DETAILED ACTION
Applicant’s response filed 12/01/2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 .
Claim Status
Claims 2, 4, 6, 12, 15, 21-22 and 24-43 are cancelled by Applicant
Claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 are currently pending and are herein under examination.
Claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 are rejected.
Priority
The instant application claims domestic benefit as a 371 filing of International Application PCT/GB2019/052456 filed 09/04/2019, which claims the benefit of priority to Great Britain Application No. GB 1814369.3 filed 09/04/2018. The claims to the benefit of priority are acknowledged. As such, the effective filing date for claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 is 09/04/2018.
Claim Objections
The objections to claim 14 are withdrawn in view of claim amendment.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 1, 3, 5, 7-11, 13-14, 16-20, 23 and 43 under 35 USC 112(b) is withdrawn in view of claim amendment.
35 USC 112(d)
The rejection of claim 43 under 35 USC 112(d) is withdrawn in view of claim amendment.
35 USC 103
The rejection of claims 3, 5, 19 and 23 under 35 U.S.C. 103 as being unpatentable over Gundlach et al. in view of Rang et al. and in further view Clarke et al. is withdrawn in view of claim amendments and in view of further considerations of the applied art.
Claim Rejections - 35 USC § 112
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, 3, 5, 7-11, 13-14, 16-20 and 23 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.
This rejection is newly recited and is necessitated by claim amendment.
Claim 1, line 18, recites the phrase “the one or more measurements of a first polymer unit” which renders the claim indefinite. It is unclear if “a first polymer unit” is supposed to be “the first non-canonical polymer unit” because lines 16-17 recite “one or more measurements of a first non-canonical polymer unit”. Or if “a first polymer unit” is a different polymer unit from the “first non-canonical polymer unit”. To overcome this rejection, clarify how the phrase should be interpreted.
Claims 3, 5, 7-11, 13-14, 16-20 and 23 are also rejected because they depend on claim 1, which is rejected, and because they do not resolve the issue of indefiniteness.
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, 3, 5, 7-11, 13-14, 16-20 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 2A, Prong 1:
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 phenomena (Step 2A, Prong 1). In the instant application, claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 recite a method. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “determining the sequence of the polymer by processing the series of measurements … to obtain a determined sequence of the polymer, the determining including processing at least one measurement obtained from a non-canonical polymer unit of the non-canonical polymer units of the polymer … maps the one or more measurements of a first polymer unit to an output indicating that the first non-canonical polymer unit giving rise to the one or more measurements is a first canonical polymer unit corresponding to the first non-canonical polymer unit …”
Claim 14 recites “analyzing the series of measurements …; and estimating canonical polymer units that correspond to the non-canonical units that have been substituted in the polymer training strands.”
Regarding the above cited limitations in claims 1 and 14 of determining, analyzing and estimating, these limitations equate to a mental process. A human with their mind or pen and paper can make determinations and estimations by analyzing data. A human could practically determine a sequence by analyzing current changes from a nanopore sequencer as well as identify current changes from modified bases as their corresponding unmodified bases. These limitations are similar to the concepts of observation, evaluation, judgment, and opinion, which the courts have established as concepts that a human could practically perform in their mind or by using pen and paper. Therefore, these limitations equate to the mental process subgrouping of abstract ideas. See MPEP 2106.04(a)(2), subsection III.
As such, claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 recite an abstract idea (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The instant claims recite the following additional elements:
Claim 1 recites “placing the polymer into a nanopore measurement and analysis system; and sequencing the polymer using the nanopore measurement and analysis system at least in part by: translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system; measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore to generate a series of measurements; and … with a trained recurrent neural network … wherein the trained recurrent neural network has been trained such that: when the trained recurrent neural network receives, as input, one or more measurements of a first non-canonical polymer unit, the recurrent neural network … ; outputting the determined sequence of the polymer at least in part by outputting an identification of the first canonical polymer unit as belonging to the determined sequence of the polymer.”
Claim 3 recites “wherein the polymer comprises two or more types of non-canonical polymer units corresponding to two or more types of canonical polymer units.”
Claim 5 recites “wherein the polymer comprises non-canonical polymer units corresponding to each type of canonical polymer unit.”
Claim 7 recites “the polymer comprises plural non-canonical polymer units for each of one or more types of non-canonical polymer unit present in the polymer.”
Claim 8 recites “wherein at least one non-canonical polymer unit of the non-canonical units of the polymer corresponds to more than one canonical polymer unit of the polymer.”
Claim 9 recites “wherein the polymer comprises approximately 50% of non-canonical polymer units.”
