score.
Zhao discloses computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives [title] but is silent on a similarity.
However, Wang discloses its feature values are mainly obtained from the comparison information of split reads and the insertion distance of paired-end sequencing reads [p. 7, col. 1, par. 2] which reads on a similarity.
Claim 20 is directed to the computer-implemented method of claim 19, wherein: when the distance score is below a distance threshold, the trained CNV Caller Guide generates the output that specifies that the end of the CNV breakpoint is located on the subject candidate sequence; or wherein, when the distance score is above the distance threshold, the trained CNV Caller Guide generates the output that specifies that the end of the CNV breakpoint is not located on the subject candidate sequence.
Wang also discloses determining which type of SV breakpoint the genome site t belongs to depends on the largest score among the seven scores where the seven scores display zeros, then the genome site t is not an SV breakpoint [p. 3, col. 2, par. 2- p. 4, col. 1, par. 1].
In regard to claim(s) 7-9 and 12-20, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zhao with Wang as they both disclose computational tools for copy number variation (CNV) detection. The motivation would have been to include the CNN model with a multilayer perceptron of Wang with the methods of Zhao to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Replacing the a trained CNN of Wang with a trained autoencoder would be a straightforward alternative to the CNN that the skilled person would use in accordance to circumstances. One could have therefore combined the elements as claimed by the known methods of Zhao and Wang, and that in combination, each element merely would have performed the same function as it did separately for a predictable result.
Conclusion
No claims are allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dawn M. Bickham whose telephone number is (703)756-1817. The examiner can normally be reached M-Th 7:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600
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