DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Herein, “the previous Office action” refers to the Non-Final Rejection filed on 5/30/2025.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/27/2025 has been entered.
Priority
As detailed on the Filing Receipt filed 2/3/2021, the instant application claims priority to as early as 11/21/2019. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Information Disclosure Statement
The Information Disclosure Statement filed on 12/19/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the IDS is included with this Office Action.
Claim Status
Claims 3, 5, 10-11, 14-15, 19, 22-23, 26-29, 31-32, 37-39, 41-42, 44-48, 51-60, 62-63, 65-67 are canceled.
Claims 1-2, 4, 6-9, 12-13, 16-18, 20-21, 24-25, 30, 33-36, 40, 43, 49-50, 61, 64 and 68-74 are pending, and examined herein.
Withdrawn Objections/Rejections
The rejections of claims 1-2, 4, 6-9, 12-13, 16-18, 20-21, 24-25, 30, 33-36, 40, 43, 49-50, 61, 64 and 68-74 under 35 USC § 103, as being unpatentable over combinations of Frisen, in view of SPOT, Cao, Smal, Cui, Akhras and/or Uchida have been withdrawn in view of the Examiner’s discovery of additional prior art.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 USC § 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 USC § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 1-2, 13, 16-18, 20-21, 40, 43, 49-50, 64, 68-71 and 73-74 are rejected under 35 USC § 103 as being unpatentable over Stahl et al (Science 353(6294): 78-82; published 7/1/2016), in view of Wong et al (Bioinformatics 34(11): 1966-1968; published 1/17/2018), SPOT (SPOT 5.0 Software User Guide, SPOT Imaging Solutions; published 2011; previously cited) and Cao et al (arXiv 1411.3229v2 [cs.CV]; published 1/13/2015; previously cited).
Claim 1 recites “[a] method of spatial analysis of analytes comprising”:
a) placing a sample on a substrate, wherein:
1. the substrate comprises fiducial markers and
2. a set of capture spots… compris[ing] at least 1000 capture spots;
b) obtaining one or more images of the sample on the substrate, wherein:
1. each image comprises pixels in the form of an array of pixel values, and
2. the array comprises at least 100,000 pixel values;
c) obtaining sequence reads, in electronic form, from the set of capture spots, wherein:
1. each capture probe plurality in a set of capture probe pluralities is at a different capture spot,
2. each capture probe plurality directly or indirectly associates with one or more analytes from the sample,
3. each capture probe plurality is characterized by at least one unique spatial barcode in a plurality of spatial barcodes, wherein each spatial barcode is associated with a particular location on the substrate,
4. the sequence reads comprise sequence reads corresponding to all or portions of the analyte(s),
5. the sequence reads comprise at least 10,000 sequence reads, and
6. each sequence read includes a spatial barcode of the corresponding capture probe plurality or a complement thereof;
d) using all or a subset of the spatial barcodes to localize sequence reads to corresponding capture spots, thereby dividing the sequence reads into subsets of sequence reads, each subset corresponding to a different capture spot;
e) using the fiducial markers to provide a composite representation comprising: (i) the image(s) aligned to the set of capture spots on the substrate, and (ii) a representation of all or a portion of each subset of sequence reads at each respective position within the image(s) that maps to a respective capture spot corresponding to the respective position of the analyte(s) in the sample, wherein a first image is aligned by:
1. analyzing the array of pixel values to identify a plurality of derived fiducial spots of the image,
2. using a substrate identifier uniquely associated with the substrate to select a template in a plurality of templates, wherein each template comprises fixed reference positions on the substrate for a corresponding plurality of reference fiducial spots and a corresponding coordinate system,
3. aligning the derived fiducial spots of the first image with the corresponding reference fiducial spots of the template using an alignment algorithm to obtain a transformation between the derived fiducial spots and the corresponding reference fiducial spots of the template, and
4. using the transformation and the coordinate system of the template to locate a corresponding position in the first image of each capture spot in the set of capture spots.
With respect to claim 1, Stahl discusses an analytical technique called “spatial transcriptomics” (pg. 78, Abstract), and discloses:
a) “plac[ing] on the slides sections of adult mouse olfactory bulb” (pg. 78, m. column), i.e., placing a sample on a substrate, wherein:
2. a slide surface includes “1007 features” (pg. 78, r. column), i.e., at least 1000 capture spots;
b) “the tissue was fixed, stained, and imaged” (pg. 78, m. column);
c) “After capturing and reverse-transcribing mRNA, we generate[] sequencing libraries… [and] sort[] the RNA-seq data to its corresponding array features” (pg. 78, r. column), i.e., obtaining electronic sequence reads from the set of capture spots, wherein:
1. oligonucleotide primers are immobilized on “each of [the] features” (pg. 78, m-r columns) such that each feature contains unique probes comprising a unique molecular identifier (pg. 80, Fig. 2A and caption),
2. mRNA in the sample “couple[] to”, i.e., sample analytes directly associate with, the arrayed oligonucleotides (pg. 78, r. column; pg. 80, Fig. 2A),
3. each feature contains unique probes comprising a spatial barcode (pg. 80, Fig. 2A and caption), also termed a “positional barcode” (pg. 78, r. column), i.e., at least one spatial barcode associated with a particular location on the substrate,
4. sequencing data corresponds to captured mRNA transcripts (pg. 78, r. column),
5. generation of 400 million sequence reads is exemplified (Supplement pg. 36, Table S1), and
6. “We sorted the RNA-seq data… by using the spatial barcodes” (pg. 78, r. column), necessitating that each sequence read includes a spatial barcode of the corresponding capture probe plurality;
d/e) “We sorted the RNA-seq data to its corresponding array features using the spatial barcodes and aligned the tissue image with the features of the array” to generate a composite representation (pg. 78, r. column; pg. 82, Figs. 4B and D);
Stahl also discusses alignment of images based on visible features (i.e., aligning fiducial spots of images), image thresholding and background object removal (Supplement pp. 9 and 12). However, Stahl does not describe steps of identifying derived fiducial spots as claimed; selecting a template using a substrate identifier as claimed; or aligning the derived fiducial spots with corresponding reference fiducial spots of the template, using an alignment algorithm, to obtain a transformation as claimed.