Claim 10 recites “wherein the non-canonical polymer units comprise a modified canonical polymer unit.”
Claim 11 recites “wherein the modified non-canonical polymer unit is naturally modified.”
Claim 13 recites “wherein the electrical signals are electrical signals indicative of ion current flow through the nanopore or electrical signals indicative of a voltage across the nanopore during translocating of the polymer.”
Claim 14 recites “wherein the trained recurrent neural network has been trained by a training method comprising the steps of: providing polymer training strands comprising a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in each of the target polymers; taking a series of measurements of signals relating to the target polymers; … using the trained recurrent neural network …”
Claim 16 recites “wherein the polymer is a polynucleotide and canonical and non-canonical polymer units of the polynucleotide are nucleotide bases.”
Claim 17 recites “wherein the non-canonical polymer units comprise one or more non-canonical bases that have been modified by means of an enzyme.”
Claim 18 recites “further comprising modifying a canonical polymer to provide the polymer comprising the one or more non-canonical bases of one or more different types.”
Claim 19 recites “wherein the polynucleotide comprising one or more non-canonical bases of one or more different types is generated from its complement by use of a polymerase and a proportion of non-canonical bases.”
Claim 20 recites “wherein the polynucleotide is DNA”
Claim 23 recites “wherein a polymer training strand of the polymer training strands comprises more than one type of non-canonical polymer unit.”
Regarding the above cited limitations in claims 1 and 14 of “with a trained recurrent neural network” and “using the trained recurrent neural network”, these limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The following paragraph discusses these three considerations.
The trained recurrent neural network (RNN) performs the following abstract ideas: in claim 1 of “determining the sequence of the polymer”, “determining including processing at least one measurement”, and “maps the one or more measurement” as well as in claim 14 of “analyzing the series of measurements” and “estimating canonical polymer units”. The trained RNN is used to generally apply the abstract ideas without placing any limits on how the trained RNN functions. Rather, these limitations only recite the outcome of the abstract ideas and do not include any details about how the determining, mapping, analyzing, or estimating are accomplished. See MPEP 2106.05(f). The recitation of “with a trained recurrent neural network” and “using the trained recurrent neural network” also indicate a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exceptions, they merely confine the use of the abstract idea to a particular technological environment (i.e., recurrent neural networks) and thus fail to add an inventive concept to the claims. See MPEP 2106.05(h).
Regarding the above cited limitations in claims 1, 13-14 and 23 of (1) placing the polymer into a nanopore measurement and analysis system, (2) sequencing the polymer using the nanopore measurement and analysis system at least in part by translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system, (3) measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore to generate a series of measurements, (4) taking a series of measurements of signals relating to the target polymers, (5) wherein the measuring is performed during translocation of the polymer through the nanopore, (6) wherein the electrical signals are electrical signals indicative of ion current flow through the nanopore or electrical signals indicative of a voltage across the nanopore duringcomprising a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in the target polymer, (8) wherein a polymer training strand of the polymer strands comprises more than one type of non-canonical polymer unit, and (9) “wherein the trained recurrent neural network has been trained such that: when the trained recurrent neural network receives, as input, one or more measurements of a first non-canonical polymer unit, the trained recurrent neural network.” These limitations equate to insignificant, extra-solution activity of mere data gathering because they collect data before implementing the abstract ideas of “determining the sequence of the polymer” and “analyzing the series of measurements … estimating canonical polymer units”.
Regarding the above cited limitations in claim 1 of “outputting the determined sequence of the polymer at least in part by outputting an identification of the first canonical polymer unit as belonging to the determined sequence of the polymer.” This limitation equates to insignificant, extra-solution activity of data outputting because it outputs the result of the judicial exception of “determining the sequence of the polymer.”
Regarding the above cited limitation in claim 18, this limitation equates to an insignificant application because it does not add more than insignificant extra-solution activity to the judicial exception. This limitation also equates to the equivalent of the words “apply it” because it modifies the target polymer before the judicial exception of determining the sequence of the polymer in claim 1.
Regarding the above cited limitations in claims 3, 5, 7-11, 16-17, 19-20 and 23, these limitations are considered field-of-use limitations because they limit the polymer, non-canonical polymer/bases, polynucleotide, polymer training strand, and modified non-canonical polymer unit.
As such, claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 are directed to an abstract idea (Step 2A, Prong 2: 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). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 1 recites “placing the polymer into a nanopore measurement and analysis system; and sequencing the polymer using the nanopore measurement and analysis system at least in part by: translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system; measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore to generate a series of measurements; and … with a trained recurrent neural network … wherein the trained recurrent neural network has been trained such that: when the trained recurrent neural network receives, as input, one or more measurements of a first non-canonical polymer unit, the recurrent neural network … ; outputting the determined sequence of the polymer at least in part by outputting an identification of the first canonical polymer unit as belonging to the determined sequence of the polymer.”
