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 statement filed 9/18/2024, 12/6/2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 46-89 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claim 1-44 of U.S. Patent No. 10,607,343 and US patent 11,682,195, US 11682195,US 12002262 . The conflicting claims are not identical because patent claim 1 requires the additional elements of “calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches; “not required by claim 46 of the instant application. However, the conflicting claims are not patentably distinct from each other because:
US 11,682,195
US 10,607,343
Instant Application
18/645309
Claim 1 :A computer implemented method of generating at least one shape of a region of interest in a digital image, the method comprising:
Claim 1: A computer implemented method of generating at least one shape of a region of interest in a digital image, the method comprising
Claim 46: A digital image processing method for separating foreground objects from a background scene, the method comprising:
obtaining, by an image processing engine, access to a digital tissue image of a biological sample;
obtaining, via at least one processor, a digital image of a scene;
tiling, by the image processing engine, the digital tissue image into a collection of image patches;
tiling, via the at least one processor, the digital image of the scene into a set of image patches;
identifying, by an image processing engine, a set of target tissue patches from a collection of image patches as a function of pixel content within the collection of image patches, wherein the collection of image patches comprises a tiled digital tissue image of a biological sample; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest,
identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches;
assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest,
assigning, via the at least one processor, each image patch of the set of image patches an initial class probability score generated by a trained foreground object classifier for each image patch;
the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria,
the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria,
generating, via the at least one processor, a first set of patches from the set of image patches and having initial class probability scores satisfying a first criteria;
the first set of tissue region seed patches comprising a subset of the set of target tissue patches;
the first set of tissue region seed patches comprising a subset of the set of target tissue patches;
generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches;
generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches;
generating, via the at least one processor, a second set of patches from the set of image patches and having initial class probability scores satisfying a second criteria;
calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches;
calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches;
calculating, via the at least one processor, a region-of-interest (ROI) score for each patch in the second set of patches as a function of initial class probability scores of neighboring patches of the second set of patches and a distance to patches within the first set of patches;
and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.
and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.
and generating, by the at least one processor, one or more ROI shapes representing foreground objects by grouping neighboring patches based on their ROI scores.
Claims 46 of application ‘309 and claim 1 of patent ‘343,claim 1 of patent '195, claim 1 ‘195, claim 1 of patent ‘262 recite common subject matter;
Whereby claim 46, which recites the open ended transitional phrase “comprising”, does not preclude the additional elements recited by claim 1 of the patent, and
Whereby the elements of claim 46 are fully anticipated by patent claim 1, and anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Allowable Subject Matter
Claims 46-67 are allowed ( note that a terminal disclaimer need to be submitted for the case to be allowable)
Reasons for Indicating Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance: The prior art of record does not teach certain distinguishing features as described below in reference to independent claim 1:
Regarding claim 46 , the prior art Elbaz et al (US 2018/0070905) teaches a system and computation method is disclosed that identifies radiation-induced lung injury after radiation therapy using 4D computed tomography (CT) scans. After deformable image registration, the method segments lung fields, extracts functional and textural features, and classifies lung tissues. The deformable registration locally aligns consecutive phases of the respiratory cycle using gradient descent minimization of the conventional dissimilarity metric. Then an adaptive shape prior, a first-order intensity model, and a second-order lung tissues homogeneity descriptor are integrated to segment the lung fields. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7.sup.th- order contrast-offset-invariant Markov-Gibbs random field (MGRF).Kil et al (US 2004/0093 166) teaches tissue image analysis and region of interest identification for further processing applications such as laser capture microdissection is provided. The invention provides three-stage processing with flexible state transition that allows image recognition to be performed at an appropriate level of abstraction. The three stages include processing at one or more than one of the pixel, sub image and object levels of processing. Also, the invention provides both an interactive mode and a high-throughput batch mode which employs training files generated automatically.
Ross et al (Rich feature hierarchies for accurate object detection and semantic segmentation ) teaches (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost; In order to compute features for a region proposal, we must first convert the image data in that region into a form that is compatible with the CNN (its architecture requires inputs of a fixed 227and 227 pixel size. Kotsiani et al (US 2009/0262993) teaches quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.
CN (105574859) teaches classifying the lesion slice and the normal tissue slice into a positive sample and a negative sample; constructing a multi-level depth convolutional neural network, training a model through a stochastic gradient descent to obtain a network model, and acquiring a coarse segmentation binary image of a tumor and a pixel-classification probability image through a classifier; performing morphological erosion operation on the coarse segmentation binary image of the tumor to obtain a foreground image needed by graph cut, performing subtraction operation on the binary image of a liver and the coarse segmentation binary image of the tumor, and performing the morphological erosion operation to obtain a background image corresponding to normal tissues of the liver; and constructing an undirected graph, and obtaining a finial segmentation region of the tumor through a graph cut optimization algorithm.
None teaches:
calculating, via the at least one processor, a region-of-interest (ROI) score for each patch in the second set of patches as a function of initial class probability scores of neighboring patches of the second set of patches and a distance to patches within the first set of patches; and generating, by the at least one processor, one or more ROI shapes representing foreground objects by grouping neighboring patches based on their ROI scores.
However, none of the reference teaches or fairly suggests the combination of claimed elements. The Examiner finds no reason or motivation to combine the above references in an obviousness rejection thus placing the application in condition for allowance.
Claims 66-67 are allowable by analogy.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NANCY BITAR whose telephone number is (571)270-1041. The examiner can normally be reached on Mon-Friday from 8:00 am to 5:00 p.m..
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mrs. Jennifer Mehmood reached on (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/Primary Examiner, Art Unit 2664
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