Nor does Stahl describe analysis of an array of pixel values comprising at least 100,000 pixels; or using the transformation and the coordinate system of the template to locate a corresponding position in the first image of each capture spot in the set of capture spots.
Wong discusses ST Spot Detector, a web tool that automates and facilitates alignment of transcriptome sequencing data with tissue images (pg. 1966, Abstract), and describes analysis of captured fluorescent spot image and bright field images with particular pixel resolutions (Supplemental pg. 2, Image tiling). In this way, Wong indicates that analyzed images are arrays of pixel values. Wong teaches conversion between (substrate) array coordinates and pixel coordinates (Supplemental pg. 6, Output file format).
Wong further describes performance of OpenCV blob detection on the fluorescent image, and storage of resultant keypoints containing detected blob positions and diameters (pg. 1967, r. column; Supplement pg. 3, Blob detection). In other words, analyzing the array of pixel values to identify a plurality of derived fiducial spots of the image.
Wong also describes user positioning of a frame indicating where outermost spots lie, and use of said frame as a guide to detect array coordinates, calculate column and row spacing, and calculate predicted array positions (pg. 1967, l. column; Supplement pg. 3, Calculating expected positions). This is considered equivalent to deriving a set of fixed reference positions on the substrate for a corresponding plurality of reference fiducial spots and a corresponding coordinate system., i.e., a template as claimed.
Wong additionally describes mapping of blob positions to spot array coordinates based on comparison of detected positions to expected positions (pg. 1967, r. column). This is considered equivalent to aligning the derived fiducial spots of the first image with the corresponding reference fiducial spots; and using the coordinate system to locate a corresponding position in the first image of each capture spot in the set of capture spots.
Wong further discusses generation of an affine transformation matrix that allows for conversion of array coordinates to pixel coordinates, and using the affine matrix to plot the spatial transcriptomic data onto the tissue image via scripts (Supplement pg. 6). This is considered equivalent to using an obtained transformation (along with the coordinate system) to locate capture spot positions.
However, Wong does not teach analysis of an array of pixel values comprising at least 100,000 pixels; using a substrate identifier uniquely associated with the substrate to select a template in a plurality of templates; or using an alignment algorithm to obtain a transformation between the derived fiducial spots and the corresponding reference fiducial spots.
SPOT is a manual for microscopy imaging software, and teaches that “Modern electronic imaging is based on the charged coupled device (CCD). All of today’s digital cameras have a CCD chip… composed of light-sensitive cells arranged in a checkerboard pattern. Each cell of the checkerboard is known as… a pixel… when you take a picture… each photosensitive cell receives photons of light, converts the photons to electrons, and then stores the electrons… Following the exposure, a digital camera… measures the voltage of each cell… converts the voltage to a binary number… [and] transmits this number down a cable to your computer… [which] reconstructs the image by assigning a brightness value to each pixel… proportional to the voltage of the corresponding cell on the CCD chip” (pg. 423). SPOT thereby teaches that modern digital images, such as those used for microscopy, comprise arrays of pixel values.
SPOT further discusses "resolution mode settings for SPOT cameras… [which] increase or decrease the resolution for any region of interest” comprising a 4Mp (i.e., 4 million pixel) “Normal setting” which “provides images of a quality similar to other color mosaic SPOT cameras” (pg. 49); a 16 Mp “16-shot setting” which “is suggested for… when… high resolution is required”; and a 36Mp “9-shot setting” which “is suggested for… when very high resolutions images are desired” (pg. 50). SPOT thereby teaches that 4Mp, i.e., over 100,000 pixels, is a standard camera resolution for capturing microscopy images.
Additionally, SPOT teaches “useful features for scripting your images and customizing the interface to meet your needs”, including “The Profile feature [which] allows you to name a particular configuration of image settings and recall it when you encounter that imaging situation again” (pg. 36). In other words, a SPOT ‘image profile’ is a stored image configuration set that can be selected, using an associated unique identifier, for use in a relevant imaging situation.
The combined teachings of SPOT (regarding user selection of an imaging profile) and Wong (regarding user positioning of an array frame and calculation of array coordinates and predicted positions, i.e., a template, therefrom) are considered to make obvious the claimed process of using a substrate-associated unique identifier to select a template.
However, SPOT does not teach identifying derived fiducial spots; using an alignment algorithm to obtain a transformation; or using the transformation and coordinate system to locate a position of each capture spot.
Cao discusses “methods of image registration for correlative microscopy”, and teaches that “Image registration estimates space transformations between images (to align them)… to combine for example knowledge about protein locations (using fluorescence microscopy) with high-resolution structural data” (pg. 2). Cao teaches “automatic landmark based registration. We extract landmarks based on the fiducials and compute the matching landmarks in both images. The transformation matrix is estimated from the corresponding landmarks” (pg. 2). Cao further teaches that these steps are performed by an “algorithm” (pg. 4), and that “a prerequisite of this algorithm is that fiducials exist in the images” (pg. 4).
Cao thereby teaches identifying derived fiducial spots; and aligning derived fiducial spots with reference fiducial spots using an alignment algorithm to obtain a transformation. Cao depicts an exemplary rendering of landmark positions produced, for a pair of images, by their methods (pg. 5, Fig. 2d). Cao additionally discusses relative advantages of this method, stating: “I applied both the proposed automatic landmark based image registration method and [a prior] method… I compared the mean absolute errors (MAE) and standard deviations (STD) of the absolute errors on all the corresponding landmarks… Our method improved the registration accuracy in both MAE and STD” (pg. 14).
