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 .
Status of the Claims
Claims 1-20 are under examination.
Claim Rejections - 35 USC § 112
2. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Instant claim 8 is drawn to a computer program product that causes a data processor to perform actions. Steps (c) and (e) of the instant claim 8 require using in vitro assays. It is unclear how a data processor is to perform a physical wet lab step such as an in vitro assay. Dependent claims 9-14 are also rejected for depending from claim 8.
Instant claim 14 is drawn to synthesizing aptamer sequences or a biologic. However, it is unclear how a data processor is to synthesize a physical aptamer or biologic.
Instant claim 15 is drawn to a system with a data processor and a non-transitory computer readable storage medium that causes the data processors to perform actions. Steps (c) and (e) of the instant claim 15 require using in vitro assays. It is unclear how a data processor is to perform a physical wet lab step such as an in vitro assay. Dependent claims 16-20 are also rejected for depending from claim 15.
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.
Claims 1-6 , 8-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception without significantly more.
Claims 1-6 , 8-13, and 15-20 are directed to method identifying aptamer sequences that satisfy one or more constraints based on analytical data associated with each aptamer.. As described in Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S._, 134 S. Cr. 2347, 110 U.S.P.Q.2d 1976 (2014), a two-step analysis is required in considering the patent eligibility of the claimed subject matter. The first step requires determining if the claimed subject matter is directed to a judicial exception. The instant claims require identifying, by a first machine learning model, a first set of aptamer sequences; identifying, by a second machine learning model, a second set of aptamer sequences; and identifying a final set of aptamer sequences from the second set of aptamer sequences. Utilizing a machine learning model to identify aptamer sequences is a mathematical algorithm. Identifying the final set of aptamer sequences is drawn to a mathematical algorithm or a mental step. A mental step is a judicial exception. The courts have found mathematical algorithms to be drawn to the judicial exception of an abstract idea (In re Grams, 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). Instant claims 2-6, 12,13, 15-20 recite additional mathematical steps regarding the machine learning model, Thus, the instant claims are drawn to a judicial exception.
This judicial exception is not integrated into a practical application. The instant claims do not recite an element that reflects an improvement in the functioning of a computer or other technology, an element that applies the judicial exception to effect a particular treatment, an element that implements the judicial exception with a particular machine, or an element that effects a transformation of a particular article to a different state or thing. The instant claims recite obtaining an initial sequence data, using an in vitro binding selection process, using an in vitro assay, and outputting the final set of aptamer sequences. The in vitro steps do not integrate the step of identifying a final set of aptamer sequences, which is a judicial exception, into a practical application. Thus, these steps are extra solution data gathering steps and outputting steps. Extra solution steps are not sufficient to integrate a judicial exception into a practical application. The instant claims also recite a computer program product, a non-transitory computer readable medium, and processor. However, the instant claims do not recite any structural limitations of these elements, and they are not drawn to a particular machine. Thus, the instant claims do not integrate the judicial exception into a practical application.
The second part of the analysis requires determining if the claims include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant claims recite the additional elements of obtaining an initial sequence data, using an in vitro binding selection process, using an in vitro assay, and outputting a final set of sequences. The steps of obtaining an initial sequence data, using an in vitro binding selection process, using an in vitro assay, and outputting a final sequence are well-understood, routine, and conventional data gathering and outputting steps (Specification, paragraphs [0036] and [0110]-[0116] ). The instant claims also recite a computer program product, a non-transitory computer readable medium, and processor, which are well-understood, routine, and conventional components of a computer (Specification, paragraphs [0110]-[0116]). Reciting such well-understood, routine, and conventional elements do not transform a judicial exception into patent eligible subject matter. In addition, the recitation of the specific types of data, to be used in the judicial exception does not transform the abstract idea into a non-abstract idea. (See buySAFE, Inc. v Google, Inc. 765 F.3d 1350, 112 U.S.P.Q.2d 1093 (Fed.Cir.2014)). Furthermore, the elements taken as a combination are also well-understood, routine, and conventional, since the elements are merely gathering data for the judicial exception and performing it on a computer. Thus, the instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
4. 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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bashir et al. (“Machine Learning Guided Aptamer Refinement and Discovery” Nature Communications (April 22, 2021) volume 12, pages 1-10 (IDS, filed 4/13/23)).
