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 .
Election/Restrictions
Applicant’s election without traverse of Group 1 in the reply filed on23 January 2026 is acknowledged. Claims 1-11, 17-19, and 22 are hereby examined on the merits.
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.
The factual inquiries 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 1-11, 17-19, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Feldman et al. US Pre-Grant Application Publication US2010/0329529 (hereinafter “Feldman”) in view of Moloney et al. WO2019/226686 (hereinafter “Moloney”).
Regarding claim 1, Feldman teaches a computer-implemented method of training a model to determine a correspondence between a subset of a medical image and a dimensionality-reduced representation of the subset (see paragraphs 0005-0011), the method comprising:
acquiring training image subset data which describes subsets of a training medical image (see paragraph 0008 “the invention provides a method of automatically segmenting a boundary on an T1-w or T2-w MRI image, comprising the steps of: obtaining a training MRI dataset…”);
determining transformation data which describes a transformation between the training image positional reference system in which positions in the medical image are defined and a comparative reference system which is different from the training image reference system (see paragraph 0083, “The ASM is trained in one embodiment of the methods described herein, by identifying 24 user-selected landmarks on 5 T2-MRI images. By using transformations like shear in one embodiment, or rotation, scaling, or translation in other, discrete embodiment, the current shape is deformed, with constraints in place, to best fit the prostate region.”);
determining first distance data based on the training image subset data and the transformation data, wherein the first distance data describes a distance between the subsets in the comparative reference system (paragraphs 0109-0112, “…C-LLE attempts to reconstruct the true low dimensional data manifold by learning pairwise object distance across the entire data space…” );
determining reduced representation data by encoding the training image subset data, wherein the reduced representation data describes a dimensionality-reduced representation of each of the subsets in a dimensionality-reduced reference system which is reduced in dimensionality compared to the dimensionality of the training image reference system (paragraphs 0063-64 and 0085 “…embedding the extracted image feature into a low dimensional space, thereby reducing the dimensionality of the extracted image feature…”);
determining second distance data based on the reduced representation data, wherein second distance data describes a distance between the dimensionality-reduced representations in the dimensionality-reduced reference system (see paragraphs 0116-127);
determining algorithm parameter data which describes parameters of the machine learning model, wherein the algorithm parameter data is determined based on the first distance data and the second distance data, thereby adjusting the parameters to reflect that the lower- dimensional representations correspond to the subsets of the training medical image if the distance between the dimensionality-reduced representations corresponds to the distance between the subsets in the comparative reference system (see paragraphs 0192, and 0206-207).
Feldman is silent on the limitation that the trained model is a machine learning model. However, in a similar field of endeavor, Moloney et al. teaches three-dimensional graphical modeling system using dimensionally reduction techniques (see Moloney paragraphs 0098-99, and 0123). It would have been obvious to one of ordinary skill in the art at the time the invention was filed to substitute Moloney’s machine learning model including dimensionality reduction features into Feldman’s statistical shape model. Doing so would achieve the known and expected uses and benefit of improved object detection and classification.
Regarding claim 2, Feldman teaches the method according to claim 1, wherein the first distance data is determined as a Euclidean distance between the positions of the subsets in the comparative reference system (see paragraph 0109).
Regarding claim 3, Feldman teaches the method according to claim 1, further comprising acquiring atlas data describing a digital model of an anatomical body part, wherein the comparative reference system describes positions in the digital model (see paragraph 0131).
Regarding claim 4, Feldman teaches the method according to claim 1, further comprising: determining feature distribution data describing, for each of the subsets, a distribution of image features of the respective subset onto different classes; determining, based on the feature distribution data, a difference between the distributions; and determining the first distance data based on the difference between the distributions (see paragraphs 0064, and 0078-85).
Regarding claim 5, Feldman teaches the method according to claim 1, wherein the distance described by the first distance data is defined as the difference between the distributions (see paragraphs 0116 and 0122).
Regarding claim 6, Feldman teaches the method according to claim 1, further including acquiring the atlas data, wherein the classes define anatomical structures segmented in the digital model (see paragraph 0131).
Claim 7 has been analyzed and is rejected for the reasons indicated in claim 1 above.
Regarding claim 8, Feldman teaches acquiring medical image data which describes the medical image; and determining image subset data by extracting, from the medical image data, the subsets (see paragraph 0006).
Regarding claim 9, Feldman teaches determining cluster data describing clusters of the lower-dimensional representations of the subsets (see paragraphs 0064, and 0078-85).
Regarding claim 10, Feldman teaches the subsets are extracted from the medical image data by applying at least one of the following criteria: each of the subsets comprises only units of the medical image which have no more than a predetermined distance from each other; no more than a predetermined amount of each of the subsets includes black image content; each of the subsets has a predetermined size; or an overlap of each of the subsets with a spatially neighbouring subset does not exceed a predetermined percentage of the size of each of the subsets (see paragraphs 0035, 0132, and 0192).
Regarding claim 11, Feldman teaches wherein the subsets are extracted from the medical image data by applying a cluster analysis process (see paragraphs 0064, and 0078-85).
Regarding claim 17, Moloney further teaches the method according to claim 1, including that the machine learning model comprises or consists of a convolutional neural network (see Moloney paragraphs 0098-99, and 0123).
Regarding claim 18, Moloney further teaches the method according to claim 1, wherein the parameters define the learnable parameters (see Moloney paragraph 0101).
Regarding claim 19, Moloney further teaches the method according to claim 1, wherein the machine learning model is configured to compute a loss function by applying a triplet network approach to minimize the distance between the dimensionality-reduced representations in the dimensionality-reduced reference system for determining whether the lower-dimensional representations correspond to the subsets of the training medical image if the distance between the dimensionality-reduced representations corresponds to the distance between the subsets in the comparative reference system (see Moloney paragraph 0118).
Claim 21 has been analyzed and is rejected for the reasons indicated in claim 1 above.
Conclusion
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/Stephen R Koziol/ Supervisory Patent Examiner, Art Unit 2665
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