Claim 3 recites “wherein the polymer comprises two or more types of non-canonical polymer units corresponding to two or more types of canonical polymer units.”
Claim 5 recites “wherein the polymer comprises non-canonical polymer units corresponding to each type of canonical polymer unit.”
Claim 7 recites “the polymer comprises plural non-canonical polymer units for each of one or more types of non-canonical polymer unit present in the polymer.”
Claim 8 recites “wherein at least one non-canonical polymer unit of the non-canonical units of the polymer corresponds to more than one canonical polymer unit of the polymer.”
Claim 9 recites “wherein the polymer comprises approximately 50% of non-canonical polymer units.”
Claim 10 recites “wherein the non-canonical polymer units comprise a modified canonical polymer unit.”
Claim 11 recites “wherein the modified non-canonical polymer unit is naturally modified.”
Claim 13 recites “wherein the electrical signals are electrical signals indicative of ion current flow through the nanopore or electrical signals indicative of a voltage across the nanopore during translocating of the polymer.”
Claim 14 recites “wherein the trained recurrent neural network has been trained by a training method comprising the steps of: providing polymer training strands comprising a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in each of the target polymers; taking a series of measurements of signals relating to the target polymers; … using the trained recurrent neural network …”
Claim 16 recites “wherein the polymer is a polynucleotide and canonical and non-canonical polymer units of the polynucleotide are nucleotide bases.”
Claim 17 recites “wherein the non-canonical polymer units comprise one or more non-canonical bases that have been modified by means of an enzyme.”
Claim 18 recites “further comprising modifying a canonical polymer to provide the polymer comprising the one or more non-canonical bases of one or more different types.”
Claim 19 recites “wherein the polynucleotide comprising one or more non-canonical bases of one or more different types is generated from its complement by use of a polymerase and a proportion of non-canonical bases.”
Claim 20 recites “wherein the polynucleotide is DNA”
Claim 23 recites “wherein a polymer training strand of the polymer training strands comprises more than one type of non-canonical polymer unit.”
Regarding the above cited limitations in claims 1, 14 and 23 of “wherein the trained recurrent neural network has been trained such that: when the trained recurrent neural network receives, as input, one or more measurements of a first non-canonical polymer unit, the trained recurrent neural network”, “outputting the determined sequence of the polymer at least in part by outputting an identification of the first canonical polymer unit as belonging to the determined sequence of the polymer”, “providing polymer training strands comprising a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in the target polymer”, and “wherein a polymer training strand of the polymer strands comprises more than one type of non-canonical polymer unit”. The BRI of these limitations includes that they are computer-implemented, especially because the instant specifications states that these steps may be performed on a computer (pg. 20, lines 7-8). Therefore, these limitations equate to receiving/transmitting data over a network, which is a WURC function of a generic computer as established by the courts 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 and 14 of “with a trained recurrent neural network” and “using the trained recurrent neural network”, these limitations equate to instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). See discussion above in Step 2A, Prong 2.
Regarding the above cited limitations in claims 1 and 13-14 (1) placing the polymer into a nanopore measurement and analysis system, (2) sequencing the polymer using the nanopore measurement and analysis system at least in part by translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system, (3) measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore to generate a series of measurements, (4) taking a series of measurements of signals relating to the target polymers, (5) wherein the measuring is performed during translocation of the polymer through the nanopore, and (6) wherein the electrical signals are electrical signals indicative of ion current flow through the nanopore or electrical signals indicative of a voltage across the nanopore duringThese limitations, when considered individually and in combination, equate to be WURC limitation as taught by Gundlach et al. (“Gundlach”; US 10,017,814 B2, published 07/10/2018, pg. 1-21; previously cited on PTO892 mailed 06/05/2024), Chao-Chi Pan et al. (“Pan”; US 8,486,630 B2, pg. 1-44, published 07/16/2013; previously cited on PTO892 mailed 06/05/2024), and Ecker et al. (“Ecker”; US 2018/0164280 A1, pg. 1-49, published 06/14/2018; previously cited on PTO892 mailed 06/05/2024).
