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 .
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of EP21382787.6, filed on 08/30/2021.
Preliminary Amendment
Applicant submitted a preliminary amendment on 02/28/2024. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. Claim 12 is non-statutory under the most recent interpretation of the Interim Guidelines regarding 35 U.S. C.101 because: the A computer program product comprising claimed is not positively disclosed in the specification as a statutory only embodiment and is not limited to non-transitory media. The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signa Is per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) transitory embodiments are not directed to statutory subject matter and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p. 2. To overcome this rejection, the claim may be amended to recite "A non-transitory computer readable medium comprising program instructions, the program instructions executable by at least one hardware processor"
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over (Dmitriev et al.: "Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble", 4 September 2017, Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August) hereinafter referred to as (Dmitriev) in view of Hashimoto (U.S. Patent Pub. No. 2018/0293729).
Regarding Claim 1, Dmitriev teaches a computer implemented method for the automatic classification of pancreatic cysts using computed tomography (CT), images, the method comprising (Abstract Line 2-5: This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst:)
accessing, by a computer, a set of CT images of a patient, each image of the set of CT images representing a different slice (Section 2: The dataset in this study contains 134 abdominal contrast-enhanced CT scans collected with a Siemens SOMATOM scanner (Siemens Medical Solutions, Malvern, PA). The dataset consists of the four most common pancreatic cysts: 74 cases of IPMNs, 14 cases of MCNs, 29 cases of SCAs, and 17 cases of SPNs. All CT images have 0.75 mm slice thickness. The ages of the subjects (43 males, 91 females) range from 19 to 89 years (mean age 59.9 ± 17.4 years);)
performing, by the computer, a filtering operation on the set of CT images, and defining a region of interest (ROI), candidate to contain a pancreatic cyst within the filtered set of CT images (Section 2: One of the most critical parts in the computer-aided cyst analysis is segmentation. The effectiveness and the robustness of the ensuing classification algorithm depend on the precision of the segmentation outlines (ROI). The outlines of each cyst (if multiple) within the pancreas were obtained by a semi-automated graph-based segmentation technique [3] (Fig. 1), and were confirmed by an experienced radiologist (E.F.). The histopathological diagnosis for each subject was confirmed by a pancreatic pathologist (R.H.H.) based on the subsequently resected specimen;)
performing, by the computer, a segmentation operation of the defined ROI using a neural network, obtaining a segmented image as a result, the segmented image representing values of a first value for pixels occupied by a pancreatic cyst and values of at least a second value for pixels not occupied by a pancreatic cyst (Section 2: One of the most critical parts in the computer-aided cyst analysis is segmentation. The effectiveness and the robustness of the ensuing classification algorithm depend on the precision of the segmentation outlines (ROI). The outlines of each cyst (if multiple) within the pancreas were obtained by a semi-automated graph-based segmentation technique [3] (Fig. 1), and were confirmed by an experienced radiologist (E.F.). The histopathological diagnosis for each subject was confirmed by a pancreatic pathologist (R.H.H.) based on the subsequently resected specimen;)
performing, by the computer, a morphological analysis of the pancreatic cyst using an image processing algorithm that:
computes a value of eccentricity and of convexity of the pancreatic cyst by processing the pixels with the first value, and (Section 3.1: The most common features mentioned in the medical literature that are used for initial pancreatic cyst differentiation involve gender and age of the subject, as well as location, shape and general appearance of the cyst [9]. In this paper, we define a set 𝒬 of 14 quantitative features to describe particular cases by: (1) age a ∈ 𝒬 and gender g ∈ 𝒬 of the patient, (2) cyst location l ∈ 𝒬, (3) intensity ℐ ∈ 𝒬and (4) shape 𝒮 ∈ 𝒬 features of a cyst. The importance and discriminative power of these features are described below… Shape features. Pancreatic cysts also demonstrate differences in shape depending on the category. Specifically, cysts can be grouped into three categories: smoothly shaped, lobulated and pleomorphic cysts [1]. To capture different characteristics of the shape of a cyst, we use volume V ∈ 𝒮, surface area SA ∈ 𝒮, surface area-to-volume ratio SA/V ∈ 𝒮, rectangularity r ∈ 𝒮, convexity c ∈ 𝒮 and eccentricity e ∈ 𝒮 features summarized in [11].)
classifying, by the computer, the pancreatic cyst based on the morphological analysis performed in the previous step and demographic data of the patient, the demographic data, at least, including the gender and age of the patient (Section 3.1: Following feature extraction, we use an RF classifier to perform the classification of a feature vector qm computed for an unseen cyst sample xm. RF-based classifiers have shown excellent performance in various classification tasks, including numerous medical applications, having high accuracy of prediction and computation efficiency.)
