DETAILED ACTION
Applicant's response, filed 15 December 2025, has been fully considered. The following
rejections and/or objections are either reiterated or newly applied. They constitute the complete set
presently being applied to the instant application.
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 .
Priority
The instant application does not claim the benefit of priority to any earlier filed applications. As such, the effective filing date of claims 1-20 is 02/16/2022.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Objections
Response to Amendment
In view of applicant’s amendments to the claims, previous objections to the claims, specifically claim 6, are withdrawn.
Drawings
Response to Amendment
In view of applicant’s amendments to the drawings previous objections to the drawings are withdrawn.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below. Previous rejections of claims 15-20 as being non-statutory are withdrawn.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method for segmenting and labeling an amino acid sequence during single molecule sequencing. The judicial exception is not integrated into a practical application because while claims 1-20 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [See MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1-7), an apparatus (Claims 8-14), and while claims 15-20 are non-statutory the analysis under the Alice/Mayo test will be continued in the interest of compact prosecution.
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [See MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Segmenting the training signal, determining signal characteristics, and generating an HMM signal to segment and label a signal are processes of grouping, classifying, and calculating that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes. Additionally generating an HMM signal to segment and label a signal is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 2: Determining probabilities that signal characteristics correspond to events is a process of calculating values which can be done with pen and paper or in the human mind and is therefore an abstract idea, specifically a mental process. Additionally, determining probabilities that signal characteristics correspond to events are verbal articulations of a mathematical processes and are thus abstract ideas, specifically mathematical concepts.
Claim 3: Assigning probabilities to each HMM node and edge is a process of classifying that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 4: Applying a Viterbi algorithm to the HMM and segmenting the signal is the application of a way of thinking which is an abstract idea, specifically a mental process. Additionally, applying a Viterbi algorithm to the HMM and segmenting the signal are verbal articulations of a mathematical processes and are thus abstract ideas, specifically mathematical concepts.
Claim 5: Estimating a likelihood that the signal characteristics correspond to events based on probabilities is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 8: Segmenting the training signal, determining signal characteristics, determine a first set of event labels, and generating an HMM signal to segment and label a signal are processes of grouping, classifying, training, and calculating that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes. Additionally generating an HMM signal to segment and label a signal is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 9: Determining that a single characteristic corresponds to an individual event is a process of classifying that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 10: Assigning probabilities to each HMM node and edge is a process of classifying that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 11: Applying a Viterbi algorithm to the HMM and segmenting the signal is the application of a way of thinking which is an abstract idea, specifically a mental process.
Claim 12: Estimating a likelihood that the signal characteristics correspond to events based on probabilities is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 13: The training signal including signal noise selected from the specified group is merely limiting the information/data we are using which is itself an abstract idea, specifically a mental process.
Claim 14: The training signal including known amino acid sequences and unknown amino acid sequences is merely limiting the information/data we are using which is itself an abstract idea, specifically a mental process.
Claim 15: Segmenting the training signal, determining signal characteristics, determining a first set of event labels, generating an HMM signal to segment and label a signal are processes of grouping, classifying, and calculating that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes. Additionally generating an HMM signal to segment and label a signal is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 16: Determining that a single characteristic corresponds to an event is a process of classifying that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 17: Assigning probabilities to each HMM node and edge is a process of classifying that can be done with a pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 18: Applying a Viterbi algorithm to the HMM and segmenting the signal is the application of a way of thinking which is an abstract idea, specifically a mental process.
Claim 19: Estimating a likelihood that the signal characteristics correspond to events based on probabilities is a verbal articulation of a mathematical process and is therefore an abstract idea, specifically a mathematical concept.