Gundlach discloses a method/system for improving nanopore-based analysis of polymers that modifies one or more monomeric subunits of a polymer that produces a detectable signal in a nanopore-based system (abstract). Gundlach states that the word modified indicates that a structural change exists in the subunit of the polymer analyte that results in a distinguishable signal from the signal produced by the corresponding unmodified or original subunit in the pre-analyte polymer (col. 7, lines 23-30). Gundlach translocates a polymer containing the modified subunit through a nanopore wherein an ion current is measured to detect the modified subunit (col. 2, lines 46-59).
Pan discloses methods for determining the sequence of nucleic acids and for identifying the positions of modified bases in nucleic acids (abstract). Pan states that the nucleic acids can contain modified bases such as 5-methylcytosine, 5,6-dihydrouracil or ribothymine (col. 22, lines 64-67). Pan states that nanopore sequencing may be used wherein each base alters a current to a create a different, distinguishable degree to uniquely identify a base (col. 17, lines 1-8). Pan discloses threading a single stranded nucleic acid molecule through a nanopore to collect ion current data, wherein a specific base is identified by the alterations it causes on the current (col. 17, lines 1-8).
Ecker discloses methods for modifying a nucleic acid, translocating the modified nucleic acid through a nanopore, measuring an electrical signal produced by the translocation of the modified nucleic acid through the nanopore, and characterizing the nucleic acid by analyzing the electrical signal (claim 69). Ecker states that the electrical signal for the modified nucleotides are distinct from other bases [4], and the absolute position of a nucleotide in the nucleic acid can be determined [66]. Ecker discloses in claim 69 translocating the modified nucleic acid through a nanopore and measuring the electrical signal produced by translocation to then characterize the nucleic acid based on the electrical signal.
Regarding the above cited limitations in claim 7, Gundlach recites a scenario where one or more cytosine subunits in a polymer are methylated (col. 8, lines 63-65). Pan shows in Figures 15 and 16 that the nucleic acid sequence may have at least two modified bases along with other canonical bases. Ecker states that a polymer may 50% modified and 50% unmodified bases [194].
Regarding the above cited limitations in claims 10-11, Gundlach states that in the instance of DNA and RNA cytosine or guanine may be modified by methylation by using a methyltransferase enzyme (col. 8, line 63 – col. 9, line 3; col. 9, lines 39-45). Pan states that naturally occurring nucleic acids can be modified by enzymes such as a methyltransferase (col. 12, lines 3-5). Ecker states that a nucleobase may be a naturally occurring or synthetic modified base [48].
Regarding the above cited limitations in claims 16-18 and 20, Gundlach states that a methyltransferase may be used to modify a nucleotide (col. 9, lines 39-45) and that the pre-analyte polymer may be a nucleic acid, wherein the nucleic acids are DNA or RNA polymers (col. 6, line 65 – col. 7 line 8). Pan states that a nucleic acid may be modified by a methyltransferase (col. 12, lines 2-3) and nucleic acids refers to polynucleotides (col. 9, lines 53-54). Ecker discloses that their method modifies nucleotides in DNA or RNA [5] and uses a methyltransferase to modify bases [125].
Regarding the above cited limitations in claim 19, the instant specification states that any known method for preparing polynucleotides comprising non-canonical and polymer units can be used (pg. 12, lines 19-23). The instant specification also states that a number of methods exist for determining the sequence of a polynucleotide that contains modified nucleotides (pg. 2, lines 20-29).
Regarding the above cited limitations of claims 3, 5 and 8, Ansorge (New biotechnology, 25(4), 195-203; published 2009; previously cited on PTO892 mailed 06/05/2024) discloses a commercially available sequencer from VisiGen Biotechnologies that uses an engineered DNA polymerase with a donor fluorescent dye incorporated close to the active site involved in the selection of nucleotides during synthesis, wherein all four nucleotides to be integrated have been modified each with a different acceptor dye (pg. 199, col. 2, para. 3). During the synthesis, when the correct nucleotide is found, selected and enters the active site of the enzyme, the donor dye label in the polymerase comes into close proximity with the acceptor dye on the nucleotides and energy is transferred from donor to acceptor dye giving rise to a fluorescent resonant energy transfer (FRET) light signal (pg. 199, col. 2, para. 3). The frequency of this signal varies depending on the label incorporated in the nucleotides, so that by recording frequencies of emitted FRET signals it will be possible to determine base sequences (pg. 199, col. 2, para. 3).
