DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim(s) 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Petitjean (US 20230019599).
Regarding claim 1:
Petitjean discloses: an evaluation method for a biological sample (¶ [0058] “FIG. 2 is a high-level flow diagram of a process for quantifying a phenotype of fibrosis in a digital image of a biological tissue sample”) , comprising:
an image acquisition step of imaging the biological sample by optical coherence tomography and acquiring an image (¶ [0106] “At step 220, the server 104 receives a digital image of a biological tissue sample…”; ¶ [0108] “…the image is taken using a tissue imaging method or combination of tissue imaging methods or modalities that can enrich the detection signal of the fibrous tissue, so that the collagen in the resulting image of the tissue sample is more easily detected. Suitable tissue imaging methods include fluorescent imaging, using ex-vivo fresh tissue, performing optical biopsies, in-vivo imaging (such as endoscopic imaging, for example). In general, any digital imaging and optical methods may be used, including stained histopathology slides imaged by Whole Slide Imaging Scanners, two-photon microscopy, fluorescence imaging, structured imaging, polarized imaging, CARS, OCT images”);
a region extraction step of extracting a fibrotic region in which a cell is fibrosed, from the taken image (¶ [0021] “…an object of the present disclosure to quantify the phenotype of fibrosis in a biological tissue sample that considers multiple, if not all, the degrees of complexity of the collagens and its traits. As used herein, “fibrosis” includes all kinds of collagen-based structures, regardless of the chemical nature of the collagen”; ¶ [0107] “…such pre-processing may include color-based segmentation, thresholding, filtering, enhancement, texture analysis, binarization, edge detection, region analysis, Fourier transformation, object detection, object analysis segmentation, skeletonization, machine learning, deep learning for image processing, 2D and 3D variants of any of these techniques, and any other computational technique that can enrich the extraction of collagens from an image”; ¶ [0109] “…quantify a plurality of parameters, each parameter describing a feature of the collagens in the biological tissue sample that is expected to be different for different phenotypes of fibrosis. The quantitative parameters represent various features of collagens appearing in the digital image, either individually or as a group, and are the same parameters described above, selected from the set of candidate parameters stored in databases 106b-d and described below in detail in relation to FIGS. 3-9.”; ¶ [0082] “…FIGS. 3-9 show exemplary ways to extract various data from digital images taken of biological tissue samples having differing severity of fibrosis as defined by pathologists and widely proven by patient outcomes”) and
an evaluation step of analyzing the fibrotic region, to evaluate a condition of the biological sample (¶ [0118] “Returning to FIG. 2, at step 224, the server 104 combines the plurality of parameters quantified at step 222, to obtain one or more composite scores indicative of a phenotype of fibrosis for the biological tissue sample. The composite score may be derived based on a mathematical transfer function that combines some or all of the quantitative parameters computed at step 222, such as a sum of the selected quantitative parameters, where the sum can be a normalized sum or a weighted sum. The composite score precisely quantifies the fibrosis phenotype (generally along a continuous scale so that it improves upon the coarse, categorical approaches of the prior art by providing wide dynamic range and high resolution), and can be used to describe the state of fibrosis in the biological tissue sample, progression of fibrosis in the sample, or regression of fibrosis in the sample in response to treatment. Derivation of the method to compute the composite score may involve manual and/or automated methods that reduce the dimension of the calibration data set (by identifying candidate parameters that have the best signal-to-noise and are validated by existing models of fibrosis (such as METAVIR), as described below with reference to FIGS. 10-12), identify correlations and/or principal components, or any combination thereof.”.
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.
Claim(s) 2 are rejected under 35 U.S.C. 103 as being unpatentable over Petitjean (US 20230019599) in view of Koh (US 20200218874).
Regarding claim 2:
Petitjean discloses the limitations of claim 1 as applied above.
Petitjean does not specifically teach: wherein the region extraction step includes: a first extraction step of extracting an entire region corresponding to the biological sample from the taken image; a second extraction step of extracting a high-intensity region of which intensity value is higher than a predetermined threshold value in the entire region; and a third extraction step of extracting a region that is left after the high-intensity region and a region inner than the high-intensity region are excluded from the entire region, as the fibrotic region.
