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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on DD Month YYYY is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements is/are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 5 – 7 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 5 states “wherein the whole slide imaging modality is selected from bright field or fluorescence imaging”, however claim 4 from which claim 5 depends lists out multiple options of modalities that acquire the first and second visualization modalities (whole slide imaging, microscopy, non-optical imaging, or spatial transcriptomics imaging). As such, there is a possibility that a whole slide imaging modality is not utilized in the method but instead one or more of the other types of modalities. In this circumstance, it is not possible for the whole slide image to be selected from bright field or fluorescence imaging as it is not being used.
Claim 6 and 7 recite similar limitations, however they are drawn to further limiting the microscopy modality and the non-optical imaging modalities respectively. These claims also have the same issue where their specific modalities may not be utilized in the method, however they are further limiting those specific types of modalities though they may not be used.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 15, 19, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Chiaruttini et al (Chiaruttini, Nicolas. (2022). An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix. Frontiers in Computer Science. 3. 10.3389/fcomp.2021.780026, hereinafter “Chiaruttini”) in view of Kiemen et al (U.S. Patent Publication No. 2025/0139765 A1, hereinafter “Kiemen”).
Regarding claim 1, Chiaruttini teaches a method for classifying tissue cells into one or more phenotypes (Figure 3A; Examiner’s note: The examiner is interpreting “Phenotypes” in view of ¶ 0033 of the applicant’s specification filed 11 March, 2025 “As used herein “phenotype”, “phenotyping”, or “phenotyped” may be broadly understood to refer to an identification of one or more tissue cells or immune cells… a spatial location of one or more tissue cells or immune cells, a count of one or more tissue cells or immune cells”.), the method comprising, by one or more computing devices:
receiving a plurality of images of a set of tissue cells, the plurality of images comprising at least a first image including a first visualization modality and a second image including a second visualization modality (Page 1, Section “Introduction”: Whole slide imaging (WSI) is the process of acquiring images of tissue sections.; Page 5, Col. 1, ¶ 3: This sample requires alignment and registration because it consists of two multi modal images (one fluorescent, one IHC) where different zones of the sample were imaged, with different cameras, and some stitching artefacts are present. However, note that the exact same cells are imaged in the different modalities, which is a requirement for this workflow);
identifying a first tissue cell of the set of tissue cells in the first image and the first tissue cell in the second image (Page 5, Col. 1, ¶ 3: Since the same (dividing) cells are targeted in both images, we expect a very good match between the positively detected cells in both modalities.; page 5, Col. 2, ¶ 2: We first annotated a region hosting most of the dividing cells in the fluorescent (moving) image… Thanks to the generated transformation file, the annotations and detections can be transformed and transferred to their target location in the (fixed) DAB image (Figure 3A). Cell properties can then be measured and compared between both images.);
performing a cell-to-cell registration process based on the first tissue cell identified in the first image and the first tissue cell identified in the second image, the cell-to-cell registration process comprising matching of the first tissue cell identified in the first image to the first tissue cell identified in the second image (Page 5, Col. 1, ¶ 3: The sample requires alignment and registration because it consists of two multi modal images…; page 5, Col. 2, ¶ 2: We first annotated a region hosting most of the dividing cells in the fluorescent (moving) image… Thanks to the generated transformation file, the annotations and detections can be transformed and transferred to their target location in the (fixed) DAB image (Figure 3A). Cell properties can then be measured and compared between both images… We are thus transferring cell regions of interest (ROIs) from the moving image to the fixed image. Note that the deformation computed in this workflow is invertible, meaning that it is also possible to segment cells regions in the fixed image and then transfer them to the moving image.); and
classifying the first tissue cell into a phenotype based on the cell-to-cell registration process, (Figure 3A; Page 6, Col. 1, ¶ 1: These graphs allow counting cells which are considered to be dividing cells for both modalities (EdU+/DAB+), non-dividing in both modalities (EdU-/DAB-), and which give different outcomes based on the modalities (EdU+/DAB-) and (EdU-/ DAB+).; Page 5, Col 1, ¶ 3: The fluorescence based 5-Ethynyl-2’deoxyuridine (EdU) staining with click chemistry was followed by a 3,3’-Diaminobenzidine (DAB) revealing using Anti-Bromodeoxyuridine (BrdU) antibodies, leading to the immunohistochemistry (IHC) image.).
