DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 5-8 and 13 are cancelled.
Claims 1-4, 9-12, and 14-17 are pending.
Claims 9-12 and 14-16 objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend from any other multiple dependent claim. See MPEP § 608.01(n).
Claims 1-4 and 17 are rejected.
Claims 1 and 17 are objected to for minor informalities.
Priority
Applicant’s claim for the benefit of a prior-filed application, PCT/AU2021/051220 filed 2021 Oct. 20, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Acknowledgment is made of applicant’s claim for foreign priority to AU2020903839 filed 23 Oct. 2020 under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Accordingly, the effective filing date of the claimed invention is 23 Oct. 2020.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 16 Aug. 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application fails to comply with the requirements of 37 CFR 1.821 - 1.825 because it includes sequences that fall within the definitions of 37 CFR 1.821(a) in FIG. 3 and Fig. 4 but it does not contain a "Sequence Listing" as a separate part of the disclosure or a CRF of the “Sequence Listing.”.
Required response - Applicant must provide:
A "Sequence Listing" part of the disclosure; together with
An amendment specifically directing its entry into the application in accordance with 37 CFR 1.825(a)(2);
A statement that the "Sequence Listing" includes no new matter as required by 37 CFR 1.821(a)(4); and
A statement that indicates support for the amendment in the application, as filed, as required by 37 CFR 1.825(a)(3).
If the "Sequence Listing" part of the disclosure is submitted according to item 1) a) or b) above, Applicant must also provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
If the "Sequence Listing" part of the disclosure is submitted according to item 1) c) or d) above, applicant must also provide:
A CRF in accordance with 37 CFR 1.821(e)(1) or 1.821(e)(2) as required by 1.825(a)(5); and
A statement according to item 2) a) or b) above.
Drawings
The drawings filed 21 April 2023 are objected to because:
the view numbers for Fig. 1-5 are not larger than the numbers used for reference characters. 37 CFR 1.84(u)(2) states the view numbers must be larger than the numbers used for reference characters.
the drawings fail to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: #106; and
the drawings fail to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: #202 and 203 in FIG. 2, #307 in FIG. 3, #405 and 404 in FIG. 4
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code at pg. 10, para. 1. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 9-12 and 14-16 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend from any other multiple dependent claim (claim 2 is a multiple dependent claim, from which multiple dependent claims 9-12 and 14-16 depend). See MPEP § 608.01(n). Accordingly, the claims 9-12 and 14-16 have not been further treated on the merits.
Claims 1 and 17 are objected to because of the following informalities:
Claim 1 recites “evaluating the trained machine learning model to the…”, which should recite “applying the trained machine learning model to the…” to increase clarity; and
Claim 17 recites “the computer system comprising: data memory…; a processor configured to”, which should recite “comprising: data memory…; and a processor configured to” to correct grammar.
Appropriate correction is required.
Claim Interpretation
Claim 3 recites “wherein the testing sequencing data is generated by a sequencer”. Claim 1, from which claim 3 depends, recites “receiving testing sequencing data comprising multiple unaligned testing reads”, and thus receives already generated reads. Therefore, the limitation of claim 3 is interpreted to define the process in which the testing sequencing data was previously generated. However, the claims do not recite a step of generating the testing sequencing data by a sequencer. See MPEP 2113 I, stating "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-4 and 17 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention.
Claims 1 and 17 recite “evaluating [applying] the trained machine learning model to the multiple testing segments to detect deletion in the testing sequencing data”. Claims 1 and 17 previously recite that the training data used to train the machine learning model comprises “unaligned training reads associated with gene sequences with deletions and gene sequences without deletion”.
MPEP 2161.01 I. states original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
In the instant case, the only example in the specification of how a trained neural network is applied to test sequences is at para. [0044]-[0049]. The specification at para. [0044]-[0049] provides examples of 7 read segments taken from a healthy genome (associated with gene sequences without deletion) and then two read segments corresponding to a deleted component of the healthy chromosome “2345” and “3456” corresponding to the deletion of “23456”. Applicant’s specification at para. [0040] discloses the output may be a disease indicator or the presence of deletion, and at para. [0049] then further explains that for the testing segment “2345”, the neural network provides a very high probability (0.99) indicating this region is overlapped with disease and for the testing segment “789” (a non-deleted segment) is given a low probability.