Dmitriev does not explicitly disclose computes a value of a relative position of the pancreatic cyst by dividing the pixels with the second value in three thirds in a 3D space and by checking a 3D position of the pancreatic cyst within the divided three thirds.
Hashimoto is in the same field of art of image analysis. Further, Hashimoto teaches computes a value of a relative position of the pancreatic cyst by dividing the pixels with the second value in three thirds in a 3D space and by checking a 3D position of the pancreatic cyst within the divided three thirds (¶46 as shown in FIG. 4, with respect to a pixel P that is common to three sectional images S1, S2, and S3, results of the primary classification process with respect to the sectional images S1, S2, and S3 are respectively acquired, the three results of the primary classification process are evaluated, and the type of a tissue or a lesion to which the pixel P belongs is specified; ¶65 in a case where sectional images S4, S5, and S6 for which the secondary classification process is performed are disposed in parallel on a three-dimensional space as shown in FIG. 7, and for example, in a case where an abnormality in continuity of centroid positions of respective regions r4, r5, and r6 classified as cysts in the respective sectional images S4, S5, and S6 is detected, the above-described correction may be performed with respect to the regions classified as the cysts in the respective sectional images S4, S5, and S6.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dmitriev by determining the 3d position of the cyst within the divided thirds of the pixels that is taught by Hashimoto; thus, one of ordinary skilled in the art would be motivated to combine the references to classify tissues or lesions with high accuracy (Hashimoto ¶7).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Dmitriev in view of Hashimoto discloses the method of claim 1, wherein the demographic data of the patient further includes at least one of an ethnic group, a medical history, and a medication intake of the patient (Dmitriev, Section 1: A combination of CT imaging findings in addition to general demographic characteristics, such as patient age and gender, are used to discriminate different types of pancreatic cysts [5]; Ref [5] discusses medical history affecting patients.)
Regarding Claim 3, Dmitriev in view of Hashimoto discloses the method of claim 1, wherein the segmented image represents three different values: a first value for the pixels occupied by the pancreatic cyst, the second value for the pixels occupied by the pancreas and a third value for the rest of the pixels (Dmitriev Fig. 1 shows the outline of the cyst in the pancreas. Further outlining or defining the whole pancreas is an ordinary design choice.)
Regarding Claim 4, Dmitriev in view of Hashimoto discloses the method of claim 1, wherein the value of eccentricity of the pancreatic cyst is computed by calculating the major and minor axes from a middle section of the pancreatic cyst (Dmitriev Section 3.1 eccentricity e ∈ 𝒮 features summarized in [11]; [11] teaches this.)
Regarding Claim 6, Dmitriev in view of Hashimoto discloses the method of claim 1, wherein the morphological analysis of the pancreatic cyst further comprises computing one or more of the following features of the pancreatic cysts: if the cyst is multicystic, if it has calcifications and their location, if it has air balls and their location, if there are scars inside the cyst, if the cyst is flat, lobulated, circular or ovoid, an aspect of the cysts and if the cyst accesses a pancreatic duct or not (Dmitriev Section 3.2: As described in Sect. 4, RF trained on the proposed quantitative features can be used for cyst classification with reasonably high accuracy. However, despite high generalization potential, the proposed features do not take full advantage of the image information. In particular, due to variations in the internal structure of pancreatic cysts, they show different image characteristics: SCA often has a honeycomb-like appearance with a central scar or septation, MCN demonstrates a “cyst within cyst” appearance with peripheral calcification, IPMN tends to have a “cluster of grapes” appearance, and SPN typically consists of solid and cystic components [12].)
Regarding claim 12, claim 12 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Hashimoto further teaching on: A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by at least one hardware processor (Hashimoto, Claim 11: A non-transitory computer-readable storage medium storing therein an image processing program that causes a computer to function)
Claim 13 recites limitations similar to claim 3 and is rejected under the same rationale and reasoning.
Claim 14 recites limitations similar to claim 6 and is rejected under the same rationale and reasoning.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over (Dmitriev) in view of Hashimoto (U.S. Patent Pub. No. 2018/0293729) in view of Akselrod (U.S. Patent No. 6858007).
Regarding Claim 5, Dmitriev in view of Hashimoto teaches the method of claim 1.
Dmitriev in view of Hashimoto does not explicitly disclose wherein the value of convexity of the pancreatic cyst is computed as a proportion between a volume of the pancreatic cyst and a volume of a respective convex hull.