Claim 20: The training signal including known amino acid sequences and unknown amino acid sequences is merely limiting the information/data we are using which is itself an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [See MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: Receiving a training signal, training an HMM, inputting a second signal, and providing an output signal are insignificant extra solution activities, specifically mere data gathering and outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 6: The training signal including signal noise selected from the specified group are insignificant extra solution activities, specifically mere data gathering and outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 7: The training signal including known amino acid sequences and unknown amino acid sequences are insignificant extra solution activities, specifically mere data gathering and outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 8: A receiver circuit, and a processing circuit are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Receiving a training signal, training an HMM, inputting a second signal, and providing an output signal are insignificant extra solution activities, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 15: A memory device, instructions, and a processor are generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Receiving a training signal, training an HMM, inputting a second signal, and providing an output signal are insignificant extra solution activities, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [See MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of receiver circuit, a processing circuit, a memory device, instructions, and a processor are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See § MPEP 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of receiving a training signal (Conventional: Thompson et al. 2011 – Figure 2), training an HMM (Conventional: Rabiner et al. Page 261, column 2, Paragraphs 2-3), inputting a second signal (Conventional: Thompson et al. 2011 – Figure 2), the training signal including signal noise selected from the specified group (the type of data doesn’t change this as a data gathering step or change it from a conventional computer function), the training signal including known amino acid sequences and unknown amino acid sequences (the type of data doesn’t change this as a data gathering step or change it from a conventional computer function), and providing an output signal, are insignificant extra solutional activities, specifically mere data gathering, that are recognized as well understood, routine and conventional by the courts (See Analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546, and Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
Applicant asserts on page 10 of the Remarks filed 12/15/2025 that the claims as amended do not recite an abstract idea, but are rather a specific technological solution to a technical problem. Furthermore, applicant asserts on page 10 of the Remarks filed 12/15/2025 that the claims as amended recite a practical application, specifically that the method represents a significant improvement in accuracy and robustness, which is the technological solution. However, examiner reminds applicant that according to MPEP 2106.05(a) - It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements, meaning the asserted improvement to accuracy and robustness is merely an improvement to the judicial exception alone, and therefore cannot be the basis for a practical application or solution to a technical problem.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
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.
Claims 1-5, 7-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sgouralis et al. (Biophysical Journal (2017) 2021-2029; previously cited), Schreiber et al. (Bioinformatics (2015) 1897-1903; newly cited), and Van Rooyen et al. (EP 3608913 A1; previously cited).
Claim 1 is directed to a method of segmentation and labeling in single molecule sequencing using a trained Hidden Markov Model to automatically segment and label signals.
Claim 8 is directed to an apparatus for segmentation and labeling in single molecule sequencing using a trained Hidden Markov Model to automatically segment and label signals.
Claim 15 is directed to a CRM that stores instructions that performs segmentation and labeling in single molecule sequencing using a trained Hidden Markov Model to automatically segment and label signals.
Sgouralis et al. teaches in figure 7 “We may use the iHMM to estimate portions of the complete state space such as those contained in different segments… Estimated noiseless traces for two cases: 1) using a limited segment of the full trace”, on page 2022, column 2, paragraph 2 “Thus, we label with n the states of the system as it evolves through time”, on page 2022, column 1, paragraph 3 “all transitions out of state sigma are fully described by a probability vector”, on page 2023, column 1, paragraph 2 “the goals of the HMM are to estimate 1) the underlying state sequence, which is unobserved during the measurements; and 2) the model parameters, which include…and the state transition probabilities, associated with the states…”, and on page 2022, column 2, paragraph 1 “It is often practical to model the emission distributions by a general family and use state-specific parameters, psi , to distinguish its members”, furthermore it is inherent to any hidden Markov model that it be necessarily trained to correctly estimate parameters for event prediction, reading on segmenting the training signal into a set of events, determining signal characteristics of the training signal for each event of the first set of events, and training a Hidden Markov Model (HMM) using the set of events and the signal characteristics. Sgouralis et al. teaches in figure 4 the outputting of distributions of various states of a biomolecule that are a priori unknown, reading on providing the output signal having a sequence corresponding to a set of events and corresponding labels generated by the HMM.
Sgouralis et al. does not teach generating the HMM signal to the second signal to automatically segment and label the second signal to generate an output signal.
Schreiber et al. teaches in the abstract “We propose an automated method for aligning nanopore data to a reference through the use of hidden Markov models”, on page 1898, column 1, paragraph 6 “To model nanopore data, we first perform event detection on the data, detecting all regions of ionic current which are longer than 500 ms, below 90 pA and above 0 pA. We then segment each event by recursively splitting at the ionic current sample, which best splits a region into two Gaussian distributions until a threshold in probabilistic gain is reached, representing each time interval as a segment with a mean current, a standard deviation and a duration…Each match state in our HMM uses a Gaussian distribution to assign emission probabilities to the segments, with parameters l and r having initial values derived from a hand-analyzed reference sequence. Insert states, which correspond to unpredicted currents, have a uniform distribution from 0A to 90 pA, which are the limits for event detection. Initial transition probabilities inside each module were estimated by hand from a small number of events”, reading on determining a first set of event labels for the first set of events, each event label identifying a monomer of the sequence of monomers corresponding to an event of the first set of events and generating, using the trained HMM responsive to the inputting, an output signal, wherein the HMM segments the second signal into a second set of events and labels the second signal to yield a labeled second signal by assigning a predicted monomer identity to each event of the second set of events.
