DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-31 are pending and examined on the merits.
Priority
The instant application filed on 6/1/2022 claims the benefit of priority to U.S. Provisional Patent Applications No. 63/216,404 and 63/216,419 both filed on 6/29/2021. Thus, the effective filing date of the claims is 6/29/2021.
The applicant is reminded that amendments to the claims and specification must comply with 35 U.S.C. § 120 and 37 C.F.R. § 1.121 to maintain priority to an earlier-filed application. Claim amendments may impact the effective filing date if new subject matter is introduced that lacks support in the originally filed disclosure. If an amendment adds limitations that were not adequately described in the parent application, the claim may no longer be entitled to the priority date of the earlier filing.
Information Disclosure Statement
The IDS forms filed on 7/28/2022 (7 total IDS with this date), 11/23/2022, and 4/24/2025 have been entered and considered. A signed copy of the corresponding 1449 forms with any deficiencies noted have been included with this Office action. The information disclosure statement filed 7/28/2022 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered:
7/28/2022 group (total of 7 IDS), provided in file wrapper, but not found in IDS: No author, Proteins Change Through Evolutionary Processes, section title "When two protein sequences are similar, their structures and functions are usually similar too" (possibly 7/28/2022 (7/7), Cite No 36).
7/28/2022 (3/7), Cite No 32 (Alberts, Bruce, et al. "Molecular biology of the cell", Sixth Edition, 2015, 3 pages), pages 2-3 too far zoomed in to consider properly.
7/28/2022 (6/7), Cite No 8 (Kircher et al, Improved base calling for the Illumina Genome Analyzer using machine learning strategies, dated 14 August 2009, 10 pages (ILLM1020-3 WO)), not found in file wrapper.
7/28/2022 (7/7), Cite No 36 (Bahar, Protein Actions Principles and Modeling, Chapter 7, 2017 pp. 165-166), not found in file wrapper.
Claim Rejections - 35 USC § 112
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 7-11 and 13 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 7-11 recite "substantially and uniquely matching" or "substantially matched" (claim 7 line 5, claim 8 line 2, claim 9 lines 1-2, claim 10 line 1, and claim 11 line 4). The metes and bounds of "substantially" and "uniquely" matching are not clear. The instant specification suggests that for a match to be "substantial", the match may not be 100% and there may be one or more errors in the match (para.00313), and for a match to be "unique”, the subsequence may only occur a single time in the organism sequence (and conversely a non-unique matching would result as a mapping being declared as being inconclusive) (para.00314). Furthermore, the combination of “substantially and uniquely” matching as used in these limitations is unclear with respect to order of matching operations. To further prosecution, the limitations of matching are interpreted as a sequence or subsequence substantially matching a unique sequence, or the like.
Claim 7 recites "substantially and uniquely matching the initial L2 bases of the first predicted base sequence with consecutive L2 bases of the first organism base sequence" in lines 5-6. There is insufficient antecedent basis for “the first predicted base sequence”. To further prosecution, the limitation is interpreted as “the first predicted base subsequence".
Claim 8 recites "while the substantially and uniquely matching the initial L2 bases of the first predicted base sequence, refraining from aiming to match the subsequent L3 bases of the first predicted base sequence with any base of the first organism base sequence" in lines 2-4. There is insufficient antecedent basis for the two instances of “the first predicted base sequence”. To further prosecution, the instances of this term are interpreted as “the first predicted base subsequence".
Claim 9 recites "the initial L2 bases of the first predicted base sequence is substantially matched with the consecutive L2 bases of the first organism base sequence, such that at least a threshold number of bases of the initial L2 bases of the first predicted base sequence is matched with the consecutive L2 bases of the first organism base sequence" in lines 1-4. There is insufficient antecedent basis for the two instances of “the first predicted base sequence”. To further prosecution, the instances of this term are interpreted as “the first predicted base subsequence".
Claim 10 recites "the initial L2 bases of the first predicted base sequence is uniquely matched with consecutive L2 bases of the first organism base sequence, such that the initial L2 bases of the first predicted base sequence is substantially matched with only the consecutive L2 bases of the first organism base sequence" in lines 1-3. There is insufficient antecedent basis for the two instances of “the first predicted base sequence”. To further prosecution, the instances of this term are interpreted as “the first predicted base subsequence".
