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 .
Election/Restrictions
Claims 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed 7/3/2026.
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 1-20 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.
Claim 1 recites the limitation "the real-time shape data" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the MRI targets" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the breast" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "a breast biopsy" in line 9. It is unclear if this is a different breast biopsy from the “breast biopsy” in line 1, or the same breast biopsy. For the purpose of examination, it is interpreted as the same.
The term “accuracy required for an examination” in claim 3 is a relative term which renders the claim indefinite. The term “accuracy required for an examination” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As such, the level of accuracy is rendered indefinite, and claim 3 is interpreted as the volume elements are allocated to each area of the breast tissue without further limitations, for the purpose of examination.
Claim 5 recites “a breast of the patient” in line 3; however, it is unclear if the breast is the same breast as claim 1 line 8, or a different breast. For the purpose of examination, it is interpreted as the same.
Claim 6, line 4 recites “a contact area”; however, it is unclear if this is a new contact area, different from “the contact area” in line 3. Clarification is required. For the purpose of examination, it is interpreted as the same.
Claim 8 recites the limitation "the real-time deformable breast model" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the two data" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "each area" in line 3. It is unclear what of each area it is referring to.
Claims 2, 4, 7, 9-11, 13, and 15 are rejected based on their dependencies on the rejected claims.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4-5, and 8-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari, S., et al. (2023). Autonomous robotic system for breast biopsy with deformation compensation. IEEE Robotics and Automation Letters, 8(3), 1215–1222. doi.org/10.1109/lra.2023.3237499, hereto referred as Ferrari, and in view of Sengupta, A., et. al. (2020). Simultaneous tracking and elasticity parameter estimation of deformable objects. 2020 IEEE International Conference on Robotics and Automation (ICRA), 10038–10044. doi.org/10.1109/icra40945.2020.9196770, hereto referred as Sengupta, and in view of US 20110201965 A1 (Hibner et. al), hereto referred as Hibner.
As to claim 1, Ferrari teaches a method for a breast biopsy based on indirect magnetic resonance imaging (MRI) guidance comprising (Ferrari, pg. 1215, Abstract, " we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI"):
obtaining a breast MRI data of a patient (Ferrari, pg. 1216, "In MRI acquisition, the patient lies on the bed in prone position with bare breasts in a dedicated compartment");
generating a deformable breast model using the breast MRI data (pg. 1215, "Instead of directly registering the images, our idea is to use a biomechanical model of the breast and a deformation compensation algorithm to accurately predict the displacements of the tumors beforehand.");
measuring real-time breast shape of the patient using a depth sensor (Ferrari, pg. 1217, "Current breast shape is reconstructed by composing multiple point clouds acquired from different viewpoints while the robot moves on this trajectory."; Fig. 1, US probe and RGB-D camera);
estimating movements of the MRI targets inside of the breast (Ferrari, pg. 1216, "A dedicated software to implement a deformation compensation scheme for the tracking of lesion displacement due to the probe compression");
However, Ferrari does not teach performing a real-time deformable registration using the deformable breast model and the real-time shape data. Sengupta teaches relevant art of breast tissue deformation tracking (Sengupta, abstract). Sengupta teaches performing a real-time deformable registration using the deformable breast model and the real-time shape data (Sengupta, pg. 10039, "Our goal is to simultaneously track a deformable object and estimate its elasticity parameters. To achieve this goal, we propose a method composed of three different modules: one performing the deformation tracking, one estimating the elasticity parameters and one measuring the external deformation forces applied to the object."). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferrari in view of Sengupta to include performing a real-time deformable registration using the deformable breast model and the real-time shape data because doing so would improve accuracy of the device, as suggested by Sengupta (Sengupta, pg. 10038, "The approach utilizes a tightly coupled tracking and elasticity estimation module with a point-to-point ICP[9] for pointcloud registration along with a linear, tetrahedral FEM as underlying deformation model. The use of linear FEM makes this approach not suitable when large rotational deformations occur. Additionally, point-to-point ICP cannot handle occlusion adequately").
Ferrari does not teach performing a breast biopsy for the MRI targets, although strongly hints at it (Ferrari, pg. 1215, “In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions”). Hibner teaches a relevant art of breast biopsy and teaches performing a breast biopsy for the MRI targets (Hibner, [0047], “an MRI-compatible biopsy tool 14 that is selectably attached to a localization mechanism or fixture 16 to accurately and rapidly perform core biopsies of breast tissue with a minimum of insertions of a biopsy probe”). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferrari in view of Hibner because Ferrari teaches estimating MRI targets and hints at biopsy, and Hibner teaches the performance of it.
