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 the Claims
Claims 1-20 are pending and under consideration in this action.
Priority
The instant application is a divisional of U.S. Application number 17/126,842, filed 12/18/2020, which claims domestic benefit to U.S. Provisional Application No. 62/952,875, filed 12/23/2019, as reflected in the filing receipt mailed 5/17/2022. The claim for domestic benefit for claims 1-20 is acknowledged. As such, the effective filing date of claims 1-20 is 12/23/2019.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/27/2022, 4/11/2023, and 9/27/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s have been considered by the examiner.
Drawings
The drawings are objected to for the following informalities:
Reference character “115” is used to designate both “Targets and labels” and “Create bead-based capture system” in Fig. 1A.
Reference characters “115” (Fig. 1A) and “120” (Fig. 1B and Para. [0046] of the specification) have been used to designate “Targets and labels”.
Reference characters “225” (Fig. 2) and “230” (Para. [0056] of the specification) have both been used to designate “prediction models”.
Reference characters “225” (Para. [0060]/[0062] of the specification) and “230” (Fig. 2 and Para. [0059] of the specification) have both been used to designate “training samples”.
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 abstract of the disclosure is objected to because it uses phrases that can be implied, including “the present disclosure relates to” and “aspects of the present disclosure are directed to” (see MPEP § 608.01(b)(I)(C)). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1-3, 8, 11, and 16 are objected to because of the following informalities:
Claims 1, 11, and 16 recite the phrase “acquiring a library of aptamers that potential satisfy the query” in lines 3, 5, and 7, respectively, which should be corrected to “acquiring a library of aptamers that potentially satisfy the query” for clarity.
Claim 2 recites the phrase “wherein the providing the result to the query further comprising providing…” in lines 2-3 of the claim, which should be corrected to “wherein the providing the result to the query further comprises providing…” for clarity.
Claim 3 recites the phrase “from the sequence data for first set of aptamers” in line 3 of the claim, which should be corrected to “from the sequence data for the first set of aptamers” for clarity.
Claim 8 recites the phrase “did not bound to the one or more targets” in lines 3-4 of the claim, which should be corrected to “did not bind to the one or more targets” for clarity.
Appropriate correction is required.
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.
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 1-20 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.
Claims 1, 11, and 16 recite the limitations “identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query” and “validating the third set of aptamers that substantially or completely satisfy the query” in lines 4-6 and 10 of claim 1, respectively (Lines 6-8 and 12 in claim 11; and Lines 8-10 and 14 in claim 16, respectively). The metes and bounds of the claims are rendered indefinite due to the lack of clarity. It is unclear what value “substantially satisfying the query” is referring to. Para. [0068] of the specification discloses that “the term "substantially," "approximately," or "about" may be substituted with "within [a percentage] of' what is specified, where the percentage includes 0.1, 1, 5, and 10 percent”. However, it is unclear which of these values the claim is referring to. This rejection can be overcome by amendment of claims 1, 11, and 16 to clarify the value for the term “substantially”. Claims 2-10, 12-15, and 17-20 are also rejected due to their dependency from claims 1, 11, and 16.
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 an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-10 are directed towards a method, claims 11-15 are directed towards a manufacture, and claims 16-20 are directed system, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claims 1, 11, and 16 recite a mental process (i.e., an evaluation of the aptamers in comparison to the query) in “identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query” and “validating the third set of aptamers that substantially or completely satisfy the query”; a mathematical concept (i.e., using a machine learning model, such as a neural network, to predict the sequences, see specification Para. [0056]-[0057]) in “generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers”; and a mental process (i.e., an observation of the output aptamers as a result) in “upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query”.
Claim 2 recites a mental process (i.e., an observation of the output aptamers as a result) in “wherein the providing the result to the query further comprising providing the third set of aptamers and the first set of aptamers as the result to the query”.
Claims 6, 13, and 18 recite a mathematical concept (i.e., using a machine learning model, such as a neural network, to predict the analysis data, see specification Para. [0056]-[0057]) in “predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers”.
Claims 7, 14, and 19 recite a mental process (i.e., an observation of the analysis data) in “wherein the analysis data for the first set of aptamers includes a binary classifier or a multiclass classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier or the multiclass classifier”.