Van Rooyen et al. teaches in claim 2 “a database connected with the cloud computing cluster for receiving result data”, reading on receiving a training signal generated by molecular detection. Van Rooyen et al. also teaches “in certain instances, a training mode may be implemented employing a training sequence so as to further refine transition probability accuracy”, and “In various instances, the integrated circuit and/or chip may be a component within a sequencer, such as an automated sequencer or other genetic analysis apparatus, such as a mapper and/or aligner, and/or in other embodiments, the integrated circuit and/or expansion card may be accessible via the internet, e.g., cloud”, reading on applying the HMM to the second signal to automatically segment and label the second signal to generate an output signal.
It would have been obvious at the time of the effective filing date of the invention to a person skilled in the art to modify the teachings of Van Rooyan et al. for segmenting and labeling sequence data via HMMs with the teachings of Sgouralis et al. for using modified HMMs in single molecule data analysis as both are using the similar models as well as similar data to classify segmented signal information via trained HMMs. Furthermore, it would have been obvious to combine the teachings of the previous two with the teachings of Schreiber et al. for determining event labels of monomers of a sequence and assigning predicted monomer identities as the latter teaches in the abstract “We validated our automated methodology on a subset of that data by automatically calculating an error rate for the distinction between the three cytosine variants and show that the automated methodology produces a 2–3% error rate, lower than the 10% error rate from previous manual segmentation and alignment”. One would have had a reasonable expectation of success given that all three are in the same field, using the same data and the same methods, merely slight variations of each. Therefore, it would have been obvious at the time of invention to modify the teachings of each and to be successful.
Claim 2 is directed to the method of claim 1 but further specifies determining probabilities that signal characteristics correspond to sets of events.
Claim 9 is directed to the apparatus of claim 8 but further specifies determining probabilities that signal characteristics correspond to an individual event.
Claim 16 is directed to the CRM of claim 15 but further specifies determining probabilities that an individual signal characteristic corresponds to an event.
Sgouralis et al. teaches on page 2023, column 1, paragraph 2 “the goals of the HMM are to estimate 1) the underlying state sequence, which is unobserved during the measurements; and 2) the model parameters, which include…and the state transition probabilities, associated with the states…”, reading on further comprising determining the signal characteristics for the set of events includes determining probabilities that the signal characteristics correspond to the set of events.
Claim 3 is directed to the method of claim 1 but further specifies assigning probabilities to each HMM node and edge to predict correspondence to an event or transition to another node.
Claim 10 is directed to the apparatus of claim 8 but further specifies assigning probabilities to each HMM node and edge to predict correspondence to an event or transition to another node.
Claim 17 is directed to the CRM of claim 16 and thus claim 15, but further specifies assigning probabilities to each HMM node and edge to predict correspondence to events.
Van Rooyen et al. teaches “In such an instance, the initial path score through the matrix will be the sum of all edge likelihoods in the path. For example, the edge likelihood may be a function of likelihoods of all outgoing edges from a given vertex” and “Some primary analysis pipelines often include: Signal processing to amplify, filter, separate, and measure sensor output; Data reduction, such as by quantization, decimation, averaging, transformation, etc.; Image processing or numerical processing to identify and enhance meaningful signals, and associate them with specific reads and nucleotides (e.g. image offset calculation, cluster identification); Algorithmic processing and heuristics to compensate for sequencing technology artifacts (e.g. phasing estimates, cross-talk matrices); Bayesian probability calculations; Hidden Markov models”, reading on further comprising generating the HMM based on the set of events and the signal characteristics includes assigning probabilities to each HMM node and edge that model the node output to predict when the signal characteristics correspond to an event and transitioning to another HMM node when a node does not correspond.
Claim 4 is directed to the method of claim 1 but further specifies applying a Viterbi algorithm to the HMM and segmenting a second signal into a second set of events.
Claim 11 is directed to the apparatus of claim 8 but further specifies applying a Viterbi algorithm to the HMM and segmenting a second signal into a second set of events.
Claim 18 is directed to the CRM of claim 17 and thus claim 15, but further specifies applying a Viterbi algorithm to the HMM and segmenting a second signal into a second set of events.
Van Rooyen et al. teaches “Additionally, such as with respect to variant calling, both Hidden Markov model (HMM) and/or dynamic programming (DP) algorithms, including Viterbi and forward algorithms, may be implemented…”, reading on further comprising automatically segmenting the second signal includes applying a Viterbi algorithm to the HMM and responsively segmenting the second signal into a second set of events.
Claim 5 is directed to the method of claim 4 and thus claim 1, but further specifies estimating a likelihood that the signal characteristics correspond to a set of events based on the probabilities of the HMM.
Claim 12 is directed to the apparatus of claim 8 but further specifies estimating a likelihood that the signal characteristics correspond to a set of events based on the probabilities of the HMM.
Claim 19 is directed to the CRM of claim 18 and thus claim 15, but further specifies estimating a likelihood that the signal characteristics correspond to a set of events based on the probabilities of the HMM.