This does not disclose the trained machine learning model detects deletion in the testing sequence data; to the contrary, the “deleted” segment was necessarily contained in the testing sequencing data (given the “2345” was a test segment) and the model detected a sequence present in the test sequence data that may be deleted (e.g. is associated with a deletion) in some other genome. There is no other detailed description of the output of the model and/or how the model detects the presence of deletion from a test segment as claimed.
Furthermore, if the training data and labels in para. [0044]-[0049] are only intended to pertain to the embodiment in which a label is a “disease” rather than a “deletion” label, Applicant’s specification does not describe how the training data “comprising unaligned training reads associated with gene sequences with deletion and gene sequences without deletion” is intended to be generated to train the model in sufficient detail. The only other description regarding training data is at para. [0032], which merely states training reads are separated into two sets and labelled, with one set being associated with gene sequences with deletion and another set associated with gene sequences without deletion. There is no further detail (other than what’s cited above in [0044]-[0099] of the specification) regarding how certain reads are identified as being “associated with gene sequences with deletion” to inform how reads are ultimately labelled such that the neural network could be trained do detect the deletion in a subject using a single test read (corresponding to “the multiple testing segments) present in the subject, particularly given these reads are claimed as “unaligned”. Similarly, if Applicant intended the model to take the multiple segments of each test read as input (see 112(b) rejection below), there is no explanation of how different classifications for different reads of the test sequencing data might be combined to detect a deletion in the subject as claimed, given each read is labelled.
Last, given the invention is presumably novel, it is not well-known how the training set comprising reads associated with deletions and not associated with deletions is generated, labelled, and applied to allow for the detection of a deletion in a gene sequence using the neural network.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitation above recited in claims 1-4 and 17 to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Claim Rejections - 35 USC § 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 1-4 and 17 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.
Claims 1 and 17, and claims dependent therefrom, are indefinite for recitation of “unaligned training reads associated with gene sequences with deletions and gene sequences without deletion”. The metes and bounds of what reads would be considered “associated” with gene sequences with a deletion or no deletion is unclear, which could potentially encompass any read on the genome (e.g. associated with a gene sequence having deletion by being on the same genome), reads within some proximity of a deletion or no deletion, any reads from subject with a disease associated with gene deletions, etc. See MPEP 2173.05(b), stating some objective standard must be provided in order to allow the public to determine the scope of the claim. A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). A review of the specification does not serve to clarify the metes and bounds of the term. This raises further indefiniteness regarding the limitation “evaluating the trained machine learning model…to detect deletion in the testing sequencing data”; specifically, in light of the above, it is not clear if the machine learning model is configured to merely detect reads associated with gene sequences with deletion (e.g. classifying a read as associated with disease, which is associated with deletions), or if the machine learning model is trained to detect deletions within the reads themselves (i.e. detecting a deletion). Clarification is requested.
Claims 1 and 17, and claims dependent therefrom, are indefinite for recitation of “…training a machine learning model on the multiple segments”, and “…evaluating the trained machine learning model to the multiple testing segments”. Claims 1 and 17 previously recite splitting each unaligned training and testing read of multiple reads into multiple training and testing segments, respectively (i.e. there are multiple sets of segments for each of the training and test sets). As a result, it is not clear which set of multiple testing segments “the multiple segments” and “the multiple testing segments” are referring to. Dependent claim 2 also refers to “the training segments and the testing segments” which is indefinite for the same reason. For purpose of examination, the limitations of claim 1 are interpreted to refer to the multiple training and testing segments of any one of the training reads and the testing reads, respectively. Claim 2 is interpreted to further limit the training segments of each unaligned training read and the testing segments of each unaligned testing read.
Claims 1 and 17, and claims dependent therefrom, are indefinite for recitation of “…encoding the multiple segments…”. Claims 1 and 17 recite “multiple training segments” and “multiple testing segments” for each unaligned training read and unaligned testing read. As a result, it is not clear if “the multiple segments” in the encoding step is referring to one of the multiple training segments, one the multiple testing segments, or both, and its further unclear which one of the multiple segments of the sets of multiple segments is being referenced as discussed above. Clarification is requested via claim amendment.
Claim 17 is indefinite for recitation of “the method further comprises encoding…” in the last limitation. There is insufficient antecedent basis for this limitation in the claim because claim 17 does not recite a method. For purpose of examination, the limitation is interpreted to mean the processor is further configured to encode.