Claim 11 recites "failing to substantially and uniquely match (i) an initial L2 bases of the L1 bases of the third predicted base sequence with consecutive L2 bases of the first organism base sequence" in lines 4-5. There is insufficient antecedent basis for “the third predicted base sequence”. To further prosecution, the instances of this term are interpreted as “the first predicted base subsequence".
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4-17 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 4, "populating (i) a first cluster of a plurality of clusters of a flow cell with a first base subsequence of the first plurality of base subsequences of the first organism, (ii) a second cluster of the plurality of clusters of the flow cell with a second base subsequence of the first plurality of base subsequences of the first organism, and (iii) a third cluster of the plurality of clusters of the flow cell with a third base subsequence of the first plurality of base subsequences of the first organism" is not enabled by the "computer-implemented method" disclosed. The computer system has no disclosed parts or features that are enabled for physical sample manipulation (that of populating a sequencing chip with clusters of specific base subsequences). To further prosecution, the limitation of populating the chip is ignored, and the subsequent limitation of receiving is interpreted as "receiving (i) a first sequence signal from a first cluster indicative of a first base subsequence of the first plurality of base subsequences of the first organism, (ii) a second sequence signal from a second cluster indicative of a second base subsequence of the first plurality of base subsequences of the first organism, and (iii) a third sequence signal from the third cluster indicative of a third base subsequence of the first plurality of base subsequences of the first organism".
Claims 5-17 depend from claim 4, therefore are also rejected under 35 USC 112(a).
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 5, 11, and 14 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 5 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 5 recites "prior to generating the first, second, and third predicted base subsequences, training the base caller using labelled training data generated during initially training the base caller", which does not further limit claim 4 because claim 1 already recites "further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller".
Claim 11 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 11 recites "failing to substantially and uniquely match (i) an initial L2 bases of the L1 bases of the third predicted base sequence with consecutive L2 bases of the first organism base sequence", which does not further limit claim 4 because in order for a match to fail, mapping must precede it, which has already occurred in claim 4. Additionally, something failing to happen is not a positive active step of a method. Therefore, claim 11 does not further limit claim 4 because there is no positive active step (mapping would be the active step in this instance), and claim 4 has already mapped the sequences. Examiner notes this limitation may be better suited as a "wherein" clause.
Claim 14 rejected as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Claim 14 recites "the first, second, and the third sequence signals generated during the first iteration are reused in the second iteration to generate the further first predicted base subsequence, further second predicted base subsequence, and the further third predicted base subsequence, respectively", which does not further limit claim 12 because claim 12 already recites "training the base caller for a second iteration of the N1 iterations comprises: training the base caller using the labelled training data generated during the first iteration of the N1 iterations; using the base caller trained with the labelled training data generated during the first iteration of the NI iterations, generating (i) a further first predicted base subsequence, based on the first sequence signal, (ii) a further second predicted base subsequence, based on the second sequence signal, and (iii) a further third predicted base subsequence, based on the third sequence signal".
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. 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 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 29, and 31: “iteratively further training the base caller by repeating step (i) for N iterations, comprising: further training the base caller for N1 iterations of the N iterations with analyte comprising a first organism base sequence that is culled in a first plurality of base subsequences, and further training the base caller for N2 iterations of the N iterations with analyte comprising a second organism base sequence that is culled in a second plurality of base subsequences” provides an evaluation (culling sequences requires an evaluation of sequence signals, e.g. mapping from para.00229-00230 ) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 7: “substantially and uniquely matching the initial L2 bases of the first predicted base sequence with consecutive L2 bases of the first organism base sequence; identifying the first section of the first organism base sequence, such that the first section (i) includes the consecutive L2 bases as initial bases and (ii) includes L1 number of bases; and mapping the first predicted base subsequence with the identified first section of the first organism base sequence” provides an evaluation (matching, mapping, and identifying sequences requires an evaluation of sequence analysis) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 9: “at least a threshold number of bases of the initial L2 bases of the first predicted base sequence is matched with the consecutive L2 bases of the first organism base sequence” provides an evaluation (applying a threshold requires an evaluation of a comparison) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 10: “the initial L2 bases of the first predicted base sequence is uniquely matched with consecutive L2 bases of the first organism base sequence, such that the initial L2 bases of the first predicted base sequence is substantially matched with only the consecutive L2 bases of the first organism base sequence, and with no other consecutive L2 bases of the first organism base sequence” provides an evaluation (determining matching sequences requires an evaluation of sequence mapping) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 11: “failing to substantially and uniquely match (i) an initial L2 bases of the L1 bases of the third predicted base sequence with consecutive L2 bases of the first organism base sequence” provides an evaluation (determining whether sequences match requires an evaluation of sequence mapping) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 13: “generating a first error between (i) the first predicted base subsequence generated during the first iteration of the N1 iterations and (ii) the first section of the first organism base sequence; and generating a second error between (i) the further first predicted base subsequence generated during the second iteration of the N1 iterations and (ii) the first section of the first organism base sequence” provides an evaluation (generating errors involves determining matches or mismatches which requires an evaluation of sequence mapping) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“the second error is less than the first error, as the base caller is better trained during the second iteration relative to the first iteration” provides a comparison (comparing error values) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 27: “the convergence condition is satisfied when between two consecutive iterations of the N1 iterations, a decrease in an error signal generated is less than a threshold” provides insignificant extra-solution activities (iterating until a convergence is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), 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)) and 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)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claims 29-30 recite performing some aspects of the analysis on “A non-transitory computer readable storage medium impressed with computer program instructions to progressively train a base caller, the instructions, when executed on a processor, implement a method”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-31 recite an abstract idea (Step 2A, Prong 1: YES).