With respect to claim 2, Stahl depicts a color-coded composite representation of spatial features mapped to a sectioned tissue image and a gene expression heat map indicating normalized, logarithmic transcript counts of particular genes from corresponding tissue sections (pg. 82, Figs. 4D-E).
With respect to claim 13, Stahl discloses that each capture probe comprises a capture region, i.e., each capture spot comprises a capture domain (pg. 80, Fig. 2A).
With respect to claim 16, Stahl depicts graphs indicating that each feature (i.e., each respective capture probe plurality) captures between around 2000 to around 8000 unique genes, and between around 5000 to around 80,000 unique transcripts from the sample (pg. 78, r. column; pg. 80, Fig. 2C; Supplement pg. 26, Fig. S5A).
With respect to claim 17, Stahl discloses probe sequences comprising 18-mer unique barcodes and 9-mer semi-randomized UMIs (Supplement pg. 2). The 18-mer barcode and 9-mer UMI of each probe sequence can be considered together as a unique 27-mer molecular identifier associated with a particular substrate location, equivalent to a unique spatial barcode that encodes a unique predetermined value.
Stahl exemplifies immobilization of approximately 200 million oligonucleotides at each of 1007 capture spots pg. 78, r. column; Supplement pp. 2- 3). 1007 x 200 million = 2.014 x 1011 probe sequences, each with a unique 27-mer. In this way, Stahl exemplifies embodiments wherein each unique 27-mer encodes a unique predetermined value that is a natural number between 1 and 2.014 x 1011.
With respect to claim 18, Stahl discloses immobilizing approximately 200 million oligonucleotides, i.e., more than 5 x 106 capture probes, at each capture spot (Supplement pg. 3).
With respect to claims 20-21, Stahl discloses a slide surface including “1007 features” (pg. 78, r. column) each containing probes comprising a unique 18-mer spatial barcode (pg. 80, Fig. 2A and caption; Supplement pg. 2), corresponding to “1007 capture oligonucleotides”, i.e., capture probe pluralities (Supplement pg. 2) comprising approximately 200 million probes immobilized at each capture spot (Supplement pg. 3). In other words, each capture probe in a respective plurality includes the same spatial barcode.
The disclosed probe sequences additionally comprise 9-mer semi-randomized UMIs (Supplement pg. 2). The 18-mer spatial barcode and 9-mer UMI sequence features can be considered together as equivalent to a 27-mer spatial barcode that is unique to each individual probe molecule. In this sense, the teachings of Stahl additionally read on embodiments wherein each capture probe in a respective plurality includes a different spatial barcode.
With respect to claim 40, Stahl discloses capture and analysis of mRNA from a sample (pg. 78, m. column).
With respect to claim 43, Stahl discloses inclusion of fluorescently-labeled nucleotides to visualize nucleic acids coupled to the arrayed oligonucleotides (pg. 78, m-r. columns; pg. 79, Fig. 1A), i.e., in situ sequencing of the set of capture spots on the substrate.
With respect to claims 49-50, Stahl discloses probe sequences comprising unique 18-mer barcodes (Supplement pg. 2).
With respect to claim 64, Stahl discloses bright field imaging and fluorescent imaging of samples (Supplement pp. 4 and 9).
With respect to claim 68, Stahl discloses acquiring image stacks, of samples containing fluorescently-labeled probes using a motorized camera stage (Supplement pg. 12). In this way, Stahl discloses obtaining a plurality of images comprising two or more fluorescent images.
With respect to claim 69, Stahl depicts a color-coded composite representation of spatial features mapped to a sectioned tissue image and a gene expression heat map indicating normalized, logarithmic transcript counts of particular genes from corresponding tissue sections (pg. 80, Figs. 4D-E), i.e., an image that communicates numbers of unique molecules that map to particular analytes in the sample represented by subsets of sequence reads that, in turn, map to respective capture spots (pg. 82, Figs. 4D-E).
With respect to claim 70, Stahl depicts a gene expression heat map that represents normalized, logarithmic transcript counts with a color scale (pg. 82, Fig. 4E).
With respect to claim 71, Stahl describes direct coupling of transcripts to oligonucleotide probes (pg. 78, m-r. columns; pg. 79, Fig. 1A).
Claim 73 recites a computer system comprising: one or more processors; memory; and one or more programs for spatial analysis of analytes, stored in the memory, configured to be executed by the processors, and including instructions for performing functions of substantive similarity to the process limitations of claim 1.
With respect to claim 73, Stahl acknowledges particular institutions for providing computational infrastructure and states that gene counts and scripts can be downloaded via the internet (pg. 82, Acknowledgements). The teachings of Stahl, in view of Wong, SPOT and Cao, are considered to apply to the functional limitations of the claim in the same manner as detailed above regarding the process limitations of claim 1.
Although Stahl does not expressly disclose implementation on a computer system comprising processor(s), memory and program(s), these are conventional computer elements that one of ordinary skill in the art would ‘at once envisage’ upon reading the disclosure of computational script implementation (see In re Petering, 301 F.2d 676, 681 (CCPA 1962)).
Claim 74 recites a nontransitory computer readable storage medium system storing one or more programs comprising instructions which, when executed by an electronic device with one or more processors and a memory, cause the electronic device to perform functions of substantive similarity to the process limitations of claim 1.
With respect to claim 74, Stahl states that gene counts and scripts can be downloaded via the internet (pg. 82, Acknowledgements). In this way, Stahl discloses computer implementation of the discussed data analysis techniques using scripts, i.e., programs comprising instructions which, when executed by an electronic device with one or more processors and a memory, cause the electronic device to perform constituent functions. The teachings of Stahl, in view of Wong, SPOT and Cao, are considered to apply to the functional limitations of the claim in the same manner as detailed above regarding the process limitations of claim 1.
