Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-22 are pending.
Claims 1-22 are rejected.
Specification
The use of the term Illumina Inc., Illumina, Pacific Biosciences, HelicosBio, Genia, Roche, Roche 454, Ion Torrent, Oxford Nanopore, ONT, PacBio, and 10X Genomics, which are trade names or a marks used in commerce, has been noted in this application. The terms should be accompanied by the generic terminology; furthermore the terms should be capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the terms.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Drawings
The drawings are objected to because they include sequences with no SEQ ID nos. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Nucleotide and/or Amino Acid Sequence Disclosures
REQUIREMENTS FOR PATENT APPLICATIONS CONTAINING NUCLEOTIDE AND/OR AMINO ACID SEQUENCE DISCLOSURES
Items 1) and 2) provide general guidance related to requirements for sequence disclosures.
37 CFR 1.821(c) requires that patent applications which contain disclosures of nucleotide and/or amino acid sequences that fall within the definitions of 37 CFR 1.821(a) must contain a "Sequence Listing," as a separate part of the disclosure, which presents the nucleotide and/or amino acid sequences and associated information using the symbols and format in accordance with the requirements of 37 CFR 1.821 - 1.825. This "Sequence Listing" part of the disclosure may be submitted:
In accordance with 37 CFR 1.821(c)(1) via the USPTO patent electronic filing system (see Section I.1 of the Legal Framework for Patent Electronic System (https://www.uspto.gov/PatentLegalFramework), hereinafter "Legal Framework") as an ASCII text file, together with an incorporation-by-reference of the material in the ASCII text file in a separate paragraph of the specification as required by 37 CFR 1.823(b)(1) identifying:
the name of the ASCII text file;
ii) the date of creation; and
iii) the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(1) on read-only optical disc(s) as permitted by 37 CFR 1.52(e)(1)(ii), labeled according to 37 CFR 1.52(e)(5), with an incorporation-by-reference of the material in the ASCII text file according to 37 CFR 1.52(e)(8) and 37 CFR 1.823(b)(1) in a separate paragraph of the specification identifying:
the name of the ASCII text file;
the date of creation; and
the size of the ASCII text file in bytes;
In accordance with 37 CFR 1.821(c)(2) via the USPTO patent electronic filing system as a PDF file (not recommended); or
In accordance with 37 CFR 1.821(c)(3) on physical sheets of paper (not recommended).
When a “Sequence Listing” has been submitted as a PDF file as in 1(c) above (37 CFR 1.821(c)(2)) or on physical sheets of paper as in 1(d) above (37 CFR 1.821(c)(3)), 37 CFR 1.821(e)(1) requires a computer readable form (CRF) of the “Sequence Listing” in accordance with the requirements of 37 CFR 1.824.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed via the USPTO patent electronic filing system as a PDF, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the PDF copy and the CRF copy (the ASCII text file copy) are identical.
If the "Sequence Listing" required by 37 CFR 1.821(c) is filed on paper or read-only optical disc, then 37 CFR 1.821(e)(1)(ii) or 1.821(e)(2)(ii) requires submission of a statement that the "Sequence Listing" content of the paper or read-only optical disc copy and the CRF are identical.
Specific deficiencies and the required response to this Office Action are as follows:
Specific deficiency - This application fails to comply with the requirements of 37 CFR 1.821 - 1.825 because it does not contain a "Sequence Listing" as a separate part of the disclosure or a CRF of the “Sequence Listing.”.
Required response - Applicant must provide:
A "Sequence Listing" part of the disclosure; together with
An amendment specifically directing its entry into the application in accordance with 37 CFR 1.825(a)(2);
A statement that the "Sequence Listing" includes no new matter as required by 37 CFR 1.821(a)(4); and
A statement that indicates support for the amendment in the application, as filed, as required by 37 CFR 1.825(a)(3).
