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 .
Response to Amendment
This Office Action is responsive to Applicant’s remarks received on December 11, 2025. Claims 1, 4-10, 13-15 and 17-20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claim(s) 1,4, 9, 10, 13, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Abedini et al. (US 2017/0116744) and Yoo et al. (“Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images”).
Regarding claim 1, Abedini et al. discloses a computer-implemented method comprising:
receiving an input medical image patch depicting one or more lesions (“The process starts in step 302 and immediately proceeds to step 304 in which a new image is loaded” at paragraph 0038, line 2);
segmenting the one or more lesions from the input medical image patch using a plurality of segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of segmentation networks trained to segment lesions from patches with a different field of view size (“As described above for FIG. 2, there are five different sub-sections paths for N/4, N/2, N, 2*N, 4*N, N is a positive integer, correspond to patch sizes λ 0.4×, 0.8×, lx, 2×, 4×. Selecting the path for N/4, the process continues to step 310 in which and for patch size N/4 the sub-sections are generated. Next in step 312, a trained classifier is applied to each image sub-section for the discrete number of image sub-sections, the trained classifier representing a probability that the image sub-section is the boundary type (ρ.sub.λ←ApplyClassifier(Φ,λ)). In step 314, probability label map is generated” at paragraph 0038, line 4);
generating a final segmentation mask of the one or more lesions (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section.outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence) by:
combining intensity values of corresponding voxels in the plurality of initial segmentation masks into respective scores (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section.outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence; looking at Figure 5B as an example of the final probability map, it is shown that it is a merging via averaging of the pixel values from each classifier’s probability maps), and
generating the final segmentation mask based on the scores (“Then probability map is overlayed, using a weighted average, on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0036, last sentence; the contribution of each probability map’s pixel value corresponds to a probability score); and
outputting the final segmentation mask of the one or more lesions (“In response to the overlaid trained classifier being above a settable threshold, identifying the image sub-section as a border segment in step 362” at paragraph 0039, line 1).
Abedini et al. does not explicitly disclose using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks.
Yoo et al. teaches a method in the same field of endeavor of medical lesion segmentation, comprising:
receiving an input medical image depicting one or more lesions (“Therefore, in this study, we used post-contrast 3D T1-weighted image volumes only.” At section 2, line 6);
segmenting the one or more lesions from the input medical image patch using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of machine learning based segmentation networks trained to segment lesions from patches with a different field of view size (“To detect and segment GTVof brain metastasis from 3D post-Gd T1-weighted images, we evaluated encoder–decoder-based CNN architectures, which are standard deep learning approaches for medical image segmentation.19,20 We first compared the effectiveness of 2.5D and 3D encoder–decoder-based CNNs. The 2.5D CNN approach aims to learn high-resolution in-plane image features by taking five axial slices as input to the network. The 3D CNN approach tends to efficiently model 3D contexts with sampled patches” at section 3, line 1);
generating a final segmentation mask of the one or more lesions based on the plurality of initial segmentation masks (“we applied ensemble learning with random forests26 as the weak learners to effectively fuse the 2.5D and 3D predictions” at section 3.3, line 3); and
outputting the final segmentation mask of the one or more lesions (see figure 4, where the final voting produces the highlighted lesion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize multiple machine learners as taught by Yoo et al. as the classifiers of Abedini et al. to leverage benefits of each type of machine learner to produce a balanced and precise prediction (see Yoo et al. at section 5, paragraph 2).
Regarding claim 10, Abedini et al. discloses an apparatus comprising:
means for receiving an input medical image patch depicting one or more lesions (“The process starts in step 302 and immediately proceeds to step 304 in which a new image is loaded” at paragraph 0038, line 2);
means for segmenting the one or more lesions from the input medical image patch using a plurality of segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of segmentation networks trained to segment lesions from patches with a different field of view size (“As described above for FIG. 2, there are five different sub-sections paths for N/4, N/2, N, 2*N, 4*N, N is a positive integer, correspond to patch sizes λ 0.4×, 0.8×, lx, 2×, 4×. Selecting the path for N/4, the process continues to step 310 in which and for patch size N/4 the sub-sections are generated. Next in step 312, a trained classifier is applied to each image sub-section for the discrete number of image sub-sections, the trained classifier representing a probability that the image sub-section is the boundary type (ρ.sub.λ←ApplyClassifier(Φ,λ)). In step 314, probability label map is generated” at paragraph 0038, line 4);
means for generating a final segmentation mask of the one or more lesions (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section.outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence) by:
combining intensity values of corresponding voxels in the plurality of initial segmentation masks into respective scores (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section.outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence; looking at Figure 5B as an example of the final probability map, it is shown that it is a merging via averaging of the pixel values from each classifier’s probability maps), and
generating the final segmentation mask based on the scores (“Then probability map is overlayed, using a weighted average, on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0036, last sentence; the contribution of each probability map’s pixel value corresponds to a probability score); and
means for outputting the final segmentation mask of the one or more lesions (“In response to the overlaid trained classifier being above a settable threshold, identifying the image sub-section as a border segment in step 362” at paragraph 0039, line 1).
