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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 7, 10, 15, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 7, and 15 recite “optionally” and is not definite whether the clause following “optionally” is required. For a purpose of examination, the examiner would interpret the clause following “optionally” to be not required.
Claim 10 recites “a 3D BTM representation” and is not definite whether it is new and distinct from recited “3D BTM representation” in claim 2 or meant to refer to same 3D BTM representation in claim 2.
Claim 15 recites the limitation "the reference frame, the registered frame" in page 2. There is insufficient antecedent basis for this limitation in the claim, since “A reference frame” and “a registered frame” were not previously recited in claims 1 and 15.
Claim 19 recites “An ultrasound image-guided system,” and is not definite as claim 1 already recites “an ultrasound image-guided system” and is not clear whether it is new an distinct from claimed ultrasound image-guided system or meant to recite same system.
Allowable Subject Matter
Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome current 112 rejection set forth above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12 and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Zohar et al.,” US 11,123,046 (hereinafter Zohar).
Regarding to claim 1, Zohar teaches an ultrasound module in an ultrasound image-guided system for thermal ablation, the ultrasound module configured to receive at least one B-mode ultrasound (US) tissue image during the thermal ablation (ultrasound image guided system with thermal ablation unit, B-mode image Col. 17 lines 20-24), and to derive therefrom a biotrace map (BTM) (Figure 11C shows real time ablation zone morphology as “BioTrace”) that provides a tissue damage assessment (Figure 9A, B-mode images with a border indicating the thermally damaged tissue updated with proceeding thermal ablation procedures, Col. 17 lines 18-48) - by applying at least one deep neural network (DNN) to segment tissue damage in the at least one received tissue image to yield the BTM (estimating damaged tissue using image, with machine learning such as deep learning procedure applied to detecting shadowing, segmentation of identified tissues, Col. 12 lines 31-54; Figure 7C shows ultrasound image with demarcated damaged tissue regions, indication of outlining of the damaged region, Col. 14 lines 1-44).
Regarding to claim 2, Zohar teaches all limitations of claim 1 as discussed above.
Zohar further discloses following limitations wherein the at least one B-mode US tissue image comprises a plurality of two-dimensional (2D) US images and wherein the ultrasound module is further configured to aggregate a plurality of the 2D US images to generate a three-dimensional (3D) BTM representation (two planes used to derive an at least partly 3D US image, Col. 8 lines 17-44).
Regarding to claims 3-10, Zohar teaches all limitations of claim 2 as discussed above.
Zohar further discloses following limitations:
Of claim 3, further configured to derive at least one virtual 2D section through the 3D BTM representation according to a user indication, to optimize target visibility and damage estimation in addition to the received at least one B-mode US image (3D damage rendering of tissue may be projected on the 2D ultrasound image using 3D representation or allowing the user to change the plane of the 2D image, Col. 10 line 63-Col. 11 line 17).
Of claim 4, further configured to detect or receive a position of an ablation needle and indicate the needle position in the 3D BTM representation, and optionally configured to update the 3D BTM representation with derived data concerning heat propagation with respect to the needle position (spatial configuration, position and orientation for ablation tool, Col. 15 lines 15-28, Figure 9B shows position of the ablation tool in 3D representation, Col. 17 line 62- Col. 18 line 11).
Of claim 5, further configured to derive a 3D reference model prior to the ablation (preparatory image, 3D anatomical model Figure 9B, Col. 17 line 62-Col. 18 line 11), from a US sweep of a target tissue and an aggregation of multiple 2D US images from the US sweep (planning simulation on US images would require US sweep of a target as claimed, Col. 17 line 62-Col. 18 line 11); wherein the 3D reference model is further used as baseline for the generation of the 3D BTM (correlating demarcated blood vessels in preparatory image, Col. 18 lines 26-40).
Of claim 6, further configured to enhance the 3D reference model with pre-ablation data from additional image modalities including CT (computer tomography) and/or MR (magnetic resonance) (ultrasound image guided system associated with a CT system that is used to derive preparatory images of the tissue to be treated, Col. 17 lines 62-67 and Col. 15 lines 10-15).
Of claim 7, further configured to detect or receive a position of an ablation needle and indicate the needle position in the 3D reference model, and optionally configured to update the 3D BTM representation with derived data concerning heat propagation with respect to the needle position (spatial configuration, position and orientation for ablation tool, Col. 15 lines 15-28, Figure 9B shows position of the ablation tool in 3D representation, Col. 17 line 62- Col. 18 line 11).
Of claim 8, further configured to update the 3D BTM representation during the thermal ablation (Col. 17 lines 18-48, indication of bubble development and saturation, quantification of the degree of tissue damage updated)
Of claim 9, further configured to derive a 2D BTM from the 3D BTM representation, by detecting an optimal plane through the 3D BTM representation that best represents the tissue damage (use change the plane of the 2D image projected from 3D, thus can find a plane best presents the tissue damage Col. 10 line 63-Col. 11 line 17)
Of claim 10, further configured to derive a 3D BTM representation with 3D tissue damage estimation from the US images by applying the at least one DNN (deep learning procedure applied to detect shadowing to enhance analysis of the damaged tissue, Col. 12 lines 31-42)
Regarding to claims 11 and 16-19, Zohar teaches all limitations of claim 1 as discussed above.
