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 § 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.
Claims 15-20 are rejected under 35 U.S.C. 101 because 35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. The four eligible categories of invention include: (1) process which is an act, or a series of acts or steps, (2) machine which is an concrete thing, consisting of parts, or of certain devices and combination of devices, (3) manufacture which is an article produced from raw or prepared materials by giving to these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery, and (4) composition of matter which is all compositions of two or more substances and all composite articles, whether they be the results of chemical union, or of mechanical mixture, or whether they be gases, fluids, powders or solids. MPEP 2106(I).
Claim 15 is rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention because the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106(I). However, claims directed toward a non-transitory computer readable medium may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106(I). Therefore, amending the claims to recite a “non-transitory computer storage medium” would resolve this issue.
Claims 16-20 depend on rejected on claim 15 and therefore, they are rejected as well.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al (US 11,386,553) in view of Takeuchi et al (US 11,263,481).
With respect to claim 1 (exemplary claim), Park discloses A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to (see figure 5):
generate input feature vectors of images of an input medical imaging study;
perform outlier analysis using the input statistical vector and reference statistical vectors associated with reference medical imaging studies;
determine that the input medical imaging study is an outlier with respect to the reference medical imaging studies based on the performed outlier analysis, (see figure 2, non tear “outliers” and “partial tear” and “full tear” as “inliers”, from the segmentation process “feature vector” and the landmark detection process “statistical vector” in figure 3A and 3B, also, see col. 2, lines 24-31, wherein … Optionally, the identification process includes: a segmentation process to determine a mask relating to the one or more target tendons in the medical image data; a landmark detection process to …to locate a bounding box around at least a part of the one or more target tendons…; and this is done using neural networks see col. 3, lines 20-25);
exclude the input medical imaging study from a target plurality of medical imaging studies based on determining that the input medical imaging study is an outlier with respect to the reference medical imaging studies (see col. Lines 18-23, wherein …the region of interest is a region of the image volume represented by the MRI image data where at least a part of the one or more target tendons is present [tis is read as the relevant data]…; and as seen from the figure 2 only the partial tear and full tear data is further analyze i.e. no tear data is excluded i.e. “exclude the input medical imaging study from a target plurality of medical imaging studies based on determining that the input medical imaging study is an outlier”); and
cause a data analysis action to be performed on the target plurality of medical imaging studies, wherein the data analysis action is associated with analysis of a medical imaging study category with which the reference medical imaging studies are associated, (see col. 7, lines 23-45, where “the analysis is associated with analysis of a medical imaging study category with respect to the reference medical imaging studies are associated” various tears are analyzed i.e. diagnosed and the treatments are offered), as claimed.
However, Park fails to explicitly disclose generate an input statistical vector for the input medical imaging study using the generated input feature vectors of the images of the input medical imaging study, as claimed.
Takeuchi teaches generate an input statistical vector for the input medical imaging study using the generated input feature vectors of the images of the input medical imaging study (see col. 1, lines 63-67, wherein …the method comprises processing, by the second trained machine learning model of the processing pipeline, the representative slice to segment the representative slice and generate a statistical measure of a radiodensity metric for each segment in the representative slice…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of medical image processing. The teaching of Takeuchi generating the statistical measure from the image feature data can be incorporated into the Park’s system as suggested (see Park figure 2, 206), for suggestion, and modifying the system yields a predictable result for analyzing the medical images, for motivation.
With respect to claim 2, combination of Park and Takeuchi further discloses wherein the memory and the computer program code are configured to further cause the processor to generate the reference statistical vectors associated with the reference medical imaging studies; wherein generating a reference statistical vector of the reference statistical vectors associated with a reference medical imaging study of the reference medical imaging studies includes: identifying corresponding data entry values in reference feature vectors of images of the reference medical imaging study; calculating statistical values associated with the identified corresponding data entry values; and combining the calculated statistical values to form the reference statistical vector. (see Takeuchi col. 8, lines 44-55, wherein … the segmented anatomical structures, e.g., blood vessels, in the extracted slice, a statistical measure of the HU values (CT numbers) for the various anatomical structures may be determined, e.g., a median HU value may be determined from the HU values, captured and calculated by the CT equipment when generating the medical imaging study…), as claimed.