Claims 8, 15, and 20 recite a mental process (i.e., an evaluation of the binary classifier or the multiclass classifier) in “wherein: (i) the binary classifier indicates that each aptamer from the first set of aptamers functionally inhibited the one or more targets, functionally did not inhibit the one or more targets, bound to the one or more targets, or did not bound to the one or more targets, or (ii) the multiclass classifier indicates a level of functional inhibition or a gradient scale for binding affinity with respect to each aptamer from the first set of aptamers and the one or more targets”.
Claim 10 recites a mathematical concept (i.e., using a machine learning model, such as a neural network, to predict the count data, see specification Para. [0056]-[0057]) in “predicting, by another prediction model, a count for each aptamer of the third set of aptamers derived from the sequence data for the first set of aptamers and the count data for the first set of aptamers”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following claims recite limitations that equate to additional elements:
Claims 1, 11, and 16 recite “receiving a query concerning one or more targets”; “acquiring a library of aptamers that potential satisfy the query”; and “obtaining sequence data for the first set of aptamers”.
Claim 3 further recites “obtaining sequence data for the second set of aptamers, wherein the third set of aptamers is generated as being derived from the sequence data for first set of aptamers and the sequence data for the second set of aptamers”.
Claim 4 further recites “recording the third set of aptamers in a data structure in association with the one or more targets”.
Claims 5, 12, and 17 further recite “obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data”.
Claims 6, 13, and 18 further recite “recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers”.
Claim 9 further recites “obtaining count data for the first set of aptamers, wherein the count data for the first set of aptamers indicates a count of each aptamer within the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the first set of aptamers and the count data”.
Claim 10 further recites “recording the third set of aptamers in a data structure in association with the one or more targets and the predicted count for each aptamer of the third set of aptamers”.
Claim 11 recites “a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing”.
Claim 16 recites “one or more data processors” and “a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform processing”.
Regarding the above cited limitations in claims 1, 3-6, 9-13, and 16-18 of (i) receiving a query concerning one or more targets (claims 1, 11, and 16); (ii) acquiring a library of aptamers that potential satisfy the query (claims 1, 11, and 16); (iii) obtaining sequence data for the first set of aptamers (claims 1, 11, and 16); (iv) obtaining sequence data for the second set of aptamers, wherein the third set of aptamers is generated as being derived from the sequence data for first set of aptamers and the sequence data for the second set of aptamers (claim 3); (v) recording the third set of aptamers in a data structure in association with the one or more targets (claim 4); (vi) obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data (claims 5, 12, and 17); (vii) recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis/count for each aptamer of the third set of aptamers (claims 6, 10, 13, and 18); and (viii) obtaining count data for the first set of aptamers, wherein the count data for the first set of aptamers indicates a count of each aptamer within the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the first set of aptamers and the count data (claim 9). These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of validating and providing the third set of aptamers as a result to the query (see MPEP § 2106.04(d)).
Regarding the above cited limitations in claims 11 and 16 of (ix) a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing (claim 11); (x) a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform processing (claim 16); and (xi) one or more data processors (claim 16). These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations claims 11 and 16 of (ix) a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing; (x) a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform processing; and (xi) one or more data processors. These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitations in claims 1, 5, 9, 11-12, and 16-17 of (i) receiving a query concerning one or more targets (claims 1, 11, and 16); (ii) acquiring a library of aptamers that potential satisfy the query (claims 1, 11, and 16); (vi) obtaining analysis data for the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data (claims 5, 12, and 17); and (viii) obtaining count data for the first set of aptamers, wherein the count data for the first set of aptamers indicates a count of each aptamer within the first set of aptamers, and wherein the third set of aptamers are generated as being derived from the first set of aptamers and the count data (claim 9). These limitations equate to receiving/transmitting data over a network, which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in claims 1, 3, 11, and 16 of (iii) obtaining sequence data for the first set of aptamers (claims 1, 11, and 16); (iv) obtaining sequence data for the second set of aptamers, wherein the third set of aptamers is generated as being derived from the sequence data for first set of aptamers and the sequence data for the second set of aptamers (claim 3). These limitations equate to laboratory techniques that are WURC limitations in the life science arts (see MPEP 2106.05(d)). Analyzing DNA to provide sequence information or detect allelic variants is a WURC limitation in Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546.