If the "Sequence Listing" part of the disclosure is submitted according to item 1) a) or b) above, Applicant must also provide:
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required incorporation-by-reference paragraph, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
If the "Sequence Listing" part of the disclosure is submitted according to item 1) c) or d) above, applicant must also provide:
A CRF in accordance with 37 CFR 1.821(e)(1) or 1.821(e)(2) as required by 1.825(a)(5); and
A statement according to item 2) a) or b) above.
Specific deficiency – Nucleotide and/or amino acid sequences appearing in the drawings are not identified by sequence identifiers in accordance with 37 CFR 1.821(d). Sequence identifiers for nucleotide and/or amino acid sequences must appear either in the drawings or in the Brief Description of the Drawings.
Required response – Applicant must provide:
Replacement and annotated drawings in accordance with 37 CFR 1.121(d) inserting the required sequence identifiers;
AND/OR
A substitute specification in compliance with 37 CFR 1.52, 1.121(b)(3) and 1.125 inserting the required sequence identifiers into the Brief Description of the Drawings, consisting of:
A copy of the previously-submitted specification, with deletions shown with strikethrough or brackets and insertions shown with underlining (marked-up version);
A copy of the amended specification without markings (clean version); and
A statement that the substitute specification contains no new matter.
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 1-22 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, system, and computer readable medium for determining a sequence of a polymer from a subject of a species. The judicial exception is not integrated into a practical application because while claims 1-22 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 1-20), a CRM (Claim 21), and a system (Claim 22).
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: Determining a plurality of localizations, segmenting the plurality of localizations, and assembling a target polymer from the localizations are processes of identifying, dividing, and combining sequence information, that can be done via pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 2: Aligning one or more image files, and determining a plurality of fluorophores are processes of comparing/contrasting and identifying that can be done within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 3: The image processing model comprising a maximum-likelihood model is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 4: The localization comprising a superresolved localization is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 5: Determining one or more subsets of localizations is a process of identification which can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process. Fitting a respective curve to each subset of localizations is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 6: Repeating the segmentation step of step (c) is a process of repeatedly dividing information up which can be done via pen and paper or within the human mind is therefore an abstract idea, specifically a mental process.
Claim 7: Determining a corresponding probability of each target polymer sequence is a process of identifying and calculating which can be done via pen and paper or within the human mind is therefore an abstract idea, specifically a mental process.
Claim 8: Determining a combined target polymer sequence is a process of comparing/contrasting information, and identification that can be done via pen and paper or within the human mind is therefore an abstract idea, specifically a mental process.
Claim 9: Applying an optimization model to obtain the respective target polymer sequence is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 10: The optimization model being defined as the one provided is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 11: The prior probability being defined as the one provided is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 12: The prior probability being based on the equations provided is a verbal articulation of a mathematical process and therefore, and abstract idea, specifically a mathematical concept.
Claim 13: The optimization model including one or more additional parameters selected from the set of information provided is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 14: The non-uniform probability distribution being based in part on a reference genome is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 15: The species being human is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 16: The image files comprising at least one of the specified criteria provided is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 17: The target polymer comprising a nucleic acid is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 18: Each target polymer position identity corresponding to a nucleic acid is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 19: Each fitted curve comprising a parametric curve is merely further limiting, and directed to, the data itself which is an abstract idea, specifically a mental process.
Claim 20: Determining an uncertainty value for each respective spatial location is a process of calculating which can be done via pen and paper or within the human mind is therefore an abstract idea, specifically a mental process.
Claim 21: Determining a plurality of localizations, segmenting the plurality of localizations, and assembling a target polymer from the localizations are processes of identifying, dividing, and combining sequence information, that can be done via pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 22: Determining a plurality of localizations, segmenting the plurality of localizations, and assembling a target polymer from the localizations are processes of identifying, dividing, and combining sequence information, that can be done via pen and paper or in the human mind and are therefore abstract ideas, specifically mental processes.
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: A computer system, processor, memory, program, and instructions 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)]. Obtaining a dataset is an insignificant extra solution activity, specifically mere data gathering (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering), 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 2: Outputting the combined plurality of localizations is an insignificant extra solution activity, specifically necessary data outputting (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968, and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [See MPEP § 2106.05(g)].