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 1-4 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claims 1 and 17 being representative) is directed to a method and system for detecting gene deletions. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 17 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
receiving training sequencing data, the training sequencing data comprising multiple unaligned training reads associated with gene sequences with deletion and gene sequences without deletion (claim 1 only);
splitting/split each of the unaligned multiple training reads into multiple training segments shorter than the training reads;
receiving/receive testing sequencing data comprising multiple unaligned testing reads;
splitting/split each of the multiple unaligned testing reads into multiple testing segments; and
evaluating/evaluate…to the multiple testing segments to detect deletion in the testing sequencing data,
wherein the training sequencing data and the testing sequencing data comprise RNA reads and the deletion is in a genome of a subject;
encoding/encode the multiple segments and using the encoded segments directly as an input to the bidirectional gated recurrent unit.
The identified claim limitations falls into the group of abstract ideas of mental processes for the following reasons. In this case, receiving training/testing sequencing data encompasses reading and collecting information of sequence reads, which can be practically performed in the mind. Splitting each of the training reads and testing reads into segments shorter than the training reads encompasses splitting each read into k-mers (e.g. ACTGAC to ACT and GAC), which is a mental process. Evaluating the testing segments to detect a deletion in the testing sequencing data can be practically performed in the mind by analyzing the sequence reads to identify missing base pairs relative to training reads, which amounts to a mere analysis of data. Furthermore, encoding the multiple segments can be practically performed in the mind by converting each base into a numerical value. Overall, the claims are similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Therefore, these limitations recite a mental process. See MPEP 2106.04(a)(2) III.
Dependent claims 2-3 further recite an abstract idea and/or further limit the abstract idea of claim 1 above. Dependent claim 2 further limits the abstract idea of splitting reads into segments of k-mers. Dependent claim 3 further limits the process in which the received sequencing data was previously generated, and thus is part of the abstract idea of collecting information above. Therefore, claims 1-4 and 17 recite an abstract idea. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims 1 and 17 include:
a computer (claim 1);
data memory configured to store training sequencing data, the training sequencing data comprising multiple training reads associated with gene sequences with deletion and gene sequences without deletion (claim 17);
a processor (claim 17);
training a machine learning model on the multiple segments;
[applying] the trained machine learning model; and
the machine learning model is a neural network comprising a bidirectional gated recurrent unit to process forward and reverse read directions of the training sequencing data and the testing sequencing data,
The additional element of claim 4 includes:
wherein the testing sequencing data is provided in a FASTQ file format from the sequencer.
The additional elements of a computer, processor, memory, storing information, and training/applying a machine learning are generic computer processes and/or components. The courts have found 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. See 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).
The additional element of training/applying the neural network comprising a bidirectional gated recurrent unit (GRU) to process forward and reverse read directions of the training sequencing data and the testing sequencing data, only serves to generally link the abstract idea to the particular technological environment of bidirectional GRUs, which does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Furthermore, it is not apparent that the additional elements, alone or in combination with the judicial exception, improve a technology.
Last, the additional element of claim 4 of providing the test sequencing data in a FASTQ format only serves to collect data for use by the abstract idea (see MPEP 2106.05(g)) and furthermore only serves to generally link the sequencing data to the technological environment of computers, which does not provide integration. See MPEP 2106.05(h).
Therefore, the additionally recited elements amount to insignificant extra-solution activity, mere instructions to apply the exception, and/or generally links the abstract idea to a technological environment, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-4 and 7 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claims 1 and 17 include:
a computer (claim 1);
data memory configured to store training sequencing data, the training sequencing data comprising multiple training reads associated with gene sequences with deletion and gene sequences without deletion (claim 17);
a processor (claim 17);
training a machine learning model on the multiple segments;
[applying] the trained machine learning model; and
the machine learning model is a neural network comprising a bidirectional gated recurrent unit to process forward and reverse read directions of the training sequencing data and the testing sequencing data,
The additional element of claim 4 includes:
wherein the testing sequencing data is provided in a FASTQ file format from the sequencer.
First, the courts have found 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 provide significantly more. See 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).
The additional element of the neural network comprising a bidirectional gated recurrent unit (GRU) to process forward and reverse read directions of sequencing data is well-understood, routine, and conventional. This position is supported by Barshai et al. (Identifying Regulatory Elements via Deep Learning, July 2020, 3, pg. 315-338). Barshai overviews the application of deep learning in identifying regulatory elements in DNA and RNA sequences (Abstract), and discloses the Bidirectional GRU is a popular deep neural network architecture in genomics (Figure 2), bidirectional RNNs are useful when both the 5’ and 3’ flanks of an element of DNA or RNA Matter (pg. 322, col. 2, para. 3), and furthermore that bidirectional recurrent layers processes sequences from right to left and right to left allowing for upstream and downstream biological contexts to be learned (pg. 325, col. 5). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. Therefore, the use of bidirectional GRU to process sequencing data does not provide significantly more.