Regarding the above cited limitations in claims 4, 6, 10, 13, and 18 of (v) recording the third set of aptamers in a data structure in association with the one or more targets (claim 4); and (vii) recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis/count for each aptamer of the third set of aptamers (claims 6, 10, 13, and 18). These limitations when viewed individually and in combination, are WURC limitations as taught by Lee et al. (Constructive Prediction of Potential RNA Aptamers for a Protein Target. IEEE/ACM Trans Comput Biol Bioinform. 17(5):1476-1482 (2020); published 11/4/2019). Lee et al. discloses an algorithm for finding RNA aptamer candidates for a protein target, wherein the output data is a set of aptamer candidates sorted by probability output from a feature trained random forest model (Pg. 1477, Algorithm 1). The model uses structural data from individual counts of RNA-protein complexes and RNA analysis features (limitations (v) and (vii)) (Pg. 1476, Col. 2, Para. 3 – Pg. 1477, Col. 1, Para. 2 and Pg. 1478, Col. 1, Para. 1).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 1-5, 9, 11-12, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. (Constructive Prediction of Potential RNA Aptamers for a Protein Target. IEEE/ACM Trans Comput Biol Bioinform. 17(5):1476-1482 (2020); published 11/4/2019).
Regarding claims 1, 11, and 16, Lee et al. teaches a computational method that constructs potential RNA aptamers for a protein target using various features of interacting RNA and proteins, and structural constraints on the RNA secondary structure (i.e., a computer-implemented method and receiving a query concerning one or more targets) (Pg. 1481, Col. 1, Para. 4). Lee et al. further teaches that several datasets were used, including: 1) 696 protein-RNA extracted from the Protein Data Bank (PDB) (Pg. 1476, Col. 2, Para. 3); 2) structural data of 35 known RNA aptamer-protein complexes from the protein data bank (PDB), which were not including in the 696 protein-RNA complexes (Pg. 1477, Col. 1, Para. 2); and 3) a benchmark dataset with positive and negative RNA-protein pairs (Pg. 1477, Col. 1, Para. 3) (i.e., acquiring a library of aptamers that potential satisfy the query). Lee et al. further teaches that the benchmark dataset contains 145 positive and 435 negative DNA- or RNA-protein pairs. They used 56 RNA aptamer protein pairs from the positive dataset as positive instances. For negative instances, they generated random RNA sequences and paired them with a protein chain present in the 35 protein-RNA complexes from the PDB (i.e., identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query and a second set of aptamers from the library of aptamers that does not substantially or completely satisfy the query) (Pg. 1477, Col. 1, Para. 3). Lee et al. further teaches that RNA aptamer-protein complexes are from the PDB (i.e., they contain sequence data, obtaining sequence data for the first set of aptamers) (Pg. 1477, Col. 1, Para. 2). Lee et al. further teaches that to find aptamer candidates, they generated 27-mer random RNA sequences and predicted their secondary structures. Using structural constraints on the RNA secondary structures, they removed the random RNA sequences that did not satisfy the constraints. The probability computed by the random forest (RF) model was then obtained for each of the remaining RNA sequences. The RF model was trained on the aptamer-protein complexes from the PDB (i.e., generating, by a prediction model, a third set of aptamers derived from the sequence data for first set of aptamers) (Pg.1477, Col. 1, Para. 5 – Col. 2, Para. 1 and Pg. 1477, Fig. 1). Lee et al. further teaches that the aptamer candidates were sorted in descending order of probability, and docking to a protein target was performed for the top-ranked candidates (i.e., validating the third set of aptamers that substantially or completely satisfy the query) (Pg. 1477, Col. 2, Para. 1 and Pg. 1477, Fig. 1). Lee et al. further teaches that the RF model outputs the top 10 ranked candidates for structural analysis with the protein target (Pg. 1477, Fig. 1). The predicted aptamer structure can also be visualized (see, for example, Pg. 1479, Fig. 4) (i.e., upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query).
Regarding claim 2, Lee et al. teaches an example of three RNA aptamers predicted by the model for three different protein targets (e.g., the third set of aptamers) that were compared to the actual RNA aptamers from the PDB (e.g., the first set of aptamers used for training the model). These are displayed visually in Fig. 4 (i.e., wherein the providing the result to the query further comprising providing the third set of aptamers and the first set of aptamers as the result to the query) (Pg. 1479, Col. 2, Para. 3 and Pg. 1479, Fig. 4).