Claim 3: The image processing model comprising a neural network is a generic and nonspecific element of a computer that does not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Claim 21: A non-transitory computer-readable storage medium, program code, instructions, and 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)].
Claim 22: A computer system, computer, processor, memory, program, and instructions 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)].
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 a computer system, processor, memory, program, non-transitory computer-readable storage medium, program code, and instructions 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 (See 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 whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of obtaining a dataset, and outputting the combined plurality of localizations are insignificant extra solution activities, specifically necessary data gathering/outputting that is well-known, routine, and conventional within the art (See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968, and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)) [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 1-22, when the limitations are considered individually and as a whole, are rejected
under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 15, 17-18 and 21-22 are rejected under 35 U.S.C. 102(a)(I) and (a)(II) as being anticipated by Wohrstein et al. (US 20190024165 A1).
Claim 1 is directed to a method of determining a sequence of at least a portion of a target polymer.
Claim 21 is directed to a CRM for determining a sequence of at least a portion of a target polymer.
Claim 22 is directed to a system for determining a sequence of at least a portion of a target polymer.
Wohrstein et al. teaches in the abstract “The present invention relates to methods of determining the sequence of nucleotides in target nucleic acid molecules”, in paragraph [0119] “In one embodiment, after imaging, the location and lengths of the plurality of nucleic acids and the location and identity of nucleic acid detection entities bound thereon are extracted from the images and stored in a computer memory”, in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”, in paragraph [0014]-[0016] “wherein copies of said target nucleic acid molecule are immobilized on a solid substrate… wherein each nucleic acid detection entity is at least in part single stranded and comprises: (i) a specific probe nucleotide sequence, (ii) a localization nucleotide sequence…”, in paragraph [0021] “wherein, preferably, each identification tag is or can be detectably labelled, e.g. via a fluorophore”, in paragraph [0120] “the location and lengths of the pluralities of nucleic acids and the location and identity of nucleic acid detection entities, are used to assemble nucleic acid sequence or report on the identity or structure of nucleic acids in a sample”, and in paragraph [0262] “The microscope is controlled by Nikon Nis-Elements software using a high performance computer comprising for example, a Dell or Lenovo computer with a Xeon processor, 32Gb RAM and a RAID array or solid state memory”, reading on a method of determining a sequence of at least a portion of a target polymer from a subject of a species, the method comprising: at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions for: a) obtaining, in electronic form, a dataset that comprises one or more image files; b) determining a combined plurality of localizations based at least in part on each respective plurality of fluorophore localizations for each image file in the one or more image files; c) segmenting the plurality of localizations into one or more target polymer strands, wherein each target polymer strand corresponds to a respective subset of localizations from the plurality of localizations; and d) assembling, using each subset of localizations for each respective target polymer strand, a respective target polymer, thereby providing a set of target polymer sequences.
Claim 15 is directed to the method of claim 1 but further specifies that the species be human.
Wohrstein et al. teaches in paragraph [0003] “DNA is a polymer which is chemically composed of nucleotide sub-units, which in the human genome”, reading on wherein the species is human.
Claim 17 is directed to the method of claim 1 but further specifies that the target polymer be a nucleic acid.
Wohrstein et al. teaches in the abstract “The present invention relates to methods of determining the sequence of nucleotides in target nucleic acid molecules”, reading on wherein the target polymer comprises a nucleic acid.
Claim 18 is directed to the method of claim 17 and thus claim 1 but further specifies that the polymer position identity corresponds to a nucleic acid base.
Wohrstein et al. teaches in the abstract “The present invention relates to methods of determining the sequence of nucleotides in target nucleic acid molecules”, it would be inherent that any sequence determination of nucleic acid molecules would identify a nucleic acid base and its position within the sequence, thereby reading on wherein each target polymer position identity corresponds to a nucleic acid base.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-13, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wohrstein et al. (US 20190024165 A1) as applied to claims 1,15, 17-18 and 21-22 above, and further in view of Bannore et al. (Springer Berlin Heidelberg (2009) 1-121) and Wang et al. (Nature methods (2018) 103-110).