Abedini et al. does not explicitly disclose using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks.
Yoo et al. teaches an apparatus in the same field of endeavor of medical lesion segmentation, comprising:
means for receiving an input medical image depicting one or more lesions (“Therefore, in this study, we used post-contrast 3D T1-weighted image volumes only.” At section 2, line 6);
means for segmenting the one or more lesions from the input medical image patch using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of machine learning based segmentation networks trained to segment lesions from patches with a different field of view size (“To detect and segment GTVof brain metastasis from 3D post-Gd T1-weighted images, we evaluated encoder–decoder-based CNN architectures, which are standard deep learning approaches for medical image segmentation.19,20 We first compared the effectiveness of 2.5D and 3D encoder–decoder-based CNNs. The 2.5D CNN approach aims to learn high-resolution in-plane image features by taking five axial slices as input to the network. The 3D CNN approach tends to efficiently model 3D contexts with sampled patches” at section 3, line 1);
means for generating a final segmentation mask of the one or more lesions based on the plurality of initial segmentation masks (“we applied ensemble learning with random forests26 as the weak learners to effectively fuse the 2.5D and 3D predictions” at section 3.3, line 3); and
means for outputting the final segmentation mask of the one or more lesions (see figure 4, where the final voting produces the highlighted lesion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize multiple machine learners as taught by Yoo et al. as the classifiers of Abedini et al. to leverage benefits of each type of machine learner to produce a balanced and precise prediction (see Yoo et al. at section 5, paragraph 2).
Regarding claim 15, Abedini et al. discloses a non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving an input medical image patch depicting one or more lesions (“The process starts in step 302 and immediately proceeds to step 304 in which a new image is loaded” at paragraph 0038, line 2);
segmenting the one or more lesions from the input medical image patch using a plurality of segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of segmentation networks trained to segment lesions from patches with a different field of view size (“As described above for FIG. 2, there are five different sub-sections paths for N/4, N/2, N, 2*N, 4*N, N is a positive integer, correspond to patch sizes λ 0.4×, 0.8×, lx, 2×, 4×. Selecting the path for N/4, the process continues to step 310 in which and for patch size N/4 the sub-sections are generated. Next in step 312, a trained classifier is applied to each image sub-section for the discrete number of image sub-sections, the trained classifier representing a probability that the image sub-section is the boundary type (ρ.sub.λ←ApplyClassifier(Φ,λ)). In step 314, probability label map is generated” at paragraph 0038, line 4);
generating a final segmentation mask of the one or more lesions (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section. outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence) by:
combining intensity values of corresponding voxels in the plurality of initial segmentation masks into respective scores (“All the probability label maps are merged in step 360. Or stated differently, the classifiers are outputted and a probability map generated. Then probability map is overlayed on other probability maps generated from other classifiers containing the image sub-section.outputting of classifiers and generating a probability map then overlaying the probability map on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0038, third to last sentence; looking at Figure 5B as an example of the final probability map, it is shown that it is a merging via averaging of the pixel values from each classifier’s probability maps), and
generating the final segmentation mask based on the scores (“Then probability map is overlayed, using a weighted average, on other probability maps generated from other classifiers containing the image sub-section” at paragraph 0036, last sentence; the contribution of each probability map’s pixel value corresponds to a probability score); and
outputting the final segmentation mask of the one or more lesions (“In response to the overlaid trained classifier being above a settable threshold, identifying the image sub-section as a border segment in step 362” at paragraph 0039, line 1).
Abedini et al. does not explicitly disclose using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks.