Regarding the above cited limitations in claim 9, Edelman (WO 2018/115855 A1, pg. 1-281, published 06/28/2018; previously cited on PTO892 mailed 06/05/2024) discloses a method for analysis of nucleic acids (abstract), wherein nanopore sequencing may be used (pg. 4, lines 33-39) and 50% of the nucleotides may be modified nucleotides (pg. 58, lines 8-24). Ecker also discloses using nanopore sequencing of nucleic acids (abstract) [2], wherein the nucleic acid can be a mixture of 50% canonical and 50% non-canonical bases [193-194]. Belgrader et al. (US 2018/0179591 A1, pg. 1-149, published 06/28/2018; previously cited on PTO892 mailed 06/05/2024) discloses methods for polynucleotide processing (abstract) that uses nanopore sequencing [117] [263], wherein the polynucleotide may comprise 50% modified bases [294].
When these limitations are considered individually and in combination, they do not comprise an inventive concept because they equate to applying the judicial exception, generic functions of a computer, and are WURC limitation as taught above by Gundlach, Pan, Ecker, Belgrader, Ansorge, and Edelman. Therefore, these limitations do not comprise an inventive concept that transforms the claimed judicial exception into a patent-eligible application of the judicial exception (Step 2B: No).
As such, claims 1, 3, 5, 7-11, 13-14, 16-20 and 23 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 12/01/2025 have been fully considered but they are not persuasive.
Applicant argues that the claims do not recite any abstract ideas (pg. 9, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive because claims 1 and 14 have been identified as reciting mental processes.
Applicant argues that claim 1 is patent eligible under Step 2B because it recites limitations that are not well-understood, routine, and conventional (WURC) (pg. 9, para. 2 – pg. 10 of Applicant’s remarks). Applicant’s remarks are not persuasive for the following reasons:
The additional elements in claim 1 pertaining to nanopore sequencing are WURC as taught above by Gundlach, Pan and Ecker.
The additional element in claim 1 of “with a trained recurrent neural network”, including the limitations defining how the neural network was previously trained, equate to mere instructions to implement the abstract idea on a computer, which do not provide significantly more under Step 2B. See MPEP 2106.05(f) and section Step 2A, Prong 2 above. Applicant is also directed to claim 2 of Example 47 and claim 1 of Example 48 of the subject matter eligibility examples issued by the Office in 2024.
The additional elements in claim 1 of outputting the determined sequence equates to transmitting/receiving data over a network, which does not recite an inventive concept under Step 2B (MPEP 2106.05(d)(II)).
As such, the additional elements in claim 1 do not provide an inventive concept because they equate to WURC limitations of nanopore sequencing, instructions to implement an abstract idea on a computer, and to WURC functions of a generic computer.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5, 7-8, 10-11, 13-14, 16-20 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Gundlach et al. (“Gundlach”; US 10,017,814 B2, published 07/10/2018; previously cited on PTO892 mailed 06/05/2024) in view of Rang et al. (“Rang”; NPL ref. on IDS filed 04/19/2024; Genome biology, 19(1), 90, pg. 1-11; published 07/13/2018; previously cited).
Any newly recited portions herein are necessitated by claim amendment.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claim 1:
A method of determining a sequence of a polymer, comprising canonical and non-canonical polymer units, the method comprising:
Gundlach discloses a nanopore-based system that performs measurements on polymers (nanopore measurements system) (abstract), and states that current level patterns associated with oligonucleotides were subject to data analysis tools (analysis system) (col. 21, lines 1-5). The polymers contain canonical and noncanonical subunits (col. 6, lines 1-8) (col. 6, lines 35-41).
placing the polymer into a nanopore measurement and analysis system; and sequencing the polymer using the nanopore measurement and analysis system at least in part by: translocating at least a portion of the polymer through a nanopore of the nanopore measurement and analysis system;
Gundlach discloses translocating the polymer, which contains canonical and noncanonical subunits (col. 6), through a nanopore to sequence the polymer and determine modified, i.e. noncanonical, subunits based on measured ion currents (col. 2, lines 40-67) (col. 21, lines 42-45).
measuring, using the nanopore measurement and analysis system, electrical signals generated during the translocating of the polymer through the nanopore to generate a series of measurements; and
Gundlach translocates the polymer analyte through the nanopore to be measured by an ion current (col. 11, lines 11-26 and col. 12, lines 22-38). The nanopore system includes a patch-clamp amplifier that applies a voltage across a bilayer used to measure the ionic current flowing through the nanopore (col. 15, lines 29-56). Figures 1 and 2 show a series of measurements.
determining the sequence of the polymer by processing the series of measurements with a trained recurrent neural network to obtain a determined sequence of the polymer, the determining including processing at least one measurement obtained from a non-canonical polymer unit of the non-canonical polymer units of the polymer,
Gundlach determines the sequence of the polymer (col. 16, lines 13-32), which contains canonical and noncanonical subunits (col. 6). Changes in signal are measured to determine the overall sequence of the polymer analyte (col. 5, lines 20-33).