Regarding claims 1, 8 and 15, Bashir et al. teach a method that includes obtaining initial sequence data for aptamers of an initial aptamer library that bind to a target or do not bind to a target (page 2, under “Results”); identifying, by a first machine learning model, a first set of aptamer sequences as satisfying one or more constraints, wherein the first machine learning model comprises model parameters learned from the initial sequence data, and the first set of aptamer sequences are derived from a subset of sequences from the initial sequence data, sequences from a pool of sequences different from sequences from the initial sequence data (page 2, under “Results”, page 3, Fig 1, and page 7 under “Methods”); obtaining, using an in vitro binding selection process, subsequent sequence data for aptamers of a subsequent aptamer library that bind to the target or do not bind to the target, where the aptamer library comprises aptamers synthesized from the first set of aptamer sequences (page 2, under “Results”); identifying, by a second machine learning model, a second set of aptamer sequences as satisfying the one or more constraints, wherein the machine learning model comprises model parameters learned from the subsequent sequence data, and the second set of aptamer sequences are derived from a subset of sequences from the subsequent sequence data, or sequences from a pool of sequences different from sequences from the subsequent sequence data (page 2, under “Results”; page 8, under “ML models, features and output layers”); determining, using one or more in vitro assays, analytical data for aptamers synthesized from the second set of aptamer sequences (page 2, under “Results”); identifying a final set of aptamer sequences from the second set of aptamer sequences that satisfy the one or more constraints based on the analytical data associated with each aptamer, and outputting the final set of aptamer sequences (page 2, under “Results” and pages 3-6 and 8-9). Furthermore, Bashir et al. teach performing data processing with raw fastq files which would require a system with a data processor and a non-transitory computer readable storage medium (page 8, under “Sequencing and data processing”).
Regarding claims 2, 9, and 16, Bashir et al. teach training the machine learning algorithm using the initial sequence data to learn the model parameters and generate the first machine learning model, where the initial sequence data comprises aptamer sequences and associated analytical data, the analytical data comprising a first binding approximation metric, a first functional metric or a combination of aptamers derived from aptamer sequences (page 8).
Regarding claims 3, 10, and 17, Bashir et al. teach training a second machine learning algorithm using subsequence sequence data to learn the model parameters to generate a second machine learning model, where the subsequence sequence data comprises aptamer sequences and analytical data including second binding-approximation metric, a second functional-approximation metric or a combination of aptamers (page 2, under “Results”; page 3, Fig. 1; and page 8).
Regarding claims 4, 11, and 18, Bashir et al. teach prior to identifying the second set of aptamer sequences, retraining the first machine-learning algorithm using subsequent sequence data to relearn the model parameters and generating another version of a first machine learning model, wherein the subsequence sequence data comprises aptamer sequences and analytical data where the analytical data includes a second binding-approximation metric, a second functional-approximation metric, or a combination of aptamers, repeating step (b) and step (c) (page 2, under “Results” and page 8).
Regarding claims 5, 6, 11, 12, 19, and 20, Bashir et al. teach prior to identifying the final set of aptamers, retaining the first machine learning algorithm using the second set of aptamers sequences and the analytical data for aptamers to relearn the model parameters an generate another version of the first machine-learning model where the analytical data comprises a third-binding-approximation metric, a third functional approximation metric or a combination of aptamers, repeating step (b), repeating step (c) and repeating steps (d)-(e) (page 2, under “Results”, pages 8-9).
Regarding claim 7, 14, Bashir teach synthesizing the one or more aptamers using the final set of sequences and synthesizing a biologic using the aptamers (page 2, under “Results”).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY LIN whose telephone number is (571)272-2561. The examiner can normally be reached T-F 7am-5pm.
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/JERRY LIN/Primary Examiner, Art Unit 1685