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 exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claims 1, 29, and 31: “initially training a base caller, and generating labelled training data using the initially trained base caller; (i) further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller” and “labelled training data generated during an iteration of the N iterations is used to train the base caller during an immediate subsequent iteration of the N iterations” provides insignificant extra-solution activities (training a model to generate labeled training data is a pre-solution activity involving data manipulation and gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claims 2 and 30: “initially training the base caller with analyte comprising one or more oligo base sequences, and generating labelled training data using the initially trained base caller” provides insignificant extra-solution activities (training a model to generate labeled training data is a pre-solution activity involving data manipulation and gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 4: “receiving (i) a first sequence signal from a first cluster indicative of a first base subsequence of the first plurality of base subsequences of the first organism, (ii) a second sequence signal from a second cluster indicative of a second base subsequence of the first plurality of base subsequences of the first organism, and (iii) a third sequence signal from the third cluster indicative of a third base subsequence of the first plurality of base subsequences of the first organism” provides insignificant extra-solution activities (receiving data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating (i) a first predicted base subsequence, based on the first sequence signal, (ii) a second predicted base subsequence, based on the second sequence signal, and (iii) a third predicted base subsequence, based on the third sequence signal” provides insignificant extra-solution activities (generating data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“mapping (i) the first predicted base subsequence with a first section of the first organism base sequence and (ii) the second predicted base subsequence with a second section of the first organism base sequence, while failing to map the third predicted base subsequence with any section of the first organism base sequence” provides insignificant extra-solution activities (mapping sequences is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
“generating labelled training data comprising (i) the first predicted base subsequence mapped to the first section of the first organism base sequence, where the first section of the first organism base sequence is ground truth for the first predicted base subsequence, and (ii) the second predicted base subsequence mapped to the second section of the first organism base sequence, where the second section of the first organism base sequence is ground truth for the second predicted base subsequence” provides insignificant extra-solution activities (generating labeled data is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 5: “training the base caller using labelled training data generated during initially training the base caller” provides insignificant extra-solution activities (training a model with generated training data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 12: “training the base caller using the labelled training data generated during the first iteration of the N1 iterations; using the base caller trained with the labelled training data generated during the first iteration of the NI iterations, generating (i) a further first predicted base subsequence, based on the first sequence signal, (ii) a further second predicted base subsequence, based on the second sequence signal, and (iii) a further third predicted base subsequence, based on the third sequence signal” and “generating further labelled training data” provides insignificant extra-solution activities (training a model to generate labeled training data is a pre-solution activity involving data manipulation and gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“mapping (i) the further first predicted base subsequence with the first section of the first organism base sequence, (ii) the further second predicted base subsequence with the second section of the first organism base sequence, and (iii) the further third predicted base subsequence with a third section of the first organism base sequence” provides insignificant extra-solution activities (mapping sequences is a post-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 14: “the first, second, and the third sequence signals generated during the first iteration are reused in the second iteration to generate the further first predicted base subsequence, further second predicted base subsequence, and the further third predicted base subsequence, respectively” provides insignificant extra-solution activities (generating data is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 16: “the neural network configuration of the base caller is reused for multiple iterations, until a convergence condition is satisfied” provides insignificant extra-solution activities (iterating until a convergence is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 18: “for a first subset of the N1 iterations, further training the base caller with a first neural network configuration loaded in the base caller; for a second subset of the N1 iterations, further training the base caller with a second neural network configuration loaded in the base caller, the second neural network configuration different from the first neural network configuration” provides insignificant extra-solution activities (training a model using different NN configurations is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 22: “for one or more iterations of the N1 iterations with analyte comprising the first organism base sequence, loading a first neural network configuration in the base caller; and for one or more iterations of the N2 iterations with analyte comprising the second organism base sequence, loading a second neural network configuration in the base caller, the second neural network configuration different from the first neural network configuration” provides insignificant extra-solution activities (loading different NN configurations is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 26: “repeating the further training with first organism base sequence, until a convergence condition is satisfied after the N1 iterations” provides insignificant extra-solution activities (iterating until a convergence is a pre-solution activity involving data manipulation steps) that do not serve to integrate the judicial exceptions into a practical application.