However, Gundlach does use a trained recurrent neural network (RNN) to determine the sequence of the polymer by processing the series of measurements.
Rang shows in Figure 2 a RNN determining the sequence of a polymer by processing signals measured by nanopore sequencing. Rang discloses two RNNs that perform based calling by using nanopore sequencing, DeepNano and Nanonet, which can be trained (with a recurrent neural network) (pg. 8, col. 1) (Table 1) (Figure 3). The polymer may contain base modification that can be base called (abstract) (pg. 8, col. 2, last para. – pg. 9, col. 1, para. 1) or ignored (Figure 2).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Gundlach for determining the sequence of a polymer containing non-canonical and canonical bases by using a RNN as taught by Rang. Rang teaches motivation by stating that nanopore sequencing has a high error rate between 5% to 15% (abstract) which can be improved by RNNs (caption of Figure 2) (pg. 9, col. 2, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success because Rang states that RNN can be used with nanopore sequencing (Figure 2), wherein Gundlach uses nanopore sequencing (col. 4, lines 40-49).
wherein the trained recurrent neural network has been trained such that: when the trained recurrent neural network receives, as input, one or more measurements of a first non-canonical polymer unit, the trained recurrent neural network maps the one or more measurements of a first polymer unit to an output indicating that the first non-canonical polymer unit giving rise to the one or more measurements is a first canonical polymer unit corresponding to the first non-canonical polymer unit; and
The broadest reasonable interpretation (BRI) of these limitations includes them being a product by process. MPEP 2113.I recites “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." In the instant case, the product is “the trained recurrent neural network” and the training process previously performed to derive the product is the limitations recited above. Thus, any trained RNN, even if trained by a different method as recited in instant claim 1, will read on the claimed trained RNN if it is capable of “determining the sequence of the polymer by processing the series of measurements … to obtain a determined sequence of the polymer, the determining including processing at least one measurement obtained from a non-canonical polymer unit of the non-canonical polymer units of the polymer”. The trained RNN in Rang used to determine a polymer sequence, containing noncanonical and canonical bases, based on nanopore measurements reads on the trained RNN of the instant claim.
However, in the interest of compact prosecution, even if these limitations required an active step of training, Rang discloses them.
Gundlach determines the sequence of the polymer (col. 16, lines 13-32), which contains canonical and noncanonical subunits (col. 6), based on measured ionic current changes in the nanopore (col. 2, lines 57-58).
However, Gundlach does not teach training a RNN to map non-canonical polymer measurements to an output indicating that the non-canonical polymer measurement is a corresponding canonical polymer.
Rang shows in Figures 2 and 3 a RNN determining the sequence of a polymer by processing signals measured by nanopore sequencing. The parameters of a RNN recognize modified bases and to call them as their canonical nucleotide (e.g., 5-mC as C) (pg. 7, col. 2, para. 3). The BRI of mapping a non-canonical measurement to an output that indicates it being a canonical polymer, includes base calling the recognized noncanonical polymer as its canonical polymer. The outputted canonical polymer is the indication.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the nanopore sequencing method of Gundlach to use a RNN trained to recognize noncanonical bases and call them as their corresponding canonical base as taught by Rang. The motivation for doing so is taught by Rang who recites “chemical modifications such as methyl groups influence the signal. If such DNA modifications are not represented in the training data, they may result in erroneous base calls … The problem may be solved either by training the parameters to recognize modified bases and to call them as their canonical nucleotide (e.g., 5-mC as C)” (pg. 7, col. 2, para. 3).
One of ordinary skill in the art would have had a reasonable expectation of success because both Gundlach and Rang are directed to nanopore sequencing. Additionally, Rang teaches that
“efforts are being made by the ONT community to include modified bases in the RNN” (pg. 7, col. 2, para. 3).
outputting the determined sequence of the polymer at least in part by outputting an identification of the first canonical polymer unit as belonging to the determined sequence of the polymer.
Gundlach discloses outputting ion current differences produced by the polymer analytes passed through thorough the nanopore, and determining the sequence of the overall polymer analyte (col. 5, lines 20-33). However, Gundlach does not output an identification that indicates a non-canonical polymer is a corresponding canonical polymer.
Rang shows in Figure 3 outputting a determined sequence. Because Rang discloses base calling modified bases as their canonical nucleotide, the outputted sequence in Figure 3 would serve as an output indicating the modified base as its canonical nucleotide (pg. 7, col. 2, para. 3).