Although Stahl does not expressly disclose storage of scripts on a nontransitory computer readable storage medium, one of ordinary skill in the art would ‘at once envisage’ storage on a nontransitory computer readable storage medium upon reading the disclosure of script implementation (see In re Petering, 301 F.2d 676, 681 (CCPA 1962)).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have combined alignment of detected blob positions (i.e., derived fiducial spots) with expected positions (i.e., reference fiducial spots) and usage of the array coordinate system to locate capture spots, as taught by Wong, with the spatial transcriptomic analysis method taught by Stahl, because Wong teaches that their techniques allow for simple, significantly labor-saving automation of spatial mapping of images and transcriptomic data (pg. 1966, l-r. columns). Said practitioner would have had a reasonable expectation of success because Stahl and Wong both discuss capturing and processing microscopy images.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have captured and analyzed images of over 100,000 pixels, as taught by SPOT, using the spatial transcriptomic analysis method taught by Stahl, because SPOT teaches that capturing digital images (i.e., comprising arrays of pixel values) of over 100,000 pixels is standard in the field of microscopy (pp. 49-50). Said practitioner would have had a reasonable expectation of success because Stahl and SPOT both discuss capturing and processing microscopy images.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have combined algorithmic landmark-based image registration, as taught by Cao, with the spatial transcriptomic analysis method taught by Stahl, because Stahl teaches image alignment based on features but does not disclose any particular steps for doing so while Cao discloses an algorithmic method for doing so automatically (pp. 2 and 4) with improved accuracy as compared to prior techniques (pg. 14). Said practitioner would have had a reasonable expectation of success because Stahl and Cao both discuss alignment of microscopy images based on fiducial markers.
In this way the disclosure of Stahl, in view of Wong, SPOT and Cao, makes obvious the limitations of claims 1-2, 13, 16-18, 20-21, 40, 43, 49-50, 64, 68-71 and 73-74. Thus, the invention is prima facie obvious.
Claims 4, 8-9 and 12 are rejected under 35 USC § 103 as being unpatentable over Stahl, in view of Wong, SPOT and Cao, as applied to claim 1 above, and further in view of Smal et al (IEEE Transactions on Medical Imaging 29(2): 282-301; published February 2010; previously cited).
With respect to claim 4, Stahl discusses image thresholding and background object removal (Supplement pp. 9 and 12). However, Stahl does not disclose defining a bounding box; removing pixels falling outside the bounding box; running a plurality of heuristic classifiers on the pixels; or applying a segmentation algorithm.
Wong describes an image tiling process wherein a tile size (e.g., 512 x 512 pixels) is defined and resized images are divided into ‘tiles’ using a crop function (Supplement pg. 2, Image tiling). In other words, defining bounding boxes within each image and removing pixels falling outside each bounding box.
Wong also teaches performance of fluorescent image spot enhancement via OpenCV contrast limited adaptive histogram equalization and adaptive black-white thresholding (pg. 1967, r. column; Supplement pg. 3, Image spot enhancement), i.e., applying a segmentation algorithm, and discusses segmentation of tissue images into ‘background’ and ‘tissue’ pixel components (Supplement pg. 5, Tissue mask algorithm).
Wong does not teach using fiducial markers to define a bounding box; or running a plurality of heuristic classifiers on the pixels.
SPOT teaches cropping an image to reflect an area of interest (pg. 92), i.e., removing pixels falling outside a bounding box. SPOT does not teach using fiducial markers to define a bounding box; or running a plurality of heuristic classifiers on the pixels.
Cao teaches that “one can register multichannel microscopy images of cells by registering cell segmentations” (pg. 3, para. 2), and further teaches algorithmic methods of image registration (pg. 4, para. 3). However, Cao does not teach running a plurality of heuristic classifiers on the pixels.
Smal discusses “Quantitative analysis of biological image data… involv[ing] the detection of many subresolution spots” (pg. 282, Abstract) and teaches that “the detection framework in general… can be split into three subsequent steps” (pg. 284, l. column), including: a process of “Signal Enhancement… [wherein] the image is transformed to… a 2-D… signal”, i.e., array of pixel values, “the value of which at any pixel measures the certainty in the object presence at that position… [and] can also be considered a probability map that describes possible object locations”; and a process of “Signal Thresholding… [wherein] the image… is thresholded, where the threshold… is applied to the signal magnitude”, i.e., intensity, “and the binary map… is obtained” (pg. 284, r. column). Smal further teaches that “Each detector”, i.e., particular spot detection algorithm, “considered in this paper includes these steps” (pg. 284, l. column).
One particular detector considered by Smal is the “AdaBoost algorithm… which… was recently shown to perform well also for spot detection in molecular bioimaging” (pg. 288, r. column). Smal teaches that this algorithm “is a weighted linear combination of… weak classifiers”, i.e., a plurality of heuristic classifiers (pg. 289, l. column), and describes its operation: “for each pixel… the value of the feature… is computed… Then, the… values are thresholded…producing a binary version… The procedure is repeated for all… features… and the images are combined”, i.e., scores are aggregated, “[and] thresholded… producing the [classification] map” (pg. 289, l. column).
With respect to claim 8, Wong teaches a process of segmenting an image into tissue and background components comprising steps of: detecting edges, thresholding, filling, defining a binary background-tissue mask based on pixel intensity values, and initializing a GrabCut algorithm using the binary mask (pg. 1967, r. column; Supplemental pg. 5, Tissue mask algorithm). In other words, overlaying a mask on the image that causes each respective pixel to be assigned a first or second attribute.
