DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant has canceled claims 1-20 in a preliminary amendment and added claims 21-40. Currently, claims 21-40 are pending and being addressed.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The following reasons are provided to evaluate subject matter eligibility.
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Are the claims directed to a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims are directed to a judicial exception under Prong One, then is the judicial exception integrated into a practical application;
(2B) If the claims are directed to a judicial exception and do not integrate the judicial exception, do the claims provide an inventive concept.
With regard to (21), the analysis is a ‘yes’, claim 21 recites a machine, claim 32 recites a nontransitory computer readable medium, and claim 37 recites a process.
With regard to (2A) Prong One, the analysis is a “yes”. Claim 21 recites “identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles; provide, to a machine learning system, input data derived from the sequencing images; and generate, by processing the input data through the machine learning system, one or more quality predictions for the one or more base calls.” When viewed under the broadest most reasonable interpretation the claim recites an abstract idea of mental processes. The step of “identifying” is generically recited because there is no description of how this is accomplished. It can be interpreted as merely looking at the data, and evaluating the data in the mind. The concepts, as claimed, are observations and/or evaluations (“identifying”), and judgements (“generate one or more quality predictions….”). There is nothing in the claim that requires more than an operation that a human, armed with the appropriate apparatus, pen/paper, can perform. One can perform the process using pen and paper, and the recitation of modules (such as judgers, gathering unit, an inference model) in the system/device claim is a mere use of generic computer components. See MPEP 2106.04 and the 2019 PEG.
With regard to (2A) Prong Two: the analysis is a “No”. Claim 21 recites the additional elements of “generate, by processing the input data through the machine learning system, one or more quality predictions for the one or more base calls”; and these additional elements represents mere data gathering and indexing the data all together that is necessary for use of the recited abstract idea. Therefore, the limitation(s) is/are insignificant extra-solution activity, and a generic operation. See MPEP 2106.05(1). The claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application.
With regard to (2B): the pending claims do not show what is more than a routine in the art presented in the claims, i.e., the additional elements are nothing more than routine and well-known steps. The additional elements do not reflect an improvement to a technology or technical field, including the use of a particular machine or particular transformation. It has not been shown that the mental process allows the “technology” to do something that it previously was not able to do.
Claims 32 and 37 are similarly rejected for the same reasons as claim 21. Dependent claims 22-31, 33-36, and 38-40 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are rejected for the same reasons and not repeated herewith.
Claim Rejections - 35 USC § 102
5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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)(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.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rothberg et al. (U.S. patent pub. 2019/0237160 Will be further referred to as Rothberg).
Regarding claim 21: Rothberg discloses a system comprising:
at least one processor (fig. 1); and
a non-transitory computer readable medium storing instructions that, when executed by the at least one processor (fig. 1), cause the system to:
identify one or more base calls for one or more analytes based on sequencing images captured at one or more sequencing cycles (Paragraphs 0002-0003, 0032, 0077, 0095, and 0179-0190);
provide, to a machine learning system, input data derived from the sequencing images (paragraph 0005, 0014-0016, 0036, 0075, 0080, and 0158) ; and
generate, by processing the input data through the machine learning system, one or more quality predictions for the one or more base calls (paragraphs 0195, 0197, and 0203-0205).
Regarding claim 22: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to generate the one or more quality predictions for the one or more base calls by determining one or more quality scores for the one or more base calls (paragraphs 0194-0195, 0197, and 0203-0205).
Regarding claim 23: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to generate the one or more quality predictions for the one or more base calls by:
determining, at a sequencing cycle of the one or more sequencing cycles, a quality score for a base call of a base incorporated into a target cluster of nucleic acids (paragraph 0131, 0139-0146 and 0194-0195); or
determining, at the sequencing cycle of the one or more sequencing cycles, quality scores for base calls of bases incorporated into multiple clusters of nucleic acids (paragraph 0131, 0139-0146 and 0194-0195).
Regarding claim 24: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to generate the one or more quality predictions for the one or more base calls by generating continuous values that identify a quality of the one or more base calls (paragraph 0131, 0139-0146 and 0194-0195).
Regarding claim 25: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to:
generate, by processing the input data through the machine learning system, one or more predicted quality indications for the one or more base calls(paragraph 0131, 0139-0146 and 0194-0195); and
generate, based on the one or more predicted quality indications, the one or more quality predictions for the one or more base calls (paragraph 0131, 0139-0146 and 0194-0195).
Regarding claim 26: The system of claim 25, further comprising instructions that, when executed by the at least one processor, cause the system to:
generate the one or more predicted quality indications by generating quality-score likelihoods of a base call of the one or more base calls being assigned individual quality scores (paragraphs 0006-0007, 0081-0082, and 0194-0195); and
based on the quality-score likelihoods, generate the one or more quality predictions for the one or more base calls by assigning the base call a quality score from one of the individual quality scores (paragraphs 0006-0007, 0081-0082, and 0194-0195).
Regarding claim 27: The system of claim 25, further comprising instructions that, when executed by the at least one processor, cause the system to:
generate the one or more predicted quality indications by generating a first likelihood of a base call of the one or more base calls being a high quality, a second likelihood of the base call being a medium quality, and a third likelihood of the base call being a low quality (paragraphs 0006-0007, 0081-0082, 0194-0195 and 0204-0205); and
based on the first likelihood, the second likelihood, and the second likelihood, determine the one or more quality predictions for the one or more base calls by assigning a quality score to the base call (paragraphs 0006-0007, 0081-0082, 0194-0195 and 0204-0205).
Regarding claim 28: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to provide the input data derived from the sequencing images by providing the machine learning system a sequencing image depicting intensity emissions from a target cluster of nucleic acids and one or more adjacent clusters of nucleic acids at a sequencing cycle of the one or more sequencing cycles (paragraphs 0004, 0010, 0020, 0029-0030, 0126-0128, and 0183-0184).
Regarding claim 29: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to provide the input data derived from the sequencing images by providing the machine learning system:
supplemental distance information that identifies distances between pixels within the sequencing images (paragraphs 0043, 0144, and 0148-0150); or
image channel information identifying one or more image channels corresponding to the sequencing images (paragraph 0111).
Regarding claim 30: The system of claim 21, further comprising instructions that, when executed by the at least one processor, cause the system to:
provide, to the machine learning system, one or more quality predictor values for the one or more base calls (paragraphs 0194-0195); and
generate, by processing the one or more quality predictor values through the machine learning system, the one or more quality predictions further based on the one or more quality predictor values (paragraphs 0194-0195).
Regarding claim 31: The system of claim 30, wherein the one or more quality predictor values comprise one or more of online overlap, purity, phasing, start5, hexamer score, motif accumulation, endiness, approximate homopolymer, intensity decay, penultimate chastity, signal overlap with background (SOWB), shifted purity G adjustment, peak height, peak width (paragraphs 0194-0195), peak location (paragraphs 0194-0195, peak window=location) , relative peak locations, peak height ration, peak spacing ration, or peak correspondence.
Regarding claim 32: See claim 21.
Regarding claim 33: See claim 22.
Regarding claim 34: See claim 24.
Regarding claim 35: See claim 26.
Regarding claim 36: See claim 28.
Regarding claim 37: See claim 1.
Regarding claim 38: See claim 25.
Regarding claim 39: See claim 31.
Regarding claim 40: The computer-implemented method of claim 39, further comprising determining the one or more base calls for the one or more analytes based on the sequencing images captured at the one or more sequencing cycles (abstract, figs. 3-5, and paragraphs 0032, 0105-0111, and 0179-0190).
Contact Information
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on 571-272-4650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
July 29, 2026
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