Chiaruttini does not explicitly teach the phenotype at least partially indicative of a disease pathology.
However, Kiemen does teach the phenotype at least partially indicative of a disease pathology (¶ 0120: The computing system 102 can perform deep learning semantic segmentation with manual annotations of tissue types (step D, 110)… Deep learning methods can identify many structures in H&E images, such as inflammation, cancer cells, and extracellular matrix (ECM).).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
This motivation for the combination of Chiaruttini and Kiemen is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 2, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Chiaruttini teaches further comprising generating a phenotyping table based on the phenotype classification of the first tissue cell (Figure 3A, 3B; Page 6, Figure 3A: The EdU fluorescence image (moving image) is annotated and cells are detected thanks to the DAPI stain within a region of interest. Cells detected in the fluorescence image are transferred to the DAB image (fixed image), using a specific registration result file.; Page 6, Figure 3B: Each cell property for each image can be measured and combined during downstream analysis. Scatter plots of 4,393 detected cells, X: mean fluorescence EdU intensity, Y: mean DAB optical density.).
Additionally, Kiemen teaches further comprising generating a phenotyping table based on the phenotype classification of the first tissue cell (Figure 5A; ¶ 0181: 506 depicts a table showing local tissue subtype cell densities. In addition to bulk cell density, local cell density (ρlocal) can be used by the computing system for deeper exploration of these structural changes.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 3, the Chiaruttini and Kiemen combination teaches the method of claim 2.
Additionally, Chiaruttini teaches further comprising mapping the phenotype classification of the first tissue cell identified in the first image to the first tissue cell identified in the second image utilizing the phenotyping table (Figure 3A, 3B; Page 6, Figure 3B: Each cell property for each image can be measured and combined during downstream analysis. Scatter plots of 4,393 detected cells, X: mean fluorescence EdU intensity, Y: mean DAB optical density. Each plot corresponds to a specific transform file, respectively from left to right: - manual annotation of 3,000 + landmarks (ground truth) - manual rigid transformation 1)—manual rigid transformation + automated affine transformation 2)—all steps in 2) + automated spline transformation 3)—all steps in 3) + manual spline transformation. Examiner’s note: It is not clear in claims 3 or 2 how the table is utilized for mapping the classification of the first image to the cell identified in the second image. As such, the examiner is interpreting the use of information from the table that is then expressed or visualized in any way on the image to read on this claim under broadest reasonable interpretation. The examiner recommends amending this claim to narrow the scope and make clear how the table is utilized in mapping the images.).
Regarding claim 4, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Chiaruttini teaches wherein each of the first visualization modality and the second visualization modality is independently acquired by a whole slide imaging modality, microscopy modality, non-optical imaging modality, or spatial transcriptomics (ST) imaging modality (Page 1, Section “Introduction”: Whole slide imaging (WSI) is the process of acquiring images of tissue sections.; Page 2, Col. 2, ¶ 1: In the following we present a fast and easy to use workflow for whole slide image registration.).
Regarding claim 5, the Chiaruttini and Kiemen combination teaches the method of claim 4.
Additionally, Chiaruttini teaches wherein the whole slide imaging modality is selected from bright-field or fluorescence imaging (Page 1, Section “Introduction”: Most of the commercially available systems accommodate at least two modalities: brightfield and fluorescence imaging.; Page 5, Col. 1, ¶ 3: This sample requires alignment and registration because it consists of two multi modal images (one fluorescent (emphasis added), one IHC) where different zones of the sample were imaged, with different cameras, and some stitching artefacts are present.).
Regarding claim 6, the Chiaruttini and Kiemen combination teaches the method of claim 4.
Additionally, Chiaruttini teaches wherein the microscopy modality is selected from bright-field microscopy, fluorescence microscopy, confocal microscopy, high-content screening (HCS) microscopy, or synthetic image generation (Page 1, Section “Introduction”: Most of the commercially available systems accommodate at least two modalities: brightfield and fluorescence imaging.; Page 5, Col. 1, ¶ 3: This sample requires alignment and registration because it consists of two multi modal images (one fluorescent (emphasis added), one IHC) where different zones of the sample were imaged, with different cameras, and some stitching artefacts are present.).