Claims 3, 5, 7 and 10:
Gundlach teaches that the polymer contains noncanonical subunits such as 5-methyl-cytosine, 8-oxoguanine, 2-amino-adenosine, and 2-thiothymidine that correspond to their unmodified subunit (col. 6, lines 35-50) (col. 7, lines 25-30). The polymer can comprise any combination of these non-canonical subunits (col. 7, lines 4-7). One or more cytosine subunits in a polymer are methylated (the target polymer comprises plural non-canonical polymer units for each of one or more types of non-canonical polymer unit present) (col. 8, lines 63-65).
Claim 8:
Gundlach teaches that a modified subunit may produce an ion current that overlaps with an ion current produced by an existing subunit, which may be unmodified, of a different type (e.g., a modified cytosine may produce signals similar to a canonical cytosine and a canonical thymine) (col. 10, lines 58-61).
Claim 13:
Gundlach discloses translocating the polymer analyte through the nanopore to be measured by an ion current (col. 11, lines 11-26 and col. 12, lines 22-38). Gundlach states that the nanopore system includes a patch-clamp amplifier that applies a voltage across a bilayer used to measure the ionic current flowing through the nanopore (col. 15, lines 29-56).
Claim 14:
wherein the trained recurrent neural network has been trained by a training method comprising the steps of: providing polymer training strands comprising a plurality of target polymers comprising non-canonical units that have been substituted for equivalent canonical units at varying sequence positions in each of the target polymers;
Gundlach states that for a plurality of pre-analyte polymers, target polymer subunits may be modified, wherein the plurality of pre-analyte polymers comprise a common sequence (col. 3, lines 38-59). A consensus map of the plurality of ion currents measured can be used to detect the presence of multiple modified subunits in the common sequence (col. 3, lines 38-59). The canonical subunits of DNA and RNA (col. 6, lines 2-8) may be modified to become 5-methyl cytosine, 2-thiothymidine, 8-oxoguanine, or 2-amino-adenosine (col. 6, lines 35-50).
However, Gundlach does not teach training a RNN nor using training polymer strands.
Rang trains RNN (Table 1). Rang teaches training RNN using modified bases. It would have been prima facie obvious to one of ordinary skill in the art to have modified the nanopore sequencing method of Gundlach to train a RNN using modified bases, as taught by Rang. The motivation for doing so is taught by Rang who teaches that bases callers require training to optimize parameters (pg. 7, col. 1, last para.) and that the training data should contain modified bases (pg. 7, col. 2, para. 3). One of ordinary skill in the art would have expected a reasonable expectation of success because Gundlach uses polymers with canonical and noncanonical bases for nanopore sequencing, wherein Rang discloses a RNN for nanopore sequencing that can be trained on modified bases.
taking a series of measurements of signals relating to the target polymers; analyzing the series of measurements using the trained recurrent neural network; and
Gundlach states that the plurality of pre-analyte polymers is measured to produce a plurality of measured ion currents (col. 8, lines 4-6). However, Gundlach does not teach analyzing the measurements using a trained RNN.
Rang shows in Figure 2 the use of a recurrent neural network (RNN) to determine the sequence of a polymer by processing signals measured by nanopore sequencing. Rang also discloses two RNN that perform based calling by using nanopore sequencing, DeepNano and Nanonet (Figure 3).
estimating canonical polymer units that correspond to the non-canonical units that have been substituted in the polymer training strands.
Gundlach discloses determining the sequence of the polymer (col. 16, lines 13-32). Gundlach detects changes in signal to determine the overall sequence of the polymer analyte (col. 5, lines 20-33).
As discussed by Rang above in claim 1, Rang suggests training a RNN to base call non-canonical units as canonical units. Therefore, if the RNN of Rang were applied to analyze the measurements of Gundlach, who modifies bases in a polymer, then the RNN of Rang would base call the modified monomeric subunits of Gundlach as their corresponding canonical subunits.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the nanopore sequencing method of Gundlach to use a RNN trained to recognize noncanonical bases and call them as their corresponding canonical base as taught by Rang. The motivation for doing so is taught by Rang who recites “chemical modifications such as methyl groups influence the signal. If such DNA modifications are not represented in the training data, they may result in erroneous base calls … The problem may be solved either by training the parameters to recognize modified bases and to call them as their canonical nucleotide (e.g., 5-mC as C), or by treating them as distinct bases” (pg. 7, col. 2, para. 3).