As to claim 2, Ferrari-Sengupta-Hibner teaches the generation of the deformable breast model includes: segmenting breast tissue of the patient from the breast MRI data; and representing the breast of the patient as a three-dimensional (3D) model by allocating a plurality of volume elements to the segmented breast tissue (Ferrari, pg. 1218, "FEM relies on a spatial discretization of the domain, which, in our case, is achieved by creating a 3D tetrahedral mesh of the anatomy. FEM allows to solve the equations of motion on each individual element, then assembles them into a global equation system").
As to claim 4, Ferrari-Sengupta-Hibner teaches each of the plurality of volume elements is given a governing equation based on elastic potential energy, and the governing equation includes material parameters of the breast tissue (Ferrari, pg. 1218, "The breast biomechanical behavior is modelled using Neo-Hookean material law, initialized with patient-specific deformation parameters").
As to claim 5, Ferrari does not teach updating material parameters of the deformable breast model in real time. Sengtupta teaches updating material parameters of the deformable breast model in real time (Sengupta, pg. 10038, "The aim of our approach is to automatically estimate the elasticity parameters of a soft object that is being deformed by the end-effector of a robot") based on interaction between a medical instrument and a breast of the patient (Sengupta, pg. 10043, " Deformation tracking and elasticity parameter estimation are run alternatively"), wherein the update is performed based on a contact area between the medical instrument and the breast. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Ferrari in view of Sengtupa because Ferrari recognizes that doing so would increase the accuracy (Ferrari, pg. 1220, " In the real clinical practice, deformation parameters can be either initialized with values obtained from the literature or they can be estimated using dedicated imaging (e.g. elastography [35]) or analytically [36]").
As to claim 8, Ferrari-Sengupta-Hibner teaches the real-time deformable breast model is updated (Sengupta, pg. 10040, " The pose of the camera w.r.t. the object, denoted by the homogeneous matrix CTO, is updated at the beginning of each frame" ) based on information collected about a breast of the patient from various angles by the depth sensor (Ferrari, pg. 1217, "Current breast shape is reconstructed by composing multiple point clouds acquired from different viewpoints while the robot moves on this trajectory.")
As to claim 9, Ferrari-Sengupta-Hibner teaches the information collected about the breast of the patient from various angles includes information about blind spots where visibility is obscured (Ferrari, pg. 1217, "As a first step, the robot is placed below the breast anatomy to acquire a point cloud that includes the lowest breast points, i.e., around the nipple"; note, the examiner interprets lowest breast points as a location where visibility is obscured).
As to claim 10, Ferrari-Sengupta-Hibner teaches the information collected about the breast of the patient from various angles is obtained by changing a position and orientation of the depth sensor in real time (Ferrari, pg. 1217, "The centroid of this point cloud represents the center of a circular trajectory that the robot will follow, and corresponds to the point that the camera will always look at.").
As to claim 11, Ferrari-Sengupta-Hibner teaches performing deformable registration between the deformable breast model generated from the MRI data (Ferrari, pg. 1217, "Current breast shape is reconstructed by composing multiple point clouds acquired from different viewpoints while the robot
moves on this trajectory") and the real-time breast shape data measured by the depth sensor (Ferrari, Fig. 1, US probe and RGB-D camera).
As to claim 12, Ferrari-Sengupta-Hibner teaches the registration between the two data is performed separately into rigid-body registration and real-time deformation registration (Ferrari, pg. 1216, "initial image registration.... a deformation compensation scheme").
Claim(s) 3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari-Sengupta-Hibner as applied to claim 1 above, and further in view of Mîra, A., et. al. (2018). A biomechanical breast model evaluated with respect to MRI data collected in three different positions. Clinical Biomechanics, 60, 191–199. doi.org/10.1016/j.clinbiomech.2018.10.020, hereto referred as Mira.
Claim 1 is as taught above.
As to claim 3, Ferrari-Sengupta-Hibner does not teach different numbers of volume elements are allocated to each area of the breast tissue based on accuracy required for an examination. Mira teaches similar art of modeling breast (Mira, title). Mira teaches different numbers of volume elements are allocated based on accuracy required for an examination (Mira, pg. 4, "The mesh that was chosen consists in 18453 tetrahedral elements, including 9625 elements that were assigned to the pectoral muscle and the thoracic cage, and 8858 elements that were assigned to breast tissue."). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferrari-Sengupta-Hibner in view of Mira to include different numbers of volume elements are allocated to each area of the breast tissue based on accuracy required for an examination because doing so would allow improving accuracy on areas where biopsy would occur.
