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 § 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.
Claim(s) 1-2, 6, 13-14 & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averbuch et al. (U.S. Patent 10,346,976) and further in view of Rusinkiewicz et al. (S. Rusinkiewicz and M. Levoy. "Efficient variants of the ICP algorithm," Proceedings Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada, 2001, pp. 145-152; enclosed prior) and Shushan et al. (U.S. Patent Application 2015/0157267 A1).
Claim 1: Averbuch teaches –
A method performed by a medical system [A method of registering a computer model of a lumen network to a patient's lumen network] (Claim 1) [system] (Col. 9, Line 52), the method comprising:
Examiner’s Note: It is understood that the system of Averbuch is a computer performing the disclosed steps.
receiving sensor data [receiving successive data points] (Claim 1) from one or more location sensors disposed on an instrument [an electromagnetic (EM) sensor coupled to a probe] (Claim 1),
the sensor data indicating a position of a distal end (see Element 115 of Figure 1) of the instrument in a sensor coordinate system [pertaining to a location of the EM sensor while the EM sensor is navigated inside a patient's lumen network] (Claim 1) [Registration, generally, is a method of computing transformations between two different coordinate systems] (Col. 7, Line 35-37);
Examiner’s Note: The prior art, guide path (as also known as locatable guide (LG) from Col. 7, Line 23), has a coordinate system which is registered to the bronchial tree coordinate system. Thus, the LG coordinate system reads on the claimed sensor coordinate system.
generating a set of sensor location points in the sensor coordinate system based on the received sensor data [establishing a sensor path of the EM sensor within the patient's lumen network based on the successive data points] (Claim 1) [a plurality of actual LG locations] (Figure 1, Element 130);
determining a set of model location points representing a skeleton model of a luminal network in a model coordinate system [a computer model of a lumen network] (Claim 1) [a line showing the BT skeleton] (Col. 8, Line 23) [a line showing the BT skeleton] (Col. 8, Line 30-31);
determining a registration between the sensor coordinate system [sensor path] and the model coordinate system [a path shape on the computer model] based on the set of sensor location points and the set of model location points [iteratively registering the computer model of the lumen network to the sensor path by registering a path shape on the computer model of the lumen network to the sensor path] (Claim 1);
weighting each sensor location point in the set of sensor location points [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
selectively updating the registration [Perform selection of the LG location stream according to the…registration history] (Col. 8, Line 43-45) between the sensor coordinate system and the model coordinate system for each sensor location point the weighted sensor location points [updating the displayed computer model of the lumen network based on the registering] (Claim 1); and
mapping the position of the distal end of the instrument to the skeleton model based on the registration or the updated registration [Project the selected LG location segments/points on BT skeleton to obtain paired segments/points] (Col. 8, Line 46-47) [The matched branch points (p1, p2, etc.) on the path] (Col. 8, Line 52-53).
Examiner’s Note: The step of mapping is being interpreted as applying the registration.
Averbuch fails to teach the specific details regarding the weighting of the sensor location point in the set of sensor locations points. However, Rusinkiewicz teaches –
weighting each location point in the set of location points based on a distance between the location point and the model so that location points closer to the model are weighted more heavily than location points further from the model [Assigning lower weights to pairs with greater point-to-point distances] (Page 5, Section 3.3 and the equation attached below) in order to increase accuracy and provide more robust data (Page 8, Appendix and Page 7, Section 5);
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Examiner’s Note: Rusinkiewicz teaches the equation between corresponding point pairs. The Examiner is interpreting one point of the pair to be the location point and the other point of the pair to be the model point.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the weighting process of Averbuch to include the specifics of weighting as taught by Rusinkiewicz in order to increase accuracy and provide more robust data (Page 8, Appendix and Page 7, Section 5)
Averbuch and Rusinkiewicz fail to teach the specific details with respect to the selectively updating the registration. However, Shushan teaches –
selectively updating the registration [inclusion step] [exclusion step] (Para 0060)…based on whether adding the respective location point (Figure 6, Element 128 & 144; INCLUDE, IN THE MAP, ELECTROANATOMICAL DATA FOR THE MAPPING POINTS WHOSE UPDATED POINT ERROR VALUES ARE LESS THAN THE POINT ERROR THERSHOLD) to the registration results in a registration error [update cumulative error value] greater than a threshold error [point error threshold] (Figure 6, Element 144), the registration error representing an amount of mismatch between the set of sensor locations points and the model [map] (Para 0063) [calculates a tentative cumulative error value for the all the received mapping points. Each of the tentative point mapping values indicates a quality of its respective mapping point relative to the tentative registration, and the tentative cumulative point error value indicates an overall quality of the tentative registration] (Para 0061), and the selective updating including:
updating the registration with the respective location point responsive to determining that adding the respective location point to the registration does not result in the registration error being greater than the threshold error (Figure 6, Element 144; INCLUDE, IN THE MAP, ELECTROANATOMICAL DATA FOR THE MAPPING POINTS WHOSE UPDATED POINT ERROR VALUES ARE LESS THAN THE POINT ERROR THERSHOLD); and
refraining from updating the registration with the respective location point responsive to determining that adding the respective location point to the registration results in the registration error being greater than the threshold error [Returning to step 140, if the tentative cumulative error value is greater than the point error threshold, then the method continues with step 120] (Para 0063; Inclusion Step 144 is skipped and Figure 6, Element 140) in order to indicate the overall quality of the registration (Para 0061)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Averbuch and Rusinkiewicz to include the details of selectively updating as taught by Shushan in order to indicate the overall quality of the registration (Para 0061).