Claim 2 is directed to the method of claim 1 but further specifies that the image processing model aligns the images, determines a plurality of fluorophores, and outputs the combined plurality via compilation.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Wang et al. teaches on page 112, column 1, paragraph 2 “U-net is a CNN architecture that was first proposed for medical image segmentation…”, and in the abstract “We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities…Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique” and page 2, paragraph 3 “Blur can be introduced into the image during the imaging process by factors such as motion of the scene, wrong focus, atmospheric turbulence and optical point spread function. To remove the effect of blurring on an image is known as de-blurring which is a well known image enhancement technique. If the imaging conditions at the time of acquiring an image are known, it is much easier to deblur the image accurately”, which in view of the teachings of Wohrstein et al., read on wherein the determining (b) further comprises applying the one or more image files to an image processing model, wherein the image processing model: i) aligns the one or more image files in accordance with predetermined alignment criteria; ii) determines, for each image file in the one or more image files, a respective plurality of fluorophores, wherein the respective spatial location of each fluorophore is based at least in part on one or more point spread functions; and iii) outputs the combined plurality of localizations by compiling the plurality of fluorophores for each respective image file in the one or more image files.
It would have been obvious at the time of filing to modify the teachings of Wohrstein et al. for the method of claim 1 as previously described with the teachings of Bannore et al. which expressly detail the use of super-resolution image techniques as the latter is a collection of reviews from the state of the art and would merely require a simple substitution of detailed methods. One would have had a reasonable expectation of success given that Bannore et al. is merely outlining and detailing actionable methods used in super-resolution image techniques and Wohrstein et al. is implementing such techniques. Therefore, it would have been obvious at the time of filing to have modified the teachings of each and to be successful.
Claim 3 is directed to the method of claim 2 but further specifies that the image processing model comprise either a neural network or a maximum likelihood-based model.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, which in view of the teachings of Wohrstein et al., read on wherein the image processing model comprises either a neural network or a maximum-likelihood-based model.
Claim 4 is directed to the method of claim 2 but further specifies that each localization comprises a superresolved localization.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”, reading on wherein each localization in the combined plurality of localizations comprises a superresolved localization.
Claim 5 is directed to the method of claim 1, but further specifies the determination of localization based on the spatial location and the fitting of a curve to each subset of localizations.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Wang et al. teaches on page 112, column 1, paragraph 2 “U-net is a CNN architecture that was first proposed for medical image segmentation…”, and in the abstract “We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities…Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, which in view of the teachings of Wohrstein et al. and Wang et al., read on wherein the segmenting (c) further comprises applying the combined plurality of localizations to a segmentation model, wherein the segmentation model: i) determines one or more subsets of localizations based at least in part on the respective spatial location of each localization in the combined plurality of localizations; and ii) fits a respective curve to each subset of localizations, thereby obtaining one or more fitted curves, wherein each fitted curve includes a location of each fluorophore in the respective subset of fluorophores along the respective fitted curve.
Claim 6 is directed to the method of claim 5 and thus claim 1, but further specifies that step (c) in claim 5 is repeated at least once.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, maximum likelihood will inherently perform step (c) a number of times equal to the number of locations, thereby reading on wherein the segmenting step (c) is repeated at least once.
Claim 7 is directed to the method of claim 1 but further specifies determining a probability for each target polymer sequence.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, maximum likelihood inherently calculates a probability, see equations 2.11-2.14 on pages 12 and 13 of Bannore et al., thereby reading on wherein the assembling (d) further comprises determining a corresponding probability of each respective target polymer sequence.
Claim 8 is directed to the method of claim 1 but further specifies comparing each respective sequence to every other sequence.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, maximum likelihood by definition is comparing the probabilities of all, sequences in this case, under the given parameters to find the highest or maximum likelihood of a sequence given those parameters, thereby reading on further comprising: e) determining a combined target polymer sequence by comparing each respective target polymer sequence to every other target polymer sequence in the set of target polymer sequences.