As to claim 6, Ferrari-Sengupta-Hibner does not teach the material parameters of the deformable breast model are updated based on a difference between first surface information measured in real time in an area other than the contact area and second surface information calculated from an area other than a contact area on the deformable breast model. Mira teaches deformable breast model are updated based on a difference between first surface information measured in real time in an area other than the contact area and second surface information calculated from an area other than a contact area on the deformable breast model (Mira, pg. 8, "Subject‐specific mechanical tissue properties were determined using an optimization process based on a multi-gravity loading simulation procedure. First, for a given set of parameters, the breast stress‐free configuration was estimated by minimizing the difference between the simulated and the measured breast geometries in prone configuration. Then, from the new estimated stress‐free geometry, the supine breast configuration was computed and the new estimated geometry was compared to the measured one using modified Hausdorff distance 50."; pg. 8, "To exclude the geometry dissimilarity due to subject position, the modified Hausdorff distance was computed only on the breast skin surface.”) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Ferrari-Sengupta-Hibner in view of Mira to include the material parameters of the deformable breast model are updated based on a difference between first surface information measured in real time in an area other than the contact area and second surface information calculated from an area other than a contact area on the deformable breast model because Ferrari recognizes the need for improved accuracy with updated parameters (Ferrari, pg. 1220, " In the real clinical practice, deformation parameters can be either initialized with values obtained from the literature or they can be estimated using dedicated imaging (e.g. elastography [35]) or analytically [36]").
As to claim 7, Ferrari-Sengupta-Hibner-Mira teaches the difference between the first surface information and the second surface information is calculated based on Hausdorff distance (Mira, pg. 8, "Then, from the new estimated stress‐free geometry, the supine breast configuration was computed and the new estimated geometry was compared to the measured one using modified Hausdorff distance50").
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari-Sengupta-Hibner as applied to claim 1 above, and further in view of Newcombe, R. A., Fox, D., & Seitz, S. M. (2015). Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 343–352. doi.org/10.1109/cvpr.2015.7298631, hereto referred as Newcombe.
Claim 1 is taught as above.
As to claim 13, Ferrari-Sengupta-Hibner teaches the measuring real-time breast shape is performed by the depth sensor acquiring breast surface information of the patient (Ferrari, pg. 1217, "Current breast shape is reconstructed by composing multiple point clouds acquired from different viewpoints"; Fig. 1, US probe and RGB-D camera). However, Ferrari-Sengupta-Hibner does not teach generating and updating a deformable surface fusion model in real time. Newcombe teaches a relevant art of RGBD scans (Newcombe, abstract). Newcombe teaches generating and updating a deformable surface fusion model in real time (Newcombe, pg. 344, "DynamicFusion is the first system capable of real-time dense reconstruction in dynamic scenes using a single depth camera."). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ferrari-Sengupta-Hibner in view of Newcombe to include generating and updating a deformable surface fusion model in real time because Ferrari already teaches the equipment available, and doing so would better access to the model, as recognized by Newcombe (Newcombe, pg. 343, “Real-time 3D reconstruction systems like KinectFusion [18, 10] represent a major advance, by providing users the ability to instantly see the reconstruction and identify regions that remain to be scanned”).
As to claim 14, Ferrari-Sengupta-Hibner-Newcombe teaches the deformable surface fusion model is obtained by calculating a degree of deformation between a previous unit time and a current unit time for each area (Newcombe, pg. 349, "Finally, we update the current set of deformation nodes to correspond to the current time”).
As to claim 15, Ferrari-Sengupta-Hibner does not teach the details of the spatial resolution of the model. Newcombe teaches the deformable surface fusion model is obtained by giving higher spatial resolution to areas with a large degree of deformation than to areas with a relatively small degree of deformation (Newcombe, pg. 345, " In reality, surfaces tend to move smoothly in space, and so we can instead use a sparse set of transformations as bases and define the dense volumetric warp function through interpolation"). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Ferrari-Sengupta-Hibner in view of Newcombe to include the deformable surface fusion model is obtained by giving higher spatial resolution to areas with a large degree of deformation than to areas with a relatively small degree of deformation because doing so would be more computationally economical to concentrate the details in large deformation area and also increase the accuracy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELINA S JANG whose telephone number is (571)272-7019. The examiner can normally be reached M-F 9:00 am - 6:00 pm.
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/ELINA SOHYUN JANG/Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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