Claim 2/1: Averbuch teaches wherein: the skeleton model [BT skeleton] (Figure 1, Element 110) represents a centerline of the luminal network [generating a BT skeleton…with an algorithm that automatically detects the trachea inside the CT volume, a three-dimensional image created from a plurality of CT scans, and…the segmented and filtered data is skeletonized--center lines of the perceived airways are defined and used to build an anatomically valid virtual model of the airways] (Col. 2, Line 54-63), and the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42) comprises:
transforming the sensor location point from the sensor coordinate system [LG location segments/points] to the model coordinate system [Project the selected LG location segments/points on BT skeleton to obtain paired segments/points] (Col. 8, Line 46-47); and
determining the distance [Minimal Distance] (Col. 8, Line 49) between the transformed sensor location point in the model coordinate system [LG location segments/points on BT skeleton] (Col. 8, Line 46-47) and the centerline of the luminal network [Minimal Distance relative to the local bronchi diameter] (Col. 8, Line 49-50).
Claim 6/1: Averbuch teaches wherein:
the instrument further comprises a camera configured to capture images of an interior of the luminal network [bronchoscope] (Col. 6, Line 46), and
the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42) comprises determining the distance between the sensor location point [Minimal Distance] (Col. 8, Line 49) and the skeleton model [Minimal Distance relative to the local bronchi diameter] (Col. 8, Line 49-50) based on the captured images [recorded using the LG aided by a bronchoscope in the patient's airways. Doing so allows a computer to align the digital map with the data received from the LG such that an accurate representation of the LG's location is displayed on a monitor] (Col. 1, Line 59-63).
Claim 13: Averbuch teaches –
A medical system [A method of registering a computer model of a lumen network to a patient's lumen network] (Claim 1) [system] (Col. 9, Line 52) comprising:
an instrument having one or more location sensors disposed thereon [an electromagnetic (EM) sensor coupled to a probe] (Claim 1);
one or more processors [computer] (Col 1, Line 60); and
Examiner’s Note: It is understood that in order for the computer of Averbuch to performed the disclosed steps, the computer of Averbuch requires at least one processor.
a memory storing instructions that, when executed by the one or more processors [computer] (Col 1, Line 60), cause the medical system to:
Examiner’s Note: It is understood that in order for the computer of Averbuch to performed the disclosed steps, the computer of Averbuch requires memory storing instructions (aka software program).
receive sensor data [receiving successive data points] (Claim 1) from the one or more location sensors disposed on the instrument [an electromagnetic (EM) sensor coupled to a probe] (Claim 1),
the sensor data indicating a position of a distal end (see Element 115 of Figure 1) of the instrument in a sensor coordinate system [pertaining to a location of the EM sensor while the EM sensor is navigated inside a patient's lumen network] (Claim 1) [Registration, generally, is a method of computing transformations between two different coordinate systems] (Col. 7, Line 35-37);
generate a set of sensor location points in the sensor coordinate system based on the received sensor data [establishing a sensor path of the EM sensor within the patient's lumen network based on the successive data points] (Claim 1) [a plurality of actual LG locations] (Figure 1, Element 130);
determine a set of model location points representing a skeleton model of a luminal network in a model coordinate system [a computer model of a lumen network] (Claim 1) [a line showing the BT skeleton] (Col. 8, Line 23) [a line showing the BT skeleton] (Col. 8, Line 30-31);
determine a registration between the sensor coordinate system [sensor path] and the model coordinate system [a path shape on the computer model] based on the set of sensor location points and the set of model location points [iteratively registering the computer model of the lumen network to the sensor path by registering a path shape on the computer model of the lumen network to the sensor path] (Claim 1);
weight each sensor location point in the set of sensor location points [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
selectively updating the registration [Perform selection of the LG location stream according to the…registration history] (Col. 8, Line 43-45) between the sensor coordinate system and the model coordinate system for each sensor location point the weighted sensor location points [updating the displayed computer model of the lumen network based on the registering] (Claim 1); and
map the position of the distal end of the instrument to the skeleton model based on the registration or the updated registration [Project the selected LG location segments/points on BT skeleton to obtain paired segments/points] (Col. 8, Line 46-47) [The matched branch points (p1, p2, etc.) on the path] (Col. 8, Line 52-53)
Examiner’s Note: The step of mapping is being interpreted as applying the registration.