With respect to claim 9, Wong describes grayscaling and thresholding to produce a binary image wherein black (i.e., a first color) correspond to background pixels and white (i.e., a second color) corresponds to object/tissue pixels (Supplement pg. 3, Image spot enhancement).
With respect to claim 12, Wong describes reclassifying connected ‘tissue’ components (i.e., groups of neighboring ‘tissue’ pixels forming prospective capture spot representations) as ‘background’ if they make up less than 1% of the total area of the mask (Supplement pg. 5, Tissue mask algorithm). In other words, assigning each capture spot representation a first or second attribute based upon the assignment of pixels in the vicinity.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have utilized a plurality of heuristic classifiers, as taught by Smal, in combination with the spatial transcriptomic analysis method disclosed by Stahl, in view of Wong, SPOT and Cao, because Stahl, Wong, and Cao disclose steps which require detection of image objects (e.g., feature-based image alignment) while Smal discloses a well-performing algorithmic technique for detecting image objects (pg. 282, Abstract; pg. 288, r. column – pg. 289, l. column). Said practitioner would have had a reasonable expectation of success because Stahl, Wong, Cao and Smal all discuss feature-based analysis of microscopy images.
In this way the disclosure of Stahl, in view of Wong, SPOT, Cao and Smal, makes obvious the limitations of claims 4, 8-9 and 12. Thus, the invention is prima facie obvious.
Claims 6-7, 61 and 72 are rejected under 35 USC § 103 as being unpatentable over Stahl, in view of Wong, SPOT and Cao, as applied to claim 1 above, and further in view of Cui et al (Frontiers in Cell Developmental Biology 4: article 89, pp. 1-11; published 9/5/2016; previously cited).
With respect to claim 6, Stahl discloses spatial transcriptomic analysis (pg. 78, Abstract), but does not disclose determining haplotype identity.
Wong discusses spatial transcriptomic analysis (pg. 1966, l. column), but does not teach determining haplotype identity.
SPOT discusses presenting information (e.g., histology or cell pathology) related to an image capture in the form of a sample report (pg. 347). SPOT does not teach determining haplotype identity.
Cao exemplifies registration of “TEM/Confocal Microscopic image pairs of mouse brains… to localize brain regions associated with Pelizaeus-Merzbacher Disease (PMD) and do quantitative assessment of hypomyelination and demyelination... PMD is one of a group of genetic disorders characterized by progressive degeneration of the white matter of the brain affecting the myelin sheath” (pg. 13, para. 5). Cao does not teach determining haplotype identity.
Cui discusses “Fluorescence in situ hybridization (FISH)” and teaches “visualization of chromosome haplotypes from differentially specified single-nucleotide polymorphism loci” (pg. 1, Abstract). Cui describes one previous study utilizing “locus-specific… probes within a 900 Kb 17q12 inversion hybridizing onto stretched DNA fibers [to] correlate[] the inversion orientations with associated haplotypes, which allowed the evaluation of inversion frequencies among human populations globally” (pg. 6, r. column). Cui thereby teaches that determining haplotype identity of target sequences has genetic research utility.
With respect to claims 7 and 61, Stahl discloses application of spatial transcriptomics to cancer diagnostics, and exemplifies analysis of expression of genes implicated in cancer progression between regions of a breast cancer biopsy (pg. 81, r. column – pg. 82, l. column and Fig. 4E). Stahl notes that this analysis revealed unexpected spatial heterogeneity, which may give more detailed prognostic information than regular transcriptome analysis (pg. 82, l. column).
With respect to claim 72, Cui discusses various signal-enhancing modifications of FISH including rolling circle amplification (RCA), wherein mRNA is reverse transcribed in situ and hybridized to padlock probes, which are then hybridized to fluorophore-coupled oligonucleotide probes for visualization (pg. 7, r. column – pg. 8, l. column). In other words, capture probes indirectly associates with a sample analyte through an analyte capture agent. Cui states that RCA is the only method capable of distinguishing single-nucleotide allelic changes in transcripts (pg. 7, r. column).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented haplotype determination and RCA probes, as taught by Cui, in combination with the spatial analysis method taught by Stahl, in view of Wong, SPOT and Cao, because Cui teaches that determination of associated haplotype via spatial analysis is enabled and has a range of clinical and research applications (pg. 1, Abstract; pg. 6, r. column; pg. 4, r. column), and that RCA is the only method capable of distinguishing single-nucleotide allelic changes in transcripts (pg. 7, r. column). Said practitioner would have had a reasonable expectation of success because Stahl, Wong and Cui all discuss spatial analysis of nucleic acids using probes and fluorescent microscopy.
In this way the disclosure of Stahl, in view of Wong, SPOT, Cao and Cui, makes obvious the limitations of claims 6-7 and 61. Thus, the invention is prima facie obvious.
Claims 24-25 are rejected under 35 USC § 103 as being unpatentable over Stahl, in view of Wong, SPOT and Cao, as applied to claim 1 above, and further in view of Eng et al (Nature Methods 14(12): 1153-1155; published 11/13/2017).
With respect to claim 24, Stahl discloses probe sequences comprising a poly-20TVN (T20VN) capture region (pg. 80, Fig. 2A and caption; Supplement pg. 2), i.e., each capture probe includes a capture domain. Stahl does not disclose a plurality of capture domain types, each configured to bind a different analyte.
Wong teaches spatial mapping of transcriptomic data obtained via barcoded capture probes (pg. 1966, l. column). Wong does not teach a plurality of capture domain types, each configured to bind a different analyte.
SPOT discusses imaging of stained specimens on glass slides and fluorescent specimens (pg. 45). SPOT does not teach a plurality of capture domain types, each configured to bind a different analyte.
Cao exemplifies registration of microscopy images (pg. 13, para. 5. Cao does not teach a plurality of capture domain types, each configured to bind a different analyte.