Regarding claim 3, Lee et al. teaches that in the benchmark dataset with the positive and negative pairs, the negative instances were random RNA sequences generated and paired with a protein chain present in the protein-RNA complexes from the PDB (i.e., obtaining sequence data for the second set of aptamers) (Pg. 1477, Col.1, Para. 3). Lee et al. further teaches that the RF model was trained using the protein-RNA complexes, and subsequently, the RF model was used to generate aptamer candidates for a protein target (i.e., wherein the third set of aptamers is generated as being derived from the sequence data for first set of aptamers and the sequence data for the second set of aptamers) (Pg. 1477, Fig. 1).
Regarding claim 4, Lee et al. teaches the algorithm for finding RNA aptamer candidates for a protein target. The output is a set of aptamer candidates (C), that is populated with sequences and sorted based a calculated probability from the RF model (i.e., recording the third set of aptamers in a data structure in association with the one or more targets) (Pg. 1477, Algorithm 1).
Regarding claims 5, 12, and 17, Lee et al. teaches that the prediction model encodes the following RNA features: interaction propensity of nucleotide triplets with amino acids, mono-nucleotide composition (mC), di-nucleotide composition (dC), and pseudo tri-nucleotide composition (PseTNC) (i.e., obtaining analysis data for the first set of aptamers) (Pg. 1477, Col. 2, Para. 3 – Pg. 1478, Col. 1, Para. 1). Lee et al. further teaches that the RF model was trained using the protein-RNA complexes and encoded features, and subsequently, the RF model was used to generate aptamer candidates for a protein target (i.e., wherein the third set of aptamers are generated as being derived from the sequence data for first set of aptamers and the analysis data) (Pg. 1477, Fig. 1).
Regarding claim 9, Lee et al. teaches the use of structural data for the set of 696 protein-RNA extracted from the Protein Data Bank (PDB) (Pg. 1476, Col. 2, Para. 3) and the set of 35 known RNA aptamer-protein complexes from the PDB, which were not including in the 696 protein-RNA complexes (i.e., the RNA-protein complexes are all unique, giving a count of 1 for each aptamer; obtaining count data for the first set of aptamers, wherein the count data for the first set of aptamers indicates a count of each aptamer within the first set of aptamers) (Pg. 1477, Col. 1, Para. 2). Lee et al. further teaches that the RF model was trained using the protein-RNA complexes, and subsequently, the RF model was used to generate aptamer candidates for a protein target (i.e., wherein the third set of aptamers are generated as being derived from the first set of aptamers and the count data) (Pg. 1477, Fig. 1).
Regarding claim 11, Lee et al. further teaches that the method is computational (i.e., on a computer containing a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform processing) (Pg. 1476, Col. 2, Para. 2).
Regarding claim 16, Lee et al. further teaches that the method is computational (i.e., on a computer containing one or more data processors and a non-transitory computer readable storage medium containing instructions, which, when executed on the one or more data processors, causes the one or more data processors to perform processing) (Pg. 1476, Col. 2, Para. 2).
Therefore, Lee et al. teaches all the limitations in claims 1-5, 9, 11-12, and 16-17.
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.
Claims 6-8, 10, 13-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. as applied to claims 1-5, 9, 11-12, and 16-17 above, and further in view of Yang et al. (A novel nucleic acid sequence encoding strategy for high-performance aptamer identification and the aid of sequence design and optimization. Chemometrics and Intelligent Laboratory Systems. 170: 32-37 (2017); published 9/27/2017).
Lee et al., as applied to claims 1-5, 9, 11-12, and 16-17 above, does not teach predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers; recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers; wherein the analysis data for the first set of aptamers includes a binary classifier or a multiclass classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier or the multiclass classifier; wherein: (i) the binary classifier indicates that each aptamer from the first set of aptamers functionally inhibited the one or more targets, functionally did not inhibit the one or more targets, bound to the one or more targets, or did not bound to the one or more targets, or (ii) the multiclass classifier indicates a level of functional inhibition or a gradient scale for binding affinity with respect to each aptamer from the first set of aptamers and the one or more targets; predicting, by another prediction model, a count for each aptamer of the third set of aptamers derived from the sequence data for the first set of aptamers and the count data for the first set of aptamers; and recording the third set of aptamers in a data structure in association with the one or more targets and the predicted count for each aptamer of the third set of aptamers.