One of ordinary skill in the art would have had a reasonable expectation of success because both Gundlach and Rang are directed to nanopore sequencing. Additionally, Rang teaches that
“efforts are being made by the ONT community to include modified bases in the RNN” (pg. 7, col. 2,
Claims 16 and 20:
Gundlach states that the pre-analyte polymer may be a nucleic acid, wherein the nucleic acids are DNA or RNA polymers (col. 6, line 65 – col. 7 line 8). The polymer contains canonical and noncanonical DNA subunits (col. 6).
Claims 11 and 17:
Gundlach discloses modifying cytosine by contacting a pre-analyte polymer with a methyltransferase (col. 8, lines 63 – col. 9, line 1). The broadest reasoning of a naturally modifying a canonical polymer to a noncanonical polymer includes using naturally occurring elements such as a methyltransferase.
Claim 18:
Gundlach discloses modifying both cytosine and guanine using a methyltransferase (col. 8, lines 63-65 and col 9, lines 39-46) and discloses that more than one of the target polymer subunits of a kind are modified in a pre-analyte (col. 7, lines 65-67). Therefore, a pre-analyte can have modified cytosine and guanine nucleotides.
Claim 19:
Gundlach recites “the cytosine residues in a nucleic acid polymer can be replaced with modified cytosines, such as 5-methylcytosine. This can be accomplished by performing PCR or primer extension reactions using a polymerase with the proper dNTP mix that has dC replaced with the modified versions, such as 5-methyl-dCTP” (col. 22, lines 33-38), and “the guanine residues in a nucleic acid polymer can be replaced with modified guanines, such as 7-methylguanine. This can be accomplished by performing PCR or primer extension reactions using a polymerase with the proper dNTP mix that has dG substituted with 7-methyl-dGTP” (col. 22, lines 51-56).
Claim 23:
Gundlach discloses sequencing polymers with modified and unmodified bases (abstract) (col. 6). However, Gundlach does use a polymer training strand comprising more than one type of non-canonical polymer unit.
Rang trains RNN (Table 1). Rang teaches training RNN using modified bases. It would have been prima facie obvious to one of ordinary skill in the art to have modified the nanopore sequencing method of Gundlach to train a RNN using modified bases, as taught by Rang. The motivation for doing so is taught by Rang who teaches that bases callers require training to optimize parameters (pg. 7, col. 1, last para.) and that the training data should contain modified bases (pg. 7, col. 2, para. 3). One of ordinary skill in the art would have expected a reasonable expectation of success because Gundlach uses polymers with canonical and noncanonical bases for nanopore sequencing, wherein Rang discloses a RNN for nanopore sequencing that can be trained on modified bases.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gundlach et al. (“Gundlach”; US 10,017,814 B2, published 07/10/2018; previously cited on PTO892 mailed 06/05/2024) in view of Rang et al. (“Rang”; NPL ref. on IDS filed 04/19/2024; Genome biology, 19(1), 90, pg. 1-11; published 07/13/2018; previously cited), as applied above to claim 1, and in further view Ecker et al. (“Ecker”; US 2018/0164280 A1; published 06/14/2018; previously cited on PTO892 mailed 06/05/2024).
Any newly recited portions herein are necessitated by claim amendments.
The limitations of claim 1 have been taught in the rejection above by Gundlach and Rang.
Claim 9:
Gundlach discloses polymers that contain selectively modified monomeric units (abstract; claim 1 and 13). However, Gundlach and Rang do not teach that the polymer contains about 50% non-canonical polymer units.
Ecker discloses sequencing a nucleic acid with mixed canonical and non-canonical bases using a mixture of 50% methyl-dCTP, methyl-dATP, methyl-dGTP, and methyl-dTTP / methyl-dUTP mixed with 50% natural dCTP, dATP, dGTP, and dTTP / dUTP [193-194].
It would have been prima facie obvious one of ordinary skill in the art to have modified the nanopore sequencing method of Gundlach and Rang by using 50% modified polymers, as taught by Ecker, because Ecker states that introduction of modified bases specifically in homopolymer stretches reduces sequencing errors [193-194]. One of ordinary skill in the art would have had a reasonable expectation of success because these references all use modified and unmodified bases during nanopore sequencing.
Response to Arguments under 35 USC 103
Applicant's arguments filed 12/01/2025 on pg. 7, para. 4 – pg. 8, para. 2 have been fully considered but they are not persuasive in view of the newly cited portions of Gundlach and Rang necessitated by claim amendment. Gundlach in view of Rang teach the emphasized language, as discussed in the rejection above.
Applicant’s arguments are also not persuasive because the trained recurrent neural network (RNN) recites a product by process (MPEP 2113.I). The trained RNN of Rang is the same product as the trained RNN in instant claim 1, even if trained by a different method. See further discussion in the rejection above.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685