Eng discusses a method of transcriptomic profiling, called RNA sequential probing of targets (RNA SPOTs), involving imaging of mRNA transcripts captured by barcoded nucleic acid probes immobilized on a coverslip (pg. 1153, Abstract and l. column). Eng describes steps of serial hybridization of capture probes, including RNA-binding sequences, comprising different pluralities of probes specifically targeting coding regions of each of 10,212 different mRNAs (pg. 1153, r. columns). In this way, Eng teaches use of capture probe pluralities wherein each probe includes a capture domain type in a plurality of capture domain types, and each respective capture domain is configured to bind a different analyte.
Eng teaches that the barcoding space and targeted nature of RNA SPOTs allows for efficient abundance profiling of the entire transcriptome or specific sets of informative genes, saving on sequencing costs while accurately capturing essential information (pg. 1154, r. column).
With respect to claim 25, Eng exemplifies using a pool of 323,156 primary oligonucleotides, comprising 28 to 32 probes per gene, to target 10,212 mRNAs (pg. 1153, l-r. columns). Eng thus exemplifies capture probe pluralities comprising 10,212 capture domain types and respectively including 28 to 32 capture probes for each capture domain type, i.e., the plurality of capture domain types comprises between 2 and 15,000 capture domain types and the respective capture probe plurality includes at least 10 capture probes for each capture domain type.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented capture probe pluralities having a plurality of capture domain types, as taught by Eng, to enhance the spatial transcriptomic analysis method disclosed by Stahl, in view of Wong, SPOT and Cao, because Eng teaches that use of capture probes comprising a plurality of capture domains as described allows for efficient, low-cost transcriptomic profiling (pg. 1154, r. column). Said practitioner would have had a reasonable expectation of success because Stahl, Wong and Eng all discuss transcriptomic analysis via imaging of substrate-bound, barcoded oligonucleotide arrays.
In this way the disclosure of Stahl, in view of Wong, SPOT, Cao and Eng, makes obvious the limitations of claims 24-25. Thus, the invention is prima facie obvious.
Claim 30 is rejected under 35 USC § 103 as being unpatentable over Stahl, in view of Wong and Cao, as applied to claim 1 above, and further in view of Rodriques et al (Science 363: 1463-1467; published 3/29/2019).
With respect to claim 30, Stahl discloses a spatial transcriptomic analysis method involving deposition of a sample on a slide surface comprising a plurality of features (pg. 78, m-r. columns), i.e., capture spots. Stahl does not disclose implementation of at least 30 percent or more capture spots having a diameter of 80 microns or less.
Wong discusses a spot detection algorithm for use in spatial transcriptomic analysis (pg. 1966, l-r. columns), but does not teach implementation of at least 30 percent or more capture spots having a diameter of 80 microns or less.
SPOT discusses image overlays comprising circles of user-specified micron diameter (pg. 65), but does not teach implementation of at least 30 percent or more capture spots having a diameter of 80 microns or less.
Cao discusses image registration based on fiducial markers (pp. 2 and 4). Cao does not teach implementation of at least 30 percent or more capture spots having a diameter of 80 microns or less.
Rodriques discusses a method for obtaining scalable spatially resolved gene expression data, termed Slide-seq (pg. 1463, Abstract), and teaches a protocol involving capture of tissue mRNA by an array of DNA-barcoded 10 μm beads arranged in a layer on a rubber-coated glass coverslip (pg. 1463, l-m. columns). Rodriques thus teaches a spatial transcriptomic method wherein all of the capture spots have a diameter of 10 microns (i.e., at least ninety percent of the capture spots have a diameter of 80 microns or less).
Rodriques teaches that the high spatial resolution of Slide-seq was found to allow resolution of cell types in heterogenous tissue that could not be resolved when features were aggregated (pg. 1463, r. column).
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented capture spots having a diameter of 80 microns or less, as taught by Rodriques, to enhance the spatial transcriptomic analysis method disclosed by Stahl, in view of Wong, SPOT and Cao, because Rodriques teaches that imaging and analysis of capture spots having this small diameter allows for resolution of cell types that cannot be resolved with larger features (pg. 1463, r. column). Said practitioner would have had a reasonable expectation of success because Stahl, Wong and Rodriques all discuss spatial transcriptomic analysis.
In this way the disclosure of Stahl, in view of Wong, SPOT, Cao and Rodriques, makes obvious the limitations of claim 30. Thus, the invention is prima facie obvious.
Claims 33-36 are rejected under 35 USC § 103 as being unpatentable over Stahl, in view of Wong, SPOT, Cao and Smal, as applied to claim 4 above, and further in view of Uchida (Development, Growth & Differentiation 55: 523-549; published 2013; previously cited).
With respect to claim 33, Stahl discusses image thresholding and background object removal (Supplement pg. 12), but does not describe a thresholding technique wherein the intensity threshold represents a minimization of intra-class intensity variance and a maximization of inter-class variance.
Wong teaches performance of fluorescent image spot enhancement via OpenCV contrast limited adaptive histogram equalization and adaptive black-white thresholding (pg. 1967, r. column; Supplement pg. 3, Image spot enhancement), but does not further describe implementation of an intensity threshold representing a minimization of intra-class intensity variance and a maximization of inter-class variance.
SPOT teaches filtering of pixel noise based on a user-selected threshold value (pp. 94-95), but does not teach use of an intensity threshold representing a minimization of intra-class intensity variance and a maximization of inter-class variance.
Cao suggests “enforc[ing] local consistency among neighboring pixels” (pg. 3, para. 5), but does not describe an enforcement technique wherein an intensity threshold represents a minimization of intra-class intensity variance and a maximization of inter-class variance.