The additional element of providing sequence reads in a FASTQ file is well-understood, routine, and conventional. This position is supported by Tripathi et al. (Next-generation sequencing revolution through big data analytics, 2016, Frontiers in Life Sciences, 9(2), pg. 119-149). Tripathi overviews data analytics in next-generation sequencing (Abstract) and discloses raw sequencing data files are provided in FASTQ format from sequencers (pg. 134, col. 2, para. 1 to pg. 135, col. 1, para. 1).
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]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Conclusion
No claims are allowed.
Claims 1-4 and 17 are free of the prior art.
Claims 1 and 17 recite “receiving training sequencing data, the training sequencing data comprising multiple unaligned training reads associated with gene sequences with deletions and gene sequences without deletion; splitting each of the unaligned multiple training reads into multiple training segments; training a machine learning model on the multiple segments…; and evaluating [applying] the trained machine learning model to the multiple testing segments to detect deletion in the testing sequencing data…, wherein the machine learning model is a neural network comprising a bidirectional gated recurrent unit…”.
The prior art does not disclose training a neural network comprising a bidirectional gated recurrent unit (GRU) on segments of unaligned reads associated with gene sequences with and without deletion and then detecting a deletion in testing sequencing data using the trained neural network.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cai et al., DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network, 2019, BMC Bioinformatics, 12:20, pg. 1-10; cited in IDS filed 16 Aug. 2023;
Shen et al., Recurrent Neural Network for Predicting Transcription Factor Binding Sites, 2018, Scientific Reports, 8:15270, pg. 1-10; cited in IDS filed 16 Aug. 2023; and
Eghbal-zadeh et al., Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data, 2018, arXiv, pg. 1-11.
Cai discloses a method, DeepSV, for calling genomic deletions from high-throughput sequencing data using a neural network (Abstract), which comprises training a convolution neural network from sequence reads with known deletions (pg. 4, col. 1, para. 4 to col. 2, para. 1). The convolution neural network is trained using training data comprising aligned sequence reads in a binary sequence alignment file and a variant call file containing known deletions and ultimately converts the aligned sequence reads to a pileup image representing read depths across positions (pg. 5, col. 1, para. 1; Figure 3). Cai refers to another neural network based method for calling short insertions/deletions called DeepVariant which similarly treats mapped sequences as images (pg. 1, col. 2, para. 1 to pg. 2, col. 1, para. 1). Cai does not disclose the use of segmented aligned reads as the training data for input into a neural network comprising a bi-directional GRU.
Shen discloses using a neural network comprising a bi-directional GRU that takes as input k-mers from an unaligned sequence and predicts whether the sequence contains a transcription factor binding site (Abstract; Figure 5), wherein each DNA sequence of the training data is given a binary label representing whether the sequence is a TF binding region or not (pg. 6, para. 7, see Model architecture). Shen does not disclose or suggest applying this neural network architecture to detecting deletions in sequencing data, and/or generating the claimed training data or the neural network.
Eghbal-zadeh discloses a method for detecting break-points, indicators of structural chromosomal variations such as insertions and deletions, in SNP array genomic data (Abstract). Eghbal-zadeh further discloses detecting the break-points utilizes a neural network comprising a bi-directional LSTM after the convolution layers that is trained on a sequence comprising frequency values from a SNP array from windows placed around breakpoint positions or in random positions (i.e. sequences associated with a deletion or not associated with a deletion) (pg. 6, col. 1, para.5). However, Eghbal-zadeh does not disclose or suggest applying the model to sequence reads and the recited training data.
Last, Barshai et al. (Identifying Regulatory Elements via Deep Learning, July 2020, 3, pg. 315-338), cited in the 101 rejection above, overviews the application of deep learning in identifying regulatory elements in DNA and RNA sequences (Abstract), which discloses methods using neural networks with bi-directional GRUs for sequence analysis, including for TF binding site identification (Figure 2) and predicting the function of DNA sequences (pg. 325, para. 5). Barshai does not disclose using the neural network framework for deletion detection using unaligned reads as claimed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN L MINCHELLA whose telephone number is (571)272-6485. The examiner can normally be reached 7:00 - 4:00 M-Th.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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