Van Rooyen et al. teaches “In such an instance, the initial path score through the matrix will be the sum of all edge likelihoods in the path. For example, the edge likelihood may be a function of likelihoods of all outgoing edges from a given vertex” and “Some primary analysis pipelines often include: Signal processing to amplify, filter, separate, and measure sensor output; Data reduction, such as by quantization, decimation, averaging, transformation, etc.; Image processing or numerical processing to identify and enhance meaningful signals, and associate them with specific reads and nucleotides (e.g. image offset calculation, cluster identification); Algorithmic processing and heuristics to compensate for sequencing technology artifacts (e.g. phasing estimates, cross-talk matrices); Bayesian probability calculations; Hidden Markov models”, reading on further comprising labeling the second set of events based on the signal characteristics by estimating a likelihood that the signal characteristics correspond to the second set of events based on probabilities indicated by the HMM.
Claim 7 is directed to the method of claim 1 but further specifies training the signal includes a known amino acid sequence and a second unknown sequence.
Claim 14 is directed to the apparatus of claim 8 but further specifies training the signal includes a known amino acid sequence and a second unknown sequence.
Claim 20 is directed to the CRM of claim 15 but further specifies the training signal includes a known amino acid sequence and a second unknown sequence.
Van Rooyen et al. teaches “In various embodiments, the system may include one or more of an electronic data source that provides digital signals representing a plurality of reads of genomic data, such as where each of the plurality of reads of genomic data include a sequence of nucleotides”, “a training mode may be implemented employing a training sequence so as to further refine transition probability accuracy for a given sequencer run”, and “…for each mapped position, accesses the (internal or external) memory to retrieve a segment of the reference sequence/genome corresponding to the mapped position…”, reading on further comprising the training signal includes a known amino acid sequence and the second signal includes an unknown amino acid sequence.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sgouralis et al. (Biophysical Journal (2017) 2021-2029), Schreiber et al. (Bioinformatics (2015) 1897-1903), and Van Rooyen et al. (EP 3608913 A1) as applied to claims 1-5, 7-12, and 14-20 above, and further in view of Jung et al. (Journal of Physical Chemistry B. (2009) 13886-13890) and Wang et al. (IEEE Transactions on Neural Networks (1999) 1511-1517).
Claim 6 is directed to the method of claim 1 but further specifies including signal from the group of correlated noise, spiky noise, and non-Gaussian noise.
Claim 13 is directed to the apparatus of claim 8 but further specifies including signal from the group of correlated noise, spiky noise, and non-Gaussian noise.
Sgouralis et al., Schreiber et al., and Van Rooyen et al. teach the method and apparatus of claims 1 and 8 as described above.
Sgouralis et al., Schreiber et al., and Van Rooyen et al. do not teach the inclusion of a signal from the group of correlated noise, spiky noise, and non-Gaussian noise.
Jung et al. teaches on page 2, paragraph 1 “In this paper, data sets of varying length are generated by pseudo randomly generated transition and emission matrices exhibiting experimentally relevant, Poisson-distributed noise”, reading on further comprising the signal characteristics include signal noise selected from the group of correlated noise, non-Gaussian noise, and spiky noise.
It would have been obvious at the time of the effective filing date of the invention to a person skilled in the art to modify the teachings of Sgouralis et al., Schreiber et al., and Van Rooyan et al. for the method and apparatus of claims 1 and 8, with the teachings of Jung et al. for including Poisson-distributed noise as the inclusion of noise within the training is a well-understood and routine part of training models (Wang et al. 1999 – Page 1511, column 1, paragraph 1 “well known that the two major problems associated with backpropagation learning”, and the abstract “A new global optimization strategy for training adaptive systems such as neural networks”). One would have had a reasonable expectation of success given that Sgouralis et al. and Van Rooyan et al. are both applying HMMs to single molecule data analysis and Jung et al. is attempting to improve the goodness-of-fit of the models being used in such analysis. Therefore, it would have been obvious at the time of invention to modify the teachings of each and to be successful.
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
Applicant asserts on page 11 of the Remarks filed 12/15/2025 that the combination of cited references does not teach a supervised learning approach which trains on a signal with labeled events and characteristics. Additionally, applicant asserts on page 13 of the Remarks filed 12/15/2025 that the combination of cited references does not teach a model that receives a signal and outputs an event-segmented, labeled signal. Examiner agrees previously cited prior art does not read on claims as currently amended and has provided newly cited prior art, Schreiber et al. to rectify any deficiencies.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEENAN NEIL ANDERSON-FEARS whose telephone number is (571)272-0108. The examiner can normally be reached M-Th, alternate F, 8-5.
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/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685