However, in a related field, Koh teaches: wherein the region extraction step includes:
a first extraction step of extracting an entire region corresponding to the biological sample from the taken image (¶ [0093] “Briefly, “Spheroid Peeling” involves repeatedly segmenting the spheroid image from the periphery to the core zone. The entire spheroid was first segmented as an object (hereby referred to as spheroid object) and cropped from the original well image (FIG. 6)”); ¶ [0018] “…identification of primary object is achieved using (1) thresholding and (2) filtering. The thresholding step involves identifying the foreground region from the background region using Maximum correlation threshold”)
a second extraction step of extracting a high-intensity region of which intensity value is higher than a predetermined threshold value in the entire region (¶ [0018] “…segmentation is conducted in the brightfield channel where the images are represented as pixels with different intensity levels. In Cell Profiler, identification of primary object is achieved using (1) thresholding and (2) filtering. The thresholding step involves identifying the foreground region from the background region using Maximum correlation threshold”. Also, see Petitjean ¶ [0073] “FIG. 18 depicts a processed version of an exemplary digital image, in which certain regions of the digital image are recognized as collagen (gray pixels), and the collagen is organized into various collagen objects of different collagen classes (indicated by different grayscale intensities, according to an illustrative implementation”); and
a third extraction step of extracting a region that is left after the high-intensity region and a region inner than the high-intensity region are excluded from the entire region, as the fibrotic region (¶ [0093] ““Spheroid Peeling” involves repeatedly segmenting the spheroid image from the periphery to the core zone …An “inner core” was then identified from the spheroid object, hereby referred to as quiescent object. The proliferating zone was obtained by masking the quiescent object from the spheroid object. Similarly, the necrotic zone was identified as the “inner core” of the quiescent object, and the quiescent zone was obtained by masking the necrotic zone from the quiescent object.”; also see Petitjean ¶ [0107] “…pre-processing may include color-based segmentation, thresholding, filtering, enhancement, texture analysis, binarization, edge detection, region analysis, Fourier transformation, object detection, object analysis segmentation, skeletonization, machine learning, deep learning for image processing, 2D and 3D variants of any of these techniques, and any other computational technique that can enrich the extraction of collagens from an image.”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Petitjean to incorporate the teachings of Koh by including wherein the region extraction step includes: a first extraction step of extracting an entire region corresponding to the biological sample from the taken image; a second extraction step of extracting a high-intensity region of which intensity value is higher than a predetermined threshold value in the entire region; and a third extraction step of extracting a region that is left after the high-intensity region and a region inner than the high-intensity region are excluded from the entire region, as the fibrotic region in order to establish a non-invasive system for continuous monitoring of the response kinetics of the 3D spheroids in a high-throughput set-up.
Claim(s) 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Petitjean (US 20230019599) in view of De (US 20190005684).
Regarding claim 3:
Petitjean discloses the limitations of claim 1 as applied above.
Petitjean further discloses: a second calculation step of calculating an area of the fibrotic region in the taken image (¶ [0115] “..As depicted in FIG. 4, some parameters correspond to transformations of one or more other parameters. For example, total collagen area ratio is one parameter listed in FIG. 4, and so is its square root.”).
Petitjean does not specifically teach: a first calculation step of calculating an area of the entire biological sample in the taken image.
However, in a related field, De teaches: a first calculation step of calculating an area of the entire biological sample in the taken image (¶ [0038] “…receive an input including the medical image 102 and to process the input in accordance with current values of segmentation neural network parameters to generate a segmentation map (e.g., the segmentation maps 116, 118, and 120). Each segmentation map characterizes a plausible segmentation of the medical image 102 into multiple different tissue types from a predetermined set of tissue types and other components.”; ¶ [0063] “…Moreover, the volumes of different tissues are determined from the tissue segmentation maps”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Petitjean to incorporate the teachings of De by including calculating an area of the entire biological sample in the taken image in order to standardize delineation of the whole sample/tissue region and compute region sizes.
Regarding claim 4:
Petitjean in view of De discloses the limitations of claim 3 as applied above.
Petitjean further discloses: wherein the evaluation step further includes a third calculation step of calculating an evaluation index on the basis of the area of the entire biological sample and the area of the fibrotic region (¶ [0115] “..As depicted in FIG. 4, some parameters correspond to transformations of one or more other parameters. For example, total collagen area ratio is one parameter listed in FIG. 4, and so is its square root.”; ¶ [0118] “Returning to FIG. 2, at step 224. Also see De ¶¶ [0038] and [0063]).