Claim 9 is directed to the method of claim 1 but further specifies the application of an optimization model to obtain the respective sequence.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, maximum likelihood is an optimization problem (optimizing for the maximum likelihood of see a given outcome), which in view of the segmentation model Wang et al. proposes thereby reads on wherein the assembling (d) further comprises, for each target polymer strand, applying the respective subset of localizations to an optimization model to obtain the respective target polymer sequence.
Claim 10 is directed to the method of claim 9 but further specifies the optimization model being defined as the maximum likelihood model provided.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, which along with equations 2.11-2.14 on pages 12 and 13 of Bannore et al., reads on the equation given in claim 10.
Claim 11 is directed to the method of claim 10 and thus claim 1, but further specifies that the prior probability be defined using the equation provided.
It would be inherent to define the prior probability of sequences as such given that only 4 potential nucleotide bases exist.
Claim 12 is directed to the method of claim 10 and thus claim 1, but further specifies that the non-uniform probability distribution of the prior probability of sequences is defined as the equation provided.
It would be obvious to define the non-uniform probability distribution of the prior probability of sequences as such given it is merely the multiplicative sum of the prior probability of sequences which is inherently defined as such given that only 4 potential nucleotide bases exist and the product of multiple independent events is the product of their individual variables, i.e. if we say that there is probability of a sequence occurring and only four outcomes for each position (the constraints of the fundamental biology) then the probability is a product of multiple independent events which is inherent to the system or reference frame we have created.
Claim 13 is directed to the method of claim 10 and thus claim 1, but further specifies that the optimization model include one or more of the additional parameters provided.
Wohrstein et al. teaches in paragraph [0086]-[0089] “Each nucleic acid detection entity comprises or consists of: (i) a specific probe nucleotide sequence, (ii) a localization nucleotide sequence for transient binding of a localization tag, and (iii) an identification nucleotide sequence for stable hybridization with an identification tag specific for the specific nucleic acid sequence”, of which transient binding is a binding mismatch.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique” and on page 87, paragraph 1 “Super-resolution reconstructs high-quality, high-resolution images by exploiting the fact that due to the relative motion between the camera sensor and the true scene, each aliased, under-sampled low-resolution frames acquired contains distinct incomplete and degraded scene information about the true scene. Therefore, in order for super-resolution to acquire this distinct scene information and successfully generate a high-resolution image, accurate knowledge of registration parameters is required for each of the input low-resolution frames”, which in the reconstruction of sequencing would obviously include the information of each nucleic acid detection entity, thereby reading on wherein the optimization model includes one or more additional parameters selected from the set of localization errors, binding rate, unbinding rate, oligo density, non-canonical base pairing, binding mismatch, background localization, or non- binding sites.
Claim 16 is directed to the method of claim 1 but further specifies that the image files contain at least one of the specified amounts of images.
Wohrstein et al. teaches in paragraph [0188] “Super-resolution techniques allow the capture of images with a higher resolution than the diffraction limit They fall into two broad categories: “true” super-resolution techniques, which capture information contained in evanescent waves, and “functional” super-resolution techniques, which use experimental techniques and known limitations on the matter being imaged to reconstruct a super-resolution image. In one embodiment, super-resolution microscopy allows single molecule localization”.
Bannore et al. teaches on page 87, paragraph 1 “To achieve accurate super-resolution image reconstruction, it is critical for image alignment to be precise”, on page 101, paragraph 1 “In the case of noise contaminated low resolution frames, employing such motion estimation scheme ensures that even with high noise, angle of rotation and translational shifts can be accurately recovered for precise alignment for maximum recovery of scene information to generate a high-resolution approximation of the true scene”, on page 24, paragraph 3 “Another Bayesian technique used in super-resolution reconstruction is the Maximum-Likelihood (ML) technique”, thereby reading on wherein the one or more image files comprises at least 1 image file, at least 2 image files, at least 3 image files, at least 4 image files, at least 5 image files, at least 6 image files, at least 7 image files, at least 8 image files, at least 9 image files, at least 10 image files, at least 25 image files, at least 50 image files, at least 75 image files, at lea