Regarding claim 7, the Chiaruttini and Kiemen combination teaches the method of claim 4.
Additionally, Kiemen teaches wherein the non-optical imaging modality is selected from Imaging Mass Cytometry (IMC) or Multiplex Ion Beam Imaging (MIBI) (¶ 0216: FIG. 12A depicts integration of the disclosed technology with one or more additional imaging modalities 1200. 1202 depicts serially sectioned images that can be reconstructed using the disclosed techniques with integration of(l) H&E, (2) immunocytochemistry (HC), (3) immunofluorescence (IF), ( 4) imaging mass cytometry (IMC), and spatial transcriptomics/proteomics.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 8, the Chiaruttini and Kiemen combination teaches the method of claim 4.
Additionally, Chiaruttini teaches wherein at least one of the first visualization modality and the second visualization modality comprises a dye-based visualization modality (Page 5, Col. 1, ¶ 3: This sample requires alignment and registration because it consists of two multi modal images (one fluorescent, one IHC) where different zones of the sample were imaged, with different cameras, and some stitching artefacts are present. However, note that the exact same cells are imaged in the different modalities, which is a requirement for this workflow).
Regarding claim 9, the Chiaruttini and Kiemen combination teaches the method of claim 8.
Additionally, Chiaruttini teaches wherein the dye-based visualization modality is selected from histological staining, fluorescence in situ hybridization (FISH), or immunofluorescence staining (Page 5, Col 1, ¶ 3: The fluorescence based 5-Ethynyl-2’deoxyuridine (EdU) staining with click chemistry was followed by a 3,3’-Diaminobenzidine (DAB) revealing using Anti-Bromodeoxyuridine (BrdU) antibodies, leading to the immunohistochemistry (IHC) image.).
Regarding claim 10, the Chiaruttini and Kiemen combination teaches the method of claim 9.
Additionally, Kiemen teaches wherein the histological staining comprises hematoxylin and eosin (H&E) staining or chromogenic staining (¶ 0105: The disclosed technology, on the other hand, incorporates nonlinear image registration and deep learning techniques to create multi-labelled tissue volumes using H&E (hematoxylin and eosin) images alone, avoiding the need for additional stains for tissue labelling.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 11, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Chiaruttini teaches wherein at least one of the first visualization modality or the second visualization modality comprises immunostaining (Page 5, Col 1, ¶ 3: The fluorescence based 5-Ethynyl-2’deoxyuridine (EdU) staining with click chemistry was followed by a 3,3’-Diaminobenzidine (DAB) revealing using Anti-Bromodeoxyuridine (BrdU) antibodies, leading to the immunohistochemistry (IHC) image.).
Regarding claim 12, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Kiemen teaches wherein classifying the first tissue cell into the phenotype comprises classifying, based on one or more molecular annotations, a cell state of the first tissue cell as an activated immune cell (Figure 12A; ¶ 0216: 1206 depicts Multi-modal 3D reconstructions, such as immune cell heatmaps of pancreatic cancer precursor lesions.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 13, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Kiemen teaches wherein the first tissue cell comprises a cancer cell, a plasma cell, a lymphocyte, a macrophage, or a fibroblast (¶ 0009: Moreover, the disclosed technology provides for accurate and reliable identification of cancer cells protruding along aligned fibers, which can suggest that pancreatic cancer cells, or other cancer cells, in-situ may invade more easily in regions of aligned collagen and nerve fibers.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 14, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Additionally, Kiemen teaches wherein classifying the first tissue cell into the phenotype comprises classifying, based on one or more molecular annotations, the first tissue cell as an immune cell (¶ 0216: 1206 depicts Multi-modal 3D reconstructions, such as immune cell heatmaps of pancreatic cancer precursor lesions.).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
Regarding claim 15, the Chiaruttini and Kiemen combination teaches the method of claim 14.
Additionally, Kiemen teaches wherein the immune cell comprises a macrophage, a regulatory T-cell (Treg), a CD8 cell, a B lymphocyte, or a natural killer (NK) cell (¶ 0227: Deep learning semantic segmentation can also be applied such that IHC staining provides for detection of leukocytes, T cells, and regulatory T cells (1604).).