Regarding claim 6:
Petitjean discloses: an evaluation method for a biological sample (¶ [0058] “FIG. 2 is a high-level flow diagram of a process for quantifying a phenotype of fibrosis in a digital image of a biological tissue sample”) , comprising:
an image acquisition step of imaging the biological sample by optical coherence tomography and acquiring an image (¶ [0106] “At step 220, the server 104 receives a digital image of a biological tissue sample…”; ¶ [0108] “…the image is taken using a tissue imaging method or combination of tissue imaging methods or modalities that can enrich the detection signal of the fibrous tissue, so that the collagen in the resulting image of the tissue sample is more easily detected. Suitable tissue imaging methods include fluorescent imaging, using ex-vivo fresh tissue, performing optical biopsies, in-vivo imaging (such as endoscopic imaging, for example). In general, any digital imaging and optical methods may be used, including stained histopathology slides imaged by Whole Slide Imaging Scanners, two-photon microscopy, fluorescence imaging, structured imaging, polarized imaging, CARS, OCT images”);
a region extraction step to acquire the fibrotic region (¶ [0021] “…an object of the present disclosure to quantify the phenotype of fibrosis in a biological tissue sample that considers multiple, if not all, the degrees of complexity of the collagens and its traits. As used herein, “fibrosis” includes all kinds of collagen-based structures, regardless of the chemical nature of the collagen”; ¶ [0107] “…such pre-processing may include color-based segmentation, thresholding, filtering, enhancement, texture analysis, binarization, edge detection, region analysis, Fourier transformation, object detection, object analysis segmentation, skeletonization, machine learning, deep learning for image processing, 2D and 3D variants of any of these techniques, and any other computational technique that can enrich the extraction of collagens from an image”; ¶ [0109] “…quantify a plurality of parameters, each parameter describing a feature of the collagens in the biological tissue sample that is expected to be different for different phenotypes of fibrosis. The quantitative parameters represent various features of collagens appearing in the digital image, either individually or as a group, and are the same parameters described above, selected from the set of candidate parameters stored in databases 106b-d and described below in detail in relation to FIGS. 3-9.”; ¶ [0082] “…FIGS. 3-9 show exemplary ways to extract various data from digital images taken of biological tissue samples having differing severity of fibrosis as defined by pathologists and widely proven by patient outcomes”) and
an evaluation step of analyzing the fibrotic region, to evaluate a condition of the biological sample (¶ [0118] “Returning to FIG. 2, at step 224, the server 104 combines the plurality of parameters quantified at step 222, to obtain one or more composite scores indicative of a phenotype of fibrosis for the biological tissue sample. The composite score may be derived based on a mathematical transfer function that combines some or all of the quantitative parameters computed at step 222, such as a sum of the selected quantitative parameters, where the sum can be a normalized sum or a weighted sum. The composite score precisely quantifies the fibrosis phenotype (generally along a continuous scale so that it improves upon the coarse, categorical approaches of the prior art by providing wide dynamic range and high resolution), and can be used to describe the state of fibrosis in the biological tissue sample, progression of fibrosis in the sample, or regression of fibrosis in the sample in response to treatment. Derivation of the method to compute the composite score may involve manual and/or automated methods that reduce the dimension of the calibration data set (by identifying candidate parameters that have the best signal-to-noise and are validated by existing models of fibrosis (such as METAVIR), as described below with reference to FIGS. 10-12), identify correlations and/or principal components, or any combination thereof.”.
Petitjean does not specifically teach: a training step of creating a trained model configured to receive an image of the biological sample imaged by optical coherence tomography, as input information, and produce a fibrotic region in which a cell is fibrosed, as output information, by deep learning.
However, De teaches: a training step of creating a trained model configured to receive an image of the biological sample imaged by optical coherence tomography, as input information, and produce a fibrotic region in which a cell is fibrosed, as output information, by deep learning (¶ [0037] “…The medical image 102 can be acquired by a medical imaging scanner 108 of any modality, for example, an optical coherence tomography (OCT) scanner”; ¶ [0038] “…Each segmentation neural network is configured to receive an input including the medical image 102 and to process the input in accordance with current values of segmentation neural network parameters to generate a segmentation map (e.g., the segmentation maps 116, 118, and 120).”; ¶ [0078] “FIG. 3 is a flow diagram of an example process 300 for training a segmentation neural network”; ¶ [0080] “The system obtains one or more training examples, where each training example includes: (i) a training medical image, and (ii) a training segmentation map of the medical image (304)…).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Petitjean to incorporate the teachings of De by including a training step of creating a trained model configured to receive an image of the biological sample imaged by optical coherence tomography, as input information, and produce a fibrotic region in which a cell is fibrosed, as output information, by deep learning in order to standardize delineation of the whole sample/tissue region and compute region sizes using segmentation neural networks.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Petitjean (US 20230019599) in view of Nyberg (US 20120009086).
Regarding claim 5:
Petitjean discloses the limitations of claim 1 as applied above.
Petitjean does not specifically teach: wherein the biological sample is a spheroid obtained by three-dimensional culture of a liver-derived cell including a hepatocyte
However, in a related field, Nyberg teaches: wherein the biological sample is a spheroid obtained by three-dimensional culture of a liver-derived cell including a hepatocyte (¶ [0044] “…a SRBAL provided herein can include a living biological component (e.g., hepatocytes obtained from mammalian livers including human, equine, canine, porcine, bovine, ovine, and murine sources). For example, the living biological component can have porcine hepatocytes in a 3-dimensional tissue construct (i.e., spheroid) as illustrated in FIG. 2.”; ¶ [0118] “Hepatocytes were isolated from pig liver, and spheroids were formed from these cells by rocker technique”).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Petitjean to incorporate the teachings of Nyberg by including wherein the biological sample is a spheroid obtained by three-dimensional culture of a liver-derived cell including a hepatocyte in order to standardize delineation of the whole sample/tissue region and compute region sizes.
Conclusion
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665