Zohar further teaches following limitations:
Of claim 11, further configured to receive at least one reference image and derive the BTM with respect thereto, wherein the at least one reference image comprises at least one image of undamaged tissue (preparatory image will be undamaged tissue as it is imaged before the thermal ablation, Col. 17 line 62- Col. 18 line 11).
Of claim 16, further configured to accumulate multiple registered BTMs (indication of thermally damaged tissue registered repeatedly updated B-mode image, Col. 17 lines 18-48) and derived an accumulated BTM therefrom (data accumulated and processed Col. 15 lines 29-34).
Of claim 17, further configured to accumulate multiple BTMs with respect to damage thresholds defined for frames from which the BTMs are derived ( accumulation of bubbles to quantify the degree of tissue damage for the respective pixel, to reflect any defined damage threshold, Col. 17 lines 18-48; identifying a change in the correlation values above a specified threshold, identifying and detecting bubble movements in the registered B-mode image according to the identified key frames, Col. 18 line 63-Col. 19 line 5)
Of claim 18, further configured to apply shadow compensation and post processing to the BTM to yield a final BTM segmentation (mask corresponding to shadowed regions and be spatially merged for multiple identified bubble containing regions and image processing within calculated shadowed regions, detection threshold may be reduced in shadowed regions, Col. 12 lines 6-42)
Of claim 19, An ultrasound image-guided system comprising the ultrasound module of claim 1 (Figures 1A-1E an ultrasound-image guided system for thermal ablation ).
Regarding to claim 12, Zohar teaches all limitations of claim 11 as discussed above.
Zohar further teaches the ultrasound module of claim 11, further configured to derive the BTM by: selecting a reference frame from the at least one image of undamaged tissue (registration using sequential ultrasound images would require comparing reference image to current image as claimed, Col. 5 lines 43-53, registration of sequential ultrasound images identify a start of ablation by a change in the correlation values, thus will include change of correlation values arise from comparing to reference image of undamaged tissue, as ablation would result in damaging tissue, would cause change in correlation values Col. 2 lines 8-15), registering consecutive at least one received tissue image with respect to the reference frame (registration of sequential images), to yield a registered ablation frame (registration and bubble development and analyzing bubble movements, and segmentation Col. 10 line 63-Col. 11 line 17), and applying the DNN to segment tissue damage of the registered ablation frame to yield the BTM, wherein the DNN is two dimensional (2D) or three dimensional (3D) (Col. 12 lines 31-42).
Regarding to claim 20, Zohar teaches a method comprising deriving a biotrace map (BTM) (Figure 11C shows real time ablation zone morphology as “BioTrace”) during thermal ablation from at least one B-mode ultrasound (US) tissue image received during the thermal ablation (ultrasound image guided system with thermal ablation unit, B-mode image Col. 17 lines 20-24, Figure 9A, B-mode images with a border indicating the thermally damaged tissue updated with proceeding thermal ablation procedures, Col. 17 lines 18-48) by applying at least one deep neural network (DNN) to segment tissue damage in the at least one received ultrasound tissue image (estimating damaged tissue using image, with machine learning such as deep learning procedure applied to detecting shadowing, segmentation of identified tissues, Col. 12 lines 31-54; Figure 7C shows ultrasound image with demarcated damaged tissue regions, indication of outlining of the damaged region, Col. 14 lines 1-44).
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 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zohar as applied to claim 1 above, and further in view of “Wu et al.,” US 2024/0206907 (hereinafter Wu).
Regarding to claims 13-14, Zohar discloses all limitations of claim 1 as discussed above.
Zohar does disclose deep neural network, but does not explicitly disclose details of DNN.
However, in the analogous field of endeavor in image guided ablation procedures ([0002]), Wu teaches using deep learning convolutional neural network to extract and segment features from the image ([0032]), and discloses one backbone configured to extract features (backbone architecture extracting features from input images [0053]) and head configured to detect and/or segment tissue damage from the extracted feature (head architecture for segmentation [0053] and [0056]), and further teaches bounding box (mask) segmentation and mask region-based neural network ([0050] and [0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify deep neural network (machine learning algorithm) as taught by Zohar to incorporate teaching of Wu, as Zohar already discloses using DNN to segment tissue damage in tissue image, and since backbone and head of DNN was well known in the art as taught by Wu. One of ordinary skill in the art could have combined the elements as claimed by Zohar with no change in their respective functions, configuring its detail of DNN to have head architecture to segment, and backbone architecture to extract features from the images, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention. The motivation would have been to provide rapid and accurate detection of the features ([0032]) and improve segmentation ([0052]), and there was reasonable expectation of success.
Conclusion
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/PATRICIA J PARK/Primary Examiner, Art Unit 3798