The steps for training, loading, and iterating models, generating labeled training data, receiving data, and mapping sequences are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering, data manipulation, and sample manipulation steps (see MPEP 2106.04(d)(2)). Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-31 are directed to an abstract idea (Step 2A, Prong 2: NO).
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 are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional elements to indicate that the claimed “A non-transitory computer readable storage medium impressed with computer program instructions to progressively train a base caller, the instructions, when executed on a processor, implement a method” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for training, loading, and iterating models, generating labeled training data, receiving data, and mapping sequences are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional.
The 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 claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-31 are not patent eligible.
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.
Claims 1-7 and 12-31 rejected under 35 U.S.C. 103 as being unpatentable over Rothberg et al. (US-20190237160) in view of Hoorfar et al. (Hoorfar et al. "Practical considerations in design of internal amplification controls for diagnostic PCR assays." Journal of clinical microbiology 42.5 (2004): 1863-1868) and Mayr et al. (Mayr et al. "The evolution of boosting algorithms." Methods of information in medicine 53.06 (2014): 419-427).
Regarding claims 1, 29, and 31, Rothberg teaches initially training a base caller, and generating labelled training data using the initially trained base caller (Para.0009 "the method further comprises: accessing training data obtained from detected light emissions by luminescent labels associated with nucleotides during nucleotide incorporation events for a plurality of nucleic acids; and training a deep learning model using the training data and information specifying at least some of the nucleotides in the plurality of nucleic acids to obtain the trained deep learning model").
Rothberg also teaches further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller (Para.0172 "the system may be configured to retrain the deep learning model using one or more outputs generated by the trained deep learning model").
Rothberg also teaches iteratively further training the base caller by repeating step (i) for N iterations (Para.0104 "learning-enabled pulse/base callers may be updated iteratively" and Figure 2 shows a workflow of iteration).
Rothberg also teaches labelled training data generated during an iteration of the N iterations is used to train the base caller during an immediate subsequent iteration of the N iterations (Para.0210 "the potential to pre-train some deep learning models on simulated data, and thereafter train on real data to fine-tune the network weights").
Rothberg does not explicitly teach training a base caller with analytes from a first and second organism base sequence and culling the sequences, nor that the complexity of the neural network configurations loaded in the base caller monotonically increases with the N iterations.
However, Hoorfar combined with Rothberg teaches training a base caller with analytes from a first and second organism base sequence and culling the sequences via mapping (Rothberg Para.0169 "training the deep learning model based on a difference between the nucleotides identified by the output and predetermined nucleotides of the nucleic acids" suggests using predetermined nucleotides for labeled data which may be from multiple organisms used as controls, such as the IAC (Internal amplification controls) used in Hoorfar (page 3 figure 2). Additionally, instant spec para.00238 specifies three culling categories, of which mapping to a reference (or inconclusive mapping) is indicated that is also taught by Rothberg).
However, Mayr teaches the complexity of the neural network configurations loaded in the base caller monotonically increases with the N iterations (page 4 last paragraph "Very late stopping of AdaBoost may favor overfitting, as the complexity of the final solution increases", implying that each new iteration of training increases complexity of a model).