Yoo et al. teaches a method in the same field of endeavor of medical lesion segmentation, comprising:
receiving an input medical image depicting one or more lesions (“Therefore, in this study, we used post-contrast 3D T1-weighted image volumes only.” At section 2, line 6);
segmenting the one or more lesions from the input medical image patch using a plurality of machine learning based segmentation networks to respectively generate a plurality of initial segmentation masks, each of the plurality of machine learning based segmentation networks trained to segment lesions from patches with a different field of view size (“To detect and segment GTVof brain metastasis from 3D post-Gd T1-weighted images, we evaluated encoder–decoder-based CNN architectures, which are standard deep learning approaches for medical image segmentation.19,20 We first compared the effectiveness of 2.5D and 3D encoder–decoder-based CNNs. The 2.5D CNN approach aims to learn high-resolution in-plane image features by taking five axial slices as input to the network. The 3D CNN approach tends to efficiently model 3D contexts with sampled patches” at section 3, line 1); and
outputting the final segmentation mask of the one or more lesions (see figure 4, where the final voting produces the highlighted lesion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize multiple machine learners as taught by Yoo et al. as the classifiers of Abedini et al. to leverage benefits of each type of machine learner to produce a balanced and precise prediction (see Yoo et al. at section 5, paragraph 2).
Regarding claims 4 and 13, the Abedini et al. and Yoo et al. combination discloses a method and apparatus wherein the means for generating the final segmentation mask based on the scores comprises:
means for generating the final segmentation mask based on the scores using a machine learning based fusion network (“To improve both sensitivity and specificity, we applied ensemble learning with random forests26 as the weak learners to effectively fuse the 2.5D and 3D predictions, similar to fusing multi-scale features by boosting trees.27 The weak learner fusion procedure is depicted in Fig. 4” Yoo et al. at section 3.3, line 3).
Regarding claims 9 and 20, the Abedini et al. and Yoo et al. combination discloses a method and medium as described above.
The Abedini et al. and Yoo et al. combination does not explicitly disclose that the one or more lesions comprise one or more brain lesions located on a brain of a patient.
However, Yoo et al. further teaches one or more lesions comprise one or more brain lesions located on a brain of a patient (“The data cohort consisted of 480 MRI scans from 442 patients who had brain metastases” at section 2, line 1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to employ the system of the Abedini et al. and Yoo et al. combination to detect brain lesions as Abedini et al. suggests application to lesion images other than skin lesions (“The present invention improves border detection in images, especially borders in medical images such as, but not limited to, dermoscopy images” at paragraph 0019, line 1).
Claim(s) 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Abedini et al. and Yoo et al. as applied to claims 1 and 10 above, and further in view of Dey et al. (“HYBRID CASCADED NEURAL NETWORK FOR LIVER LESION SEGMENTATION”).
The Abedini et al. and Yoo et al. combination discloses a method and apparatus wherein the plurality of machine learning based segmentation networks are trained by extracting training patches from training medical images depicting at least one lesion, the training patches extracted from the training medical images (“FIG. 2 is a workflow diagram 200 illustrating a training phase for segmenting an image using the system of FIG. 1. Shown are five different training sub-sections paths for N/4, N/2, N, 2*N, 4*N. N is a discrete positive integer, correspond to patch sizes 0.4×, 0.8×, lx, 2×, 4×. Examples of different patch sizes are shown in FIG. 4A-4E” Abedini et al. at paragraph 0033, line 1).
The Abedini et al. and Yoo et al. combination does not explicitly disclose that the training patches extracted from the training medical images based on a size of the at least one lesion.
Dey et al. teaches a method and apparatus in the same field of endeavor of lesion segmentation, wherein the plurality of machine learning based segmentation networks are trained by extracting training patches from training medical images depicting at least one lesion, the training patches extracted from the training medical images based on a size of the at least one lesion (“Since this network takes care of the prediction of large lesions, we clean the training masks by removing all small lesion masks, which is implemented using the connected component labeling algorithm provided by the OpenCV library [16]. The horizontal and vertical dimensions of the removed small lesions are both less than or equal to 32 on a 2D slice in the axial orientation. In the test phase, we also remove the detected lesions less than 32 x 32 using component labeling” at section 2.3, Large Lesion Segmentation, line 4; “We use a 3D CompNet, as shown in Fig. 4, to segment the small lesions. This 3D network takes a volume of 32 x 32 x 32 as input” at section 2.3, Small Lesion Segmentation, line 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize size considerations as taught by Dey et al. for the classifiers of the Abedini et al. and Yoo et al. combination to reduce false positive detections (Dey et al. at section 2.3, paragraph 1).