With respect to claim 3, combination of Park and Takeuchi further discloses wherein the reference medical imaging studies include a first subgroup of the reference medical imaging studies associated with a first category and a second subgroup of the reference medical imaging studies associated with a second category; and wherein performing the outlier analysis using the input statistical vector and the reference statistical vectors includes: performing outlier analysis using the input statistical vector and the reference statistical vectors associated with the first subgroup of the reference medical imaging studies associated with the first category; and performing outlier analysis using the input statistical vector and the reference statistical vectors associated with the second subgroup of the reference medical imaging studies associated with the second category; and wherein determining that the input medical imaging study is an outlier with respect to the reference medical imaging studies based on the performed outlier analysis further includes: determining that the input medical imaging study is an outlier with respect to the first subgroup of reference medical imaging studies associated with the first category; and determining the input medical imaging study is an inlier with respect to the second subgroup of reference medical imaging studies associated with the second category., (see Park figure 2, the No tear is outlier and partial tear and full tear is inlier, this is done using the AI-based system, see col. 10, line 60 to col. 11, line 10, wherein …MRI image data 210 is generated and input into the determination process 220…a classification process for classifying the target tendon 204 with respect to the tendon tear 208 based on the one or more characteristics. In this example, the target tendon 204 is classified as either having no tear (e.g. if tendon tear 208 is not present) “outlier”, having a partial tear or a full tear, “inlier” as illustrated in FIG. 2. …The classification process 220 may be performed using an AI-based system, such as a neural network…), as claimed.
With respect to claim 4, combination of Park and Takeuchi further discloses wherein the input medical imaging study is labeled as being associated with the second category and not associated with the first category; and wherein the input medical imaging study is added to the second subgroup of reference medical imaging studies associated with the second category, (see Park figure 2, numerical 220, the images are either No tear or partial or full tear [this is read as first or second category]), as claimed.
With respect to claim 5, combination of Park and Takeuchi further discloses wherein the memory and the computer program code are configured to further cause the processor to generate reference feature vectors of a plurality of images in the reference medical imaging studies, wherein generating the reference feature vectors includes providing the plurality of images to a trained vision model as input and receiving the generated reference feature vectors as output from the trained vision model., (see Park col. 12, lines 21-30, wherein …neural network 402 may implement the described DL-based segmentation process using the encoding and decoding mechanism. The first set of training data may include given input MRI images of the shoulder region of the body where the target tendon 204 is present, and a first set of ground truth output images in which the target feature mask 302 is already indicated or labelled), as claimed.
With respect to claim 6, combination of Park and Takeuchi further discloses wherein excluding the input medical imaging study from a target plurality of medical imaging studies includes removing the input medical imaging study from a training data set used to train an image classification model; and wherein causing a data analysis action to be performed on the target plurality of medical imaging studies includes training the image classification model using the training data set from which the input medical imaging study was removed, (see Park col. 11, line 62 to col. 12, line 12, wherein …The data processing system may include one or more AI-based sub-systems for performing the above described processes. …a first set of training data, the first set of training data including a set of ground truth output images in which information relating to the region of interest 206 is indicated. The second neural network 404 may be trained using a second set of training data, the second set of training data including a set of ground truth output images in which information relating to the one or more characteristics of the one or more abnormalities is indicated), as claimed.
With respect to claim 7, combination of Park and Takeuchi further discloses wherein the input medical imaging study includes a medical imaging series associated with at least one of X-ray imaging, computed tomography (CT) imaging, magnetic resonance imaging (MRI), Ultrasound imaging, and positron emission tomography (PET) imaging. (see Park col. 8, lines 61-63, MRI image data), as claimed.
Claims 8-14 and 15-20 are rejected for the same reasons as set forth for the rejections for claims 1-7, because claims 8-14 and 15-20 are claiming subject matter of similar scope as claimed in claims 1-7.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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