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Rothberg as taught by Hoorfar in order to have validation criteria for sequence-related detection methods, of which base calling is substantially involved (page 1 col 1 paragraph 3 "Lack of reproducible methods often forces testing laboratories to spend substantial resources on adaptation of the published tests. It is thus necessary to have internationally validated, open-formula PCR-based methods available in which the target gene, performance characteristics and validation criteria are known (12) and which follow the ISO criteria for validation of alternative microbiological methods"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with identifying the correct sequence of nucleic acids in a sample.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Rothberg as taught by Mayr in order to not underfit or overfit a model, specifically using a boosting algorithm for biomedical research (page 2 paragraph 3 "The application of boosting algorithms thus offers an attractive option for biomedical researchers: many modern biomedical settings like genome-wide association studies and research using other 'omics' technologies are specifically challenging regarding all three points mentioned above"). One skilled in the art would have a reasonable expectation of success because both methods are using machine learning to model biological data.
Regarding claims 2 and 30, Rothberg in view of Hoorfar and Mayr teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Rothberg also teaches initially training the base caller with analyte comprising one or more oligo base sequences, and generating labelled training data using the initially trained base caller (Para.0170 "the system may be configured to train the deep learning model using supervised learning based on labeled training data. For example, the information specifying one or more nucleic acids may be labels for the data obtained at block 3902").
Regarding claim 3, Rothberg in view of Hoorfar and Mayr teach the methods of Claims 1 and 29 on which this claim depends/these claims depend, respectively. Hoorfar also teaches the N1 iterations are performed prior to the N2 iterations, and wherein the second organism base sequence has a higher number of bases than the first organism base sequence (Figure 2 shows different length subsequences used in the IAC).
Regarding claim 4, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Rothberg and Hoorfar also teach receiving (i) a first sequence signal from a first cluster indicative of a first base subsequence of the first plurality of base subsequences of the first organism, (ii) a second sequence signal from a second cluster indicative of a second base subsequence of the first plurality of base subsequences of the first organism, and (iii) a third sequence signal from the third cluster indicative of a third base subsequence of the first plurality of base subsequences of the first organism (Para.0108 "a plurality of sensors may be sized and arranged to capture a spatial distribution of the emission energy. Output signals from the one or more sensors may then be used to distinguish a label from among a plurality of labels, where the plurality of labels may be used to identify a sample within the specimen" and Hoorfar suggests using subsequences of multiple organisms as internal controls).
Rothberg also teaches generating (i) a first predicted base subsequence, based on the first sequence signal, (ii) a second predicted base subsequence, based on the second sequence signal, and (iii) a third predicted base subsequence, based on the third sequence signal (Para.0156 "method 1100 then proceeds to operation 1108 to associate a reference base; mapping (i) the first predicted base subsequence with a first section of the first organism base sequence and (ii) the second predicted base subsequence with a second section of the first organism base sequence, while failing to map the third predicted base subsequence with any section of the first organism base sequence; and generating labelled training data comprising (i) the first predicted base subsequence mapped to the first section of the first organism base sequence, where the first section of the first organism base sequence is ground truth for the first predicted base subsequence, and (ii) the second predicted base subsequence mapped to the second section of the first organism base sequence, where the second section of the first organism base sequence is ground truth for the second predicted base subsequence (that has been aligned with a stock base call) with each pulse. Then, as shown at operation 1110, a trained machine learning algorithm is used to predict a base, given the determined properties (features). It will be appreciated that the results achieved by the trained machine learning algorithm depend on the correctness of the alignments of the reference bases").
Regarding claim 6, Rothberg in view of Hoorfar and Mayr teach the methods of Claims 1 on which this claim depends/these claims depend, respectively. Rothberg also teaches the first predicted base subsequence has L1 number of bases; and one or more bases of the L1 bases of the first predicted base subsequence does not match with corresponding bases of the first section of the first organism base sequence, due to errors in base calling predictions by the base caller (para.0156, as mismatches of aligning a base called sequence to a reference due to errors in called bases is a common feature of base callers).
Regarding claim 7, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 4 on which this claim depends/these claims depend, respectively. Claim 7 describes matching kmers of L1 (L2 and L3 are kmers of L1) to the reference sequence from which they came, which is also suggested by Hoorfar (Page 3 Fig 2 legend "FIG. 2. Illustration of the composite primer technique, where the same primer set is used to amplify by PCR both the target (Salmonella enterica) and the nonrelevant chimeric DNA (a fish virus) spiked in the PCR mixture"
Rothberg also teaches mapping the first predicted base subsequence with the identified first section of the first organism base sequence (para.0156).