Regarding claims 6, 13, and 18, Yang et al. teaches a novel nucleic acid sequence encoding strategy called Apta-LoopEnc for secondary structural feature extraction of candidate sequences by analyzing their delicate substructures in loop regions (Abstract). Yang et al. further teaches that Apta-LoopEnc is coupled with support vector machine (SVM) for high affinity aptamer identification (i.e., predicting, by another prediction model, an analysis for each aptamer of the third set of aptamers) (Pg. 34, Col. 2, Para. 4). Yang et al. further teaches that when modeling, the randomly selected sequence sequences 1–71 were used as a training set for the SVM model building, and sequences 72–122 were invoked as a test set (i.e., derived from the sequence data for first set of aptamers and the analysis data for the first set of aptamers) (Pg. 34, Col. 2, Para. 2). Yang et al. further teaches that for high affinity aptamer identification, candidate aptamers were labeled with 1, denoting the high affinity and specificity aptamer candidates or -1, denoting low affinity aptamer candidates (i.e., recording the third set of aptamers in a data structure in association with the one or more targets and the predicted analysis for each aptamer of the third set of aptamers) (Pg. 34, Col. 1, Para. 2).
Regarding claims 7, 14, and 19, Yang et al. teaches that candidate aptamers from the 11th and 13th rounds were labeled with 1, denoting the high affinity and specificity aptamer candidates, and the ones from the 3rd round were labeled with 1, denoting low affinity aptamer candidates (i.e., wherein the analysis data for the first set of aptamers includes a binary classifier selected based on the query) (Pg. 34, Col. 1, Para. 2). Yang et al. further teaches an example wherein the possibility of the Apta-LoopEnc based SVM in aiding aptamer sequence optimization against SMMC-7721 liver carcinoma cells was investigated. A set of candidates was designed with the sequences different from those generated in the traditional process of systematic evolution of ligands by exponential enrichment (SELEX). Five candidates predicted as high-affinity sequence were selected, all of which have a predicted class of 1 (e.g., the class with the highest affinity for the target) (i.e., wherein the analysis data for the first set of aptamers includes a binary classifier selected based on the query, and the predicted analysis for the third set of aptamers includes the binary classifier) (Pg. 36, Col. 1, Para. 3 and Pg. 36, Table 2).
Regarding claims 8, 15, and 20, Yang et al. teaches that five candidates predicted as high-affinity sequence were selected and their binding affinities were analyzed using a flow cytometer against SMMC-7721 liver carcinoma cells. These newly designed sequences were demonstrated to have quite small equilibrium dissociation constants at nanomolar level, which were comparable to the sequence with the highest affinity (i.e., wherein (i) the binary classifier indicates that each aptamer from the first set of aptamers bound to the one or more targets) (Pg. 36, Col. 1, Para. 3 and Pg. 36, Table 2).
Regarding claim 10, Yang et al. teaches that the output of the SVM model identifies candidate aptamers with slight variability between sequences with similar secondary structures and markedly different binding affinities (i.e., the model output a count of 1 for each sequence; predicting, by another prediction model, a count for each aptamer of the third set of aptamers derived from the sequence data for the first set of aptamers and the count data for the first set of aptamers) (Pg. 36, Col. 1, Para. 2). Yang et al. further teaches an example of the output sequences identified using Apta-LoopEnc based SVM, wherein the sequences are all unique, and therefore have a count of 1 (i.e., recording the third set of aptamers in a data structure in association with the one or more targets and the predicted count for each aptamer of the third set of aptamers) (Pg. 36, Table 2).
Therefore, regarding claims 6-8, 10, 13-15, and 18-20, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of predicting aptamers for a target protein of Lee et al. with the affinity analysis of Yang et al. because the sequence encoding and prediction strategy of Yang et al. is a high performance, time-saving, and cost-effective way to design aptamers, promoting the development of aptamer-related studies and applications (Yang et al., Abstract). One of ordinary skill in the art would be able to combine the teachings of Lee et al. with Yang et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for predicting aptamers for a target protein. Therefore, regarding claims 6-8, 10, 13-15, and 18-20, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
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/D.P.S./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687