Averbuch fails to teach the specific details regarding the weighting of the sensor location point in the set of sensor locations points. However, Rusinkiewicz teaches –
weight each location point in the set of location points based on a distance between the location point and the model so that location points closer to the model are weighted more heavily than location points further from the model [Assigning lower weights to pairs with greater point-to-point distances] (Page 5, Section 3.3 and the equation attached below) in order to increase accuracy and provide more robust data (Page 8, Appendix and Page 7, Section 5);
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Examiner’s Note: Rusinkiewicz teaches the equation between corresponding point pairs. The Examiner is interpreting one point of the pair to be the location point and the other point of the pair to be the model point.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the weighting process of Averbuch to include the specifics of weighting as taught by Rusinkiewicz in order to increase accuracy and provide more robust data (Page 8, Appendix and Page 7, Section 5)
Averbuch and Rusinkiewicz fail to teach the specific details with respect to the selectively updating the registration. However, Shushan teaches –
selectively updating the registration [inclusion step] [exclusion step] (Para 0060)…based on whether adding the respective location point (Figure 6, Element 128 & 144; INCLUDE, IN THE MAP, ELECTROANATOMICAL DATA FOR THE MAPPING POINTS WHOSE UPDATED POINT ERROR VALUES ARE LESS THAN THE POINT ERROR THERSHOLD) to the registration results in a registration error [update cumulative error value] greater than a threshold error [point error threshold] (Figure 6, Element 144), the registration error representing an amount of mismatch between the set of sensor locations points and the model [map] (Para 0063) [calculates a tentative cumulative error value for the all the received mapping points. Each of the tentative point mapping values indicates a quality of its respective mapping point relative to the tentative registration, and the tentative cumulative point error value indicates an overall quality of the tentative registration] (Para 0061), and the selective updating including:
updating the registration with the respective location point responsive to determining that adding the respective location point to the registration does not result in the registration error being greater than the threshold error (Figure 6, Element 144; INCLUDE, IN THE MAP, ELECTROANATOMICAL DATA FOR THE MAPPING POINTS WHOSE UPDATED POINT ERROR VALUES ARE LESS THAN THE POINT ERROR THERSHOLD); and
refraining from updating the registration with the respective location point responsive to determining that adding the respective location point to the registration results in the registration error being greater than the threshold error [Returning to step 140, if the tentative cumulative error value is greater than the point error threshold, then the method continues with step 120] (Para 0063; Inclusion Step 144 is skipped and Figure 6, Element 140) in order to indicate the overall quality of the registration (Para 0061)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Averbuch and Rusinkiewicz to include the details of selectively updating as taught by Shushan in order to indicate the overall quality of the registration (Para 0061).
Claim 14/13: Averbuch teaches wherein: the skeleton model [BT skeleton] (Figure 1, Element 110) represents a centerline of the luminal network [generating a BT skeleton…with an algorithm that automatically detects the trachea inside the CT volume, a three-dimensional image created from a plurality of CT scans, and…the segmented and filtered data is skeletonized--center lines of the perceived airways are defined and used to build an anatomically valid virtual model of the airways] (Col. 2, Line 54-63), and the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42) comprises:
transforming the sensor location point from the sensor coordinate system [LG location segments/points] to the model coordinate system Project the selected LG location segments/points on BT skeleton to obtain paired segments/points] (Col. 8, Line 46-47); and
determining the distance [Minimal Distance] (Col. 8, Line 49) between the transformed sensor location point in the model coordinate system [LG location segments/points on BT skeleton] (Col. 8, Line 46-47) and the centerline of the luminal network [Minimal Distance relative to the local bronchi diameter] (Col. 8, Line 49-50).
Claim 18/13: Averbuch teaches wherein:
the instrument further comprises a camera configured to capture images of an interior of the luminal network [bronchoscope] (Col. 6, Line 46), and
the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42) comprises determining the distance between the sensor location point [Minimal Distance] (Col. 8, Line 49) and the skeleton model [Minimal Distance relative to the local bronchi diameter] (Col. 8, Line 49-50) based on the captured images [recorded using the LG aided by a bronchoscope in the patient's airways. Doing so allows a computer to align the digital map with the data received from the LG such that an accurate representation of the LG's location is displayed on a monitor] (Col. 1, Line 59-63).
Claim(s) 3-4 & 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averbuch et al. (U.S. Patent 10,346,976), Rusinkiewicz et al. (S. Rusinkiewicz; enclosed prior) and Shushan et al. (U.S. Patent Application 2015/0157267 A1) and further in view of Mollus et al. (W/O 2015/091299 A1; enclosed prior).
Claim 3/1: Averbuch teaches wherein the weighting of each sensor location point comprises assigning a weight value to the sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42).
Averbuch, Rusinkiewicz and Shushan fail to teach wherein the set of weight values are binary. However, Mollus teaches wherein the set of weight values are binary [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10) in order to speed up processing by being able to focus processing on the anatomy-specific