Regarding claim 12, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 4 on which this claim depends/these claims depend, respectively. Claim 12 appears to simply detail an iteration of the training described in claims 1 and 4, and therefore are also obvious over Rothberg and Hoorfar.
Regarding claim 13, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 12 on which this claim depends/these claims depend, respectively. Rothberg also teaches generating a first and second error value comprising either total or percent mismatched bases, then comparing them to determine if the second is less than the first (as interpreted above) (Para.0210 "There are potentially several benefits of a simulated dataset [], including for example: (1) by helping to understand error modes, and components of the raw data produce such components “from the ground-up”; (2) the potential for unlimited data that can be used to create novel deep learning architectures; (3) the ability to train deep learning models with very simple data at the outset (e.g., high SNR, no artifacts), and then titrate in simulated sequencing errors and noise to gauge how a model may or may not be suited toward detecting specific types of signal artifacts" suggests detecting errors and comparing outputs of different model runs, and para.0100 "In still another approach, unsupervised methods may be used, in which only the reference sequence is saved, without attempting to coordinate with specific areas of the trace during processing, and instead using an edit distance-based cost function during model training").
Regarding claim 15, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 12 on which this claim depends/these claims depend, respectively. Rothberg also teaches a neural network configuration of the base caller is the same during the first iteration of the N1 iterations and the second iteration of the N1 iterations (Para.0100 "The labels may, in one embodiment, be discretized to include a one-to-one mapping of individual bases to temporal pulse-events derived using a previous iteration of pulse/base-calling models (learning-enabled or otherwise)").
Regarding claims 16 and 26, Rothberg in view of Hoorfar and Mayr teach the methods of Claims 15 and 1 on which this claim depends/these claims depend, respectively. Rothberg also teaches the neural network configuration of the base caller is reused for multiple iterations, until a convergence condition is satisfied (Para.0142 "This process may be repeated, using the results from the most recent iteration of the classifier as the training for the next iteration, until it converges").
Regarding claims 17-25, Rothberg in view of Hoorfar and Mayr teach the methods of Claims 12 and 1 on which this claim depends/these claims depend, respectively. Rothberg also teaches for a second subset of the N1 iterations, further training the base caller with a second neural network configuration loaded in the base caller, the second neural network configuration different from the first neural network configuration (Para.0053 "FIGS. 20A-20C are plots that show running base count probabilities produced by inputting the raw trace data of FIG. 19 to a convolutional neural network applied over different-sized windows" suggests running models using different-sized windows which comprises a differing configuration).
Rothberg also suggests the second neural network configuration has a greater number of layers than the first neural network configuration; the second neural network configuration has a greater number of weights than the first neural network configuration; the second neural network configuration has a greater number of parameters than the first neural network configuration; and for one or more iterations of the N1 iterations with analyte comprising the first organism base sequence, loading a first neural network configuration in the base caller as it would be obvious to try these various model configurations because this is part of routine optimization, using the models of Rothberg.
Regarding claims 27 and 28, Rothberg in view of Hoorfar and Mayr teach the methods of Claim 26 on which this claim depends/these claims depend, respectively. Mayr also teaches the convergence condition is satisfied when between two consecutive iterations of the N1 iterations, a decrease in an error signal generated is less than a threshold (page 11 section 3.3 "Although there are different influential factors for the performance of boosting algorithms, the stopping iteration [] is considered to be the main tuning parameter [48]. Stopping the algorithm before its convergence (early stopping) prevents overfitting (Section 2.3) and typically improves prediction accuracy").
Regarding claim 29, Rothberg in view of Hoorfar and Mayr teach the overlapping methods of Claim 1 which are shared by this claim. Rothberg also teaches a non-transitory computer readable storage medium impressed with computer program instructions to progressively train a base caller, the instructions, when executed on a processor, implement a method (Para.0011 "According to another aspect, a system for identifying nucleotides of a nucleic acid. The system comprises: at least computer hardware processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform").
Claims 8-11 rejected under 35 U.S.C. 103 as being unpatentable over Rothberg et al. (US-20190237160) in view of Hoorfar et al. (Hoorfar et al. "Practical considerations in design of internal amplification controls for diagnostic PCR assays." Journal of clinical microbiology 42.5 (2004): 1863-1868) and Mayr et al. (Mayr et al. "The evolution of boosting algorithms." Methods of information in medicine 53.06 (2014): 419-427) as applied to claims 1-7 and 12-31 above, and further in view of van Rooyen et al. (US-20170124254).