Smal describes a class of spot detection methods, known as top-hat filters, that implement dynamic object/background thresholding to minimize intra-class intensity variance for a ‘top’ (i.e., neighboring object) and ‘brim’ (i.e., background) pixel regions (pg. 287, l. column). Smal also discusses a weak classifier that finds an appropriate threshold that best separates two classes (pg. 289, l. column), i.e., maximizes inter-class variance. However, Smal does not discuss a classifier that identifies an intensity threshold representing both a minimization of intra-class intensity variance and a maximization of inter-class variance.
Uchida discusses image processing and pattern recognition techniques (pg. 523, Abstract), including methods for “decid[ing] whether each pixel belongs to one of two classes, white or black” (pg. 528, l. column). Uchida describes one such method, termed Otsu’s method, which “determines the threshold value that maximizes a criterion function that evaluates the separation of the given histogram… the value that separates the histogram into two parts as clearly as possible… the criterion function is designed to become larger when the mean values of both sides of the histogram separated by the threshold value are more different and… both sides have less variance” (pg. 527, r. column). In other words, Otsu’s method is a classifier that identifies an intensity threshold representing a minimization of intra-class variance and a maximization of inter-class variance.
With respect to claim 34, Uchida teaches that “Smoothing aims to minimize gray-level difference among neighboring pixels… [which] is often caused by noise and thus smoothing is useful for noise removal” (pg. 529, l. column). Additionally, Uchida teaches that “Sometimes… the constant threshold value over the image is insufficient. For example… [when] a part of the background region is brighter than some target region. In this case, global thresholding cannot extract all the targets without any false extraction from the background… Local thresholding will solve this problem by setting an appropriate binarization threshold at each pixel” (pg. 527, r. column – pg. 528, l. column). Uchida thereby teaches improved robustness of methods incorporating smoothing and local thresholding techniques (i.e., consideration of pixels in the local neighborhood).
Uchida further describes methods wherein “the class decision depends on two factors. The first factor is class-likelihood, i.e. the similarity to each class… The second factor is smoothness”, by which a criterion “that neighboring pixels are better to be assigned to the same class is introduced” (pg. 535, l. column). Addition of a smoothness criterion to Otsu’s classification method would produce a classifier that applies a smoothed measure of maximum difference in intensity between pixels in the local neighborhood.
With respect to claim 35, Smal teaches “morphological image filtering” (pg. 283, l. column), and states that “In practice, the signal thresholding… does not always produce fully connected regions (clusters of pixels)… in places where the true objects are located… clusters of nonzero pixels… that belong to the same spot are not connected or contain erroneous zero-pixels inside the cluster… to solve this problem, the closing operation from mathematical morphology… is frequently used” (pg. 285, l. column). Smal describes one such use, wherein “holes within clusters (objects) in the binarized classification map… were filled using the closing operation with a 5 x 5 mask” (pg. 292, l. column).
Uchida discusses “Edge detection… another popular filter. Edge is defined as a set of pixels with a large change in pixel value. For an image with a white-filled circle on a black background, the edge is a boundary of the circle. By edge detection filter, edge pixels are highlighted” (pg. 530, l. column). The combination of Smal and Uchida thereby teaches application of edge detection and morphological closing operations to binary pixel classification.
With respect to claim 36, Uchida teaches “decid[ing] whether each pixel belongs to one of two classes” (pg. 528, l. column). The specific class names recited by the claim are considered to be nonfunctional descriptive material, and thus not structurally limiting. See MPEP 2111.05.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented smoothed local thresholding and edge detection techniques in the claimed manner, as taught by Uchida, in combination with the spatial analysis method taught by Stahl, in view of Wong, SPOT, Cao and Smal, because Uchida teaches that these techniques are useful for noise removal from images (pg. 529, l. column) and well-known in the art for the purpose of pixel classification (pg. 530, l. column). Said practitioner would have had a reasonable expectation of success because Stahl, Wong, SPOT, Cao, Smal and Uchida all discuss microscopy image processing.
In this way the disclosure of Stahl, in view of Wong, SPOT, Cao, Smal and Uchida, makes obvious the limitations of claims 33-36. Thus, the invention is prima facie obvious.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the remarks filed 8/27/2025, Applicant traverses the rejections under 35 USC § 103 and presents supporting arguments pertaining to particular cited prior art references. Although the previous rejections under 35 USC § 103 have been withdrawn, in view of the Examiner’s discovery of additional pertinent prior art and according issuance of § 103 rejections on new grounds herein, several of the presented particular arguments pertain to references that remain applied in combination (e.g., SPOT). In the interest of clarity, the Examiner responds to the presented arguments herein as applicable to the amended claims.
Applicant alleges that the “profiles” discussed in SPOT are not equivalent to “templates” as claimed, as the “profiles” are stored sets of imaging parameters and do not, e.g., contain fixed spatial positions of physical features (pg. 14, paras. 2-4). The Examiner agrees that the “profiles” discussed in SPOT are not equivalent to “templates” as claimed, and the argument is found persuasive. However, SPOT is applied herein in combination with additional prior art. As detailed in the rejection, the combined teachings of Stahl, Wong, SPOT and Cao are considered to make obvious the referenced claim limitations pertaining to templates.
Applicant alleges that the “reticles” discussed in SPOT are not equivalent to reference fiducial spots as claimed, as the disclosed reticles exist only virtually and lack fixed correspondence to physical substrate features (pg. 14, para. 5 – pg. 15, para. 2). The representational nature of the claimed “reference fiducial spots” does not change the fact that they are coordinates within a data object (“template”), i.e., virtual. Moreover, the current rejections do not particularly rely upon SPOT for teaching the referenced claim limitations pertaining to reference fiducial spots and these limitations are considered obvious in light of the combined teachings of the applied art. See rejections for full details. Thus, the argument is found unpersuasive.