Kiemen is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Kiemen can outperform tissue clearing methods in its ability to create easily quantifiable tissue volumes, allowing quantification of deceptively simple concepts such as 3D cell count and density, vascular connectivity, tumor branching and morphology, and cm-scale tissue heterogeneity. (See ¶ 0106).
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of device claim 19 while noting that the rejection above cites to both method and device disclosures.
For the device limitations of claim 19 see Kiemen’s teaching on:
A system including one or more computing devices (¶ 0321: the system can include one or more processors, and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computer system to perform operations), comprising:
One or more non-transitory computer readable storage media including instructions (¶ 0321: the system can include one or more processors, and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computer system to perform operations) and
One or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions to (¶ 0321: the system can include one or more processors, and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computer system to perform operations)…
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of manufacture claim 37 while noting that the rejection above cites to both method and manufacture disclosures.
For the manufacture limitations of claim 37 see Kiemen’s teaching on:
A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of one or more computing devices, cause the one or more processors to (¶ 0321: the system can include one or more processors, and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computer system to perform operations):
Claims 16 – 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chiaruttini et al (Chiaruttini, Nicolas. (2022). An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix. Frontiers in Computer Science. 3. 10.3389/fcomp.2021.780026, hereinafter “Chiaruttini”) in view of Kiemen et al (U.S. Patent Publication No. 2025/0139765 A1, hereinafter “Kiemen”) and further in view of Masaeli et al (U.S. Patent Publication No. 2024/0153289 A1, hereinafter “Masaeli”).
Regarding claim 16, the Chiaruttini and Kiemen combination teaches the method of claim 1.
Chiaruttini does not explicitly teach wherein the disease pathology comprises a non-Hodgkin's lymphoma (NHL) disease pathology.
However, Masaeli teaches wherein the disease pathology comprises a non-Hodgkin's lymphoma (NHL) disease pathology (¶ 0243: In some embodiments, the system and methods disclosed herein are used to identify cancer cells from biological samples acquired from mammalian subjects…; ¶ 0248: Non-limiting examples of cancer of interest can include… non-Hodgkin lymphoma…).
Masaeli is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Masaeli’s classifier is capable of abstracting morphological features characteristic of cell classes that it has been trained on, and also that using larger and more morphologically diverse sets of representative samples can improve and generalize model performance further. (See ¶ 0349).
This motivation for the combination of Chiaruttini, Kiemen, and Masaeli is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 17, the Chiaruttini, Kiemen, and Masaeli combination teaches the method of claim 16.
Additionally, Masaeli teaches wherein the NHL disease pathology comprises follicular lymphoma (FL) (¶ 0243: In some embodiments, the system and methods disclosed herein are used to identify cancer cells from biological samples acquired from mammalian subjects…; ¶ 0248: Non-limiting examples of cancer of interest can include… follicular lymphoma…).
Masaeli is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Masaeli’s classifier is capable of abstracting morphological features characteristic of cell classes that it has been trained on, and also that using larger and more morphologically diverse sets of representative samples can improve and generalize model performance further. (See ¶ 0349).
Regarding claim 18, the Chiaruttini, Kiemen, and Masaeli combination teaches the method of claim 16.
Additionally, Masaeli teaches wherein the NHL disease pathology comprises diffuse large B-cell lymphoma (DLBCL) (¶ 0243: In some embodiments, the system and methods disclosed herein are used to identify cancer cells from biological samples acquired from mammalian subjects…; ¶ 0248: Non-limiting examples of cancer of interest can include… Diffuse large B cell lymphoma…).
Masaeli is considered to be analogous art as it pertains to cellular imaging and pathology classification. Therefore, it would have been obvious to one of ordinary skill in the art to combine the open-source whole slide image registration workflow (as taught by Chiaruttini) and the techniques for three-dimensional reconstruction and multi-labelling of serially sectioned tissue (as taught by Kiemen) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Masaeli’s classifier is capable of abstracting morphological features characteristic of cell classes that it has been trained on, and also that using larger and more morphologically diverse sets of representative samples can improve and generalize model performance further. (See ¶ 0349).
Conclusion
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/ANDREW B. JONES/Examiner, Art Unit 2667
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