Rothberg et al. in view of Hoorfar et al. and Mayr et al. are applied to claims 1-7 and 12-31.
Regarding claims 8-10, Rothberg in view of Hoorfar and Mayr teach the method of Claim 7 on which this claim depends/these claims depend.
Rothberg, Hoorfar, nor Mayr explicitly teach the substantially and uniquely matching the initial L2 bases of the first predicted base subsequence, refraining from aiming to match the subsequent L3 bases of the first predicted base subsequence with any base of the first organism base sequence; the initial L2 bases of the first predicted base subsequence is substantially matched with the consecutive L2 bases of the first organism base sequence, such that at least a threshold number of bases of the initial L2 bases of the first predicted base subsequence is matched with the consecutive L2 bases of the first organism base sequence; nor the initial L2 bases of the first predicted base subsequence is uniquely matched with consecutive L2 bases of the first organism base sequence, such that the initial L2 bases of the first predicted base subsequence is substantially matched with only the consecutive L2 bases of the first organism base sequence, and with no other consecutive L2 bases of the first organism base sequence.
However, van Rooyen teaches the concepts of applying a threshold of matching bases for uniquely mapping segments of sequences described in claims 8-10, and suggests a tree-like data structure for finding unique sequences termed a "seed" (para.0182 "For instance, a tree-like data structure serving as an index of the reference genome may be queried by tracing a path through the tree, corresponding to a subsequence of a read being mapped, that is built up by adding nucleotides to the subsequence, using the added nucleotides to select next links to traverse in the tree, and going as deep as necessary until a unique sequence has been generated. This unique sequence may also be termed a seed, and may represent a branch and/or root of the sequence tree data structure").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Rothberg, Hoorfar, and Mayr as taught by van Rooyen in order to generate seeds for mapping the subsequences (para.0113 "a base calling module, configured for determining one or more bases of one or more reads of a sequenced nucleic acid; a mapping module, configured for generating one or more seeds from the one or more reads of sequenced data and for performing a mapping function on the one or more seeds and/or reads; an alignment module, configured for performing an alignment function on the one or more mapped reads"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with base calling data and mapping the reads.
Regarding claim 11, Rothberg in view of Hoorfar and Mayr teach the method of Claim 4 on which this claim depends/these claims depend. Hoorfar or van Rooyen teach the third predicted base subsequence has L1 number of bases, and wherein failing to map the third predicted base subsequence with any of the base subsequence of the first plurality of base subsequences comprises: failing to substantially and uniquely match (i) an initial L2 bases of the L1 bases of the third predicted base subsequence with consecutive L2 bases of the first organism base sequence (in general mapping procedures, the described limitations are inherent in chimeric sequence alignment as seen in Hoorfar, or slice junction detection of van Rooyen).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-31 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US-17830287. Although the claims at issue are not identical, they are not patentably distinct from each other because both involve progressively training a base caller by iteratively training with an analyte of base sequences and generating labelled training data, iteratively further training the base caller using the generated labelled training data. Additionally, teaching that the complexity of the neural network configuration is increased per training iteration, and that the training data generated during one iteration is immediately used to train the base caller in the next iteration. These are limitations shared by claim 1 of both applications from which all other claims depend.
Claims 1-31 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US-12354008 in view of Mayr et al. (Mayr et al. "The evolution of boosting algorithms." Methods of information in medicine 53.06 (2014): 419-427). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve an artificial intelligence method of base calling, training base caller on annotated data (cluster images), training a second base caller using the output of the first, and the training data generated during one iteration is immediately used to train the base caller in the next iteration.
While US-12354008 does not explicitly teach that the complexity of neural network configurations loaded in the base caller monotonically increases with each iteration, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Mayr as described above for claim 1 of the instant application, in order to not underfit or overfit a model, specifically using a boosting algorithm for biomedical research (page 2 paragraph 3 "The application of boosting algorithms thus offers an attractive option for biomedical researchers: many modern biomedical settings like genome-wide association studies and research using other 'omics' technologies are specifically challenging regarding all three points mentioned above"). One skilled in the art would have a reasonable expectation of success because both methods are using machine learning to model biological data.
Conclusion
No claims are allowed.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686