Applicant alleges that the X,Y pixel coordinates discussed in SPOT are not equivalent to a coordinate system tied to a physical substrate layout as claimed (pg. 15, paras. 4-5). Applicant’s argument pertains to the representational nature (“inherent relationship to physical positions”) of the claimed coordinate system to differentiate it from that discussed in SPOT, however, this representational nature does not fundamentally alter the identity of the claimed coordinate system as a coordinate system. Moreover, the current rejections do not particularly rely upon SPOT for teaching a coordinate system and incorporate a reference (Wong) which teaches a coordinate system having the discussed representational nature. Thus, the argument is found unpersuasive.
Applicant alleges that the “blank image” pixel grid discussed in SPOT is not equivalent to a substrate-specific template as claimed, as the “blank image” comprises arbitrary image space values unrelated to physical substrate geometry unless manually calibrated (pg. 15, para. 6 – pg. 16, para. 1). Applicant’s argument pertains to the representational nature of the claimed “templates” to differentiate them from the teachings of SPOT, however, this representational nature (and origin, by manual calibration or otherwise) does not fundamentally alter the identity of the claimed templates as stored pixel grid objects. Moreover, the current rejections do not particularly rely upon SPOT for teaching the referenced claim limitations pertaining to templates and these limitations are considered obvious in light of the combined teachings of the applied art. See rejections for full details. Thus, the argument is found unpersuasive.
Applicant alleges the silence of SPOT to the following claim features: selection of a geometry-based template using a substrate-specific identifier, fixed physical reference positions of fiducial spots, and a physical-to-image coordinate mapping for a uniquely-identified substrate (pg. 16, paras. 2-4). The Examiner agrees that SPOT does not teach all claim limitations pertaining to these features, and the argument is found persuasive. However, SPOT is applied herein in combination with additional prior art. As detailed in the rejections, the combined teachings of Stahl, Wong, SPOT and Cao are considered to make obvious the referenced claim limitations pertaining to templates.
Applicant alleges that the combination of Cao and SPOT fails to teach the claimed physical alignment framework, as Cao and SPOT describe image-space pixel coordinates that are not associated with the physical layout of a substrate or particular spatial barcodes (pg. 16, para. 5 – pg. 18, para. 3). The current rejections do not particularly rely upon Cao or SPOT for teaching the referenced claim limitations and these limitations are considered obvious in light of the combined teachings of the applied art. See rejections for full details. Thus, the argument is found unpersuasive.
Applicant further alleges that Frisen fails to cure the deficiency of SPOT and Cao regarding the clamed physical alignment framework, as Frisen merely discloses “marking the solid substrate” and does not disclose, e.g., alignment of derived fiducial positions in an image to fixed reference positions from a template to obtain a transformation (pg. 18, paras. 1-3). The Examiner agrees that Frisen does not teach all claim limitations pertaining to the referenced features, and the argument is found persuasive. However, as Frisen is no longer applied to the claims, the argument is considered moot. As detailed in the rejections, the applied combination of prior art is considered to make obvious the claim limitations pertaining to the referenced features.
Applicant alleges that claims 2, 13, 16-18, 20, 24-25, 30, 40, 43, 49-50, 64 and 68-74 are patentable for at least the same reasons discussed with respect to claim 1, as claims 73-74 are computer system and computer readable storage medium counterparts of claim 1 while claims 2, 13, 16-18, 20, 24-25, 30, 40, 43, 49-50, 64 and 68-72 depend from claim 1 (pg. 18, para. 5 – pg. 19, para. 1). As the combination of Frisen, SPOT and Cao is no longer applied to these claims, the argument is considered moot. As detailed in the rejections, the applied combinations of prior art are considered to make obvious the limitations of the cited claims.
Applicant notes that claims 4, 8-9, 12 and 33 depend from claim 1, notes that Smal reviews spot detection methods in fluorescence microscopy, and alleges that Smal fails to remedy the alleged deficiencies of Frisen, SPOT, and Cao (pg. 19, paras. 2-3). As the combination of Frisen, SPOT and Cao is no longer applied to these claims, the argument is considered moot. As detailed in the rejections, the applied combinations of prior art are considered to make obvious the limitations of the cited claims. Applicant notes that claims 6-7 and 61 depend from claim 1, notes the direction of Cui to the use of fluorescence in in situ cell-based genetic diagnostic and research applications, and alleges that Cui fails to remedy the alleged deficiencies of Frisen, SPOT, and Cao (pg. 19, para. 4). As the combination of Frisen, SPOT and Cao is no longer applied to these claims, the argument is considered moot. As detailed in the rejections, the applied combinations of prior art are considered to make obvious the limitations of the cited claims.
Applicant notes that claim 21 depends from claim 1, and alleges that Akhras fails to remedy the alleged deficiencies of Frisen, SPOT, and Cao. In particular, Applicant alleges that the barcodes of Akhras are not associated with a particular location on a substrate, and the probes of Akhras are designed for solution-phase circularization that is not readily adaptable to substrate immobilization (pg. 20, para. 1 – pg. 21, para. 4). As the combination of Frisen, SPOT and Cao is no longer applied to these claims, nor is Akhras, the argument is considered moot. As detailed in the rejections, the applied combinations of prior art are considered to make obvious the limitations of the cited claim.
Applicant notes that claims 34-36 depend from claim 1, notes that Uchida reviews various image processing and recognition techniques, and alleges that Uchida fails to remedy the alleged deficiencies of Frisen, SPOT, and Cao (pg. 21, para. 5). As the combination of Frisen, SPOT and Cao is no longer applied to these claims, the argument is considered moot. As detailed in the rejections, the applied combinations of prior art are considered to make obvious the limitations of the cited claims.
For the above reasons, the arguments are found unpersuasive and the rejections are maintained.
Conclusion
At this point in prosecution, no claim is allowable.
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 February 11, 2026