Akselrod is in the same field of art of image analysis. Further, Akselrod teaches wherein the value of convexity of the pancreatic cyst is computed as a proportion between a volume of the pancreatic cyst and a volume of a respective convex hull (Col 19 Lines 4-14: Structure separation begins by computing the convex hull of the cyst, which is the smallest polygon circumscribing the cyst (see FIGS. 8a-b). Example 3 below provides a mathematical background about the convex hull, and presents a simple method for computing it. Subtracting the original object from its convex hull provides the convex deficiency of the cyst. The convex deficiency of the cyst is composed of several separated regions, each associated with a single cystic structure (e.g., the gray regions in FIG. 8a). This description is also valid for images containing two cystic regions (see FIG. 8b).)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dmitriev in view of Hashimoto by computing the convexity that is taught by Akselrod; thus, one of ordinary skilled in the art would be motivated to combine the references to efficiently classify masses (Akselrod Background).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over (Dmitriev) in view of Hashimoto (U.S. Patent Pub. No. 2018/0293729) in view of Ionita (U.S. Patent Pub. No. 2023/0000364).
Regarding Claim 7, Dmitriev in view of Hashimoto teaches the method of claim 1.
Dmitriev in view of Hashimoto does not explicitly disclose wherein the filtering operation comprises a median filter.
Ionita is in the same field of art of image analysis. Further, Ionita teaches wherein the filtering operation comprises a median filter (¶105 The averaged frame was passed through a two-dimensional median filter sized 3×3 pixels to attenuate background structure caused by motion artifacts.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dmitriev in view of Hashimoto by using a median filter that is taught by Ionita; thus, one of ordinary skilled in the art would be motivated to combine the references to more accurately predict an outcome for the patient (Ionita ¶5).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 8, Dmitriev in view of Hashimoto in view of Ionita discloses the method of claim 7, wherein the median filter comprises a 3 x 3 filter window (Ionita, ¶105 The averaged frame was passed through a two-dimensional median filter sized 3×3 pixels to attenuate background structure caused by motion artifacts.)
Regarding Claim 9, Dmitriev in view of Hashimoto in view of Ionita discloses the method of claim 1, wherein the neural network comprises a semantic segmentation algorithm (Ionita, ¶102 Further improvements that have increased the resolution to the individual pixel scale have opened the door to a pixel-by-pixel classification, essentially carrying out a semantic segmentation process with the network.)
Regarding Claim 10, Dmitriev in view of Hashimoto in view of Ionita discloses the method of claim 9, wherein the neural network is based on deep learning, U-Net (Ionita, ¶73 For the proof of the concept, a deep neural network based on U-Net Architecture; This is also well known in the art.)
Claims 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over (Dmitriev) in view of Hashimoto (U.S. Patent Pub. No. 2018/0293729) in view of Craik (U.S. Patent Pub. No. 2022/0381784).
Regarding Claim 11, Dmitriev in view of Hashimoto teaches the method of claim 1.
Dmitriev in view of Hashimoto does not explicitly disclose wherein the classifying step comprises classifying the pancreatic cyst in four different groups, namely: Intraductal papillary mucinous neoplasia(IPMN); Mucinous cystic neoplasms(MCN); Serous cystadenoma(SCA); and Pseudocysts(PCYS).
Craik is in the same field of art of image analysis. Further, Craik teaches wherein the classifying step comprises classifying the pancreatic cyst in four different groups, namely: Intraductal papillary mucinous neoplasia(IPMN); Mucinous cystic neoplasms(MCN); Serous cystadenoma(SCA); and Pseudocysts(PCYS) (¶4 Mucinous cysts, which include intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), are precursor lesions to pancreatic cancer and should be resected if they harbor high-grade dysplasia or invasive cancer (HGD/IC). However, a significant portion of mucinous cysts only contain low-grade dysplasia (LGD). These lesions are generally considered benign and it is currently recommended that they be followed through surveillance for malignant progression. Serous cystadenomas (SCAs) and pancreatic pseudocysts, which are both types of nonmucinous cysts, are also common and present minimal risk to patients if they remain asymptomatic; This shows these are common categories for cysts.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Dmitriev in view of Hashimoto by categorizing the cyst into well known groups that is taught by Craik; thus, one of ordinary skilled in the art would be motivated to combine the references in order to diagnose a patent properly (Craik ¶10-12).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 15 recites limitations similar to claim 11 and is rejected under the same rationale and reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (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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/DUSTIN BILODEAU/Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664