Claim(s) 6-8 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Abedini et al. and Yoo et al. as applied to claims 1 and 15 above, and further in view of Hu et al. (“Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery”).
Regarding claims 6 and 17, the Abedini et al. and Yoo et al. combination discloses a method and medium as described in claims 1 and 15 above.
The Abedini et al. and Yoo et al. combination does not explicitly disclose that at least some of the plurality of machine learning based segmentation networks are trained with different loss functions.
Hu et al. teaches a method and medium in the same field of endeavor of lesion segmentation wherein at least some of the plurality of machine learning based segmentation networks are trained with different loss functions (“Furthermore, U-Net was trained to optimize DSC and DeepMedic was set to minimize cross-entropy loss” at page 64, Ensemble Model, line 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to employ different loss conditions as taught by Hu et al. in the classifiers of Abedini et al. and Yoo et al. combination to “maximize the capability of the ensemble model” (Hu et al. at page 64, Ensemble Model, line 2).
Regarding claims 7 and 18, the Abedini et al. and Yoo et al. combination discloses a method and medium as described in claims 1 and 15 above.
The Abedini et al. and Yoo et al. combination does not explicitly disclose that at least some of the plurality of machine learning based segmentation networks are implemented with different network architectures.
Hu et al. teaches a method and medium in the same field of endeavor of lesion segmentation wherein at least some of the plurality of machine learning based segmentation networks are implemented with different network architectures (“3D U-Net and DeepMedic were trained separately with different hyperparameters and different objective functions” at page 64, Ensemble Model, line 1; see also Figure 1 the shows the U-net and DeepMedic have different architectures).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to employ different architectures as taught by Hu et al. in the classifiers of Abedini et al. and Yoo et al. combination to “maximize the capability of the ensemble model” (Hu et al. at page 64, Ensemble Model, line 2).
Regarding claims 8 and 19, the Abedini et al. and Yoo et al. combination discloses a method and medium as described in claims 1 and 15 above.
The Abedini et al. and Yoo et al. combination does not explicitly disclose that at least some of the plurality of machine learning based segmentation networks are implemented with different hyperparameters.
Hu et al. teaches a method and medium in the same field of endeavor of lesion segmentation wherein at least some of the plurality of machine learning based segmentation networks are implemented with different hyperparameters (“3D U-Net and DeepMedic were trained separately with different hyperparameters and different objective functions” at page 64, Ensemble Model, line 1; see also Figure 1 the shows the U-net and DeepMedic have different architectures).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to employ different hyperparameters as taught by Hu et al. in the classifiers of Abedini et al. and Yoo et al. combination to “maximize the capability of the ensemble model” (Hu et al. at page 64, Ensemble Model, line 2).
Response to Arguments
Summary of Remarks (@ response pages labeled 7-8): “While the cited portions of Abedini may refer to merging probability maps, the cited portions of Abedini do not teach or suggest at least that the probability maps are merged by combining intensity values of corresponding voxels in the probability maps. That is, the cited portions of Abedini do not teach or suggest at least "combining intensity values of corresponding voxels in the plurality of initial segmentation masks into respective scores, and generating the final segmentation mask based on the scores" as recited in amended claim 1. Instead, the cited portions of Abedini merely overlay the probability maps. Paragraph [0038] of Abedini explains this as follows: "probability map is overlayed on other probability maps generated from other classifiers." However, merely overlaying probability maps, as described in the cited portions of Abedini, does not teach or suggest at least "combining intensity values of corresponding voxels in the plurality of initial segmentation masks into respective scores" as recited in amended claim 1.”
Examiner’s Response: As explained above, the overlaying corresponds to a weighted averaging of the individual probability maps together. The contribution of each probability map’s pixel value corresponds to a probability score, which is combined via the aforementioned weighted averaging to arrive at the final combined probability map.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATRINA R FUJITA whose telephone number is (571)270-1574. The examiner can normally be reached Monday - Friday 9:30-5:30 pm ET.
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/KATRINA R FUJITA/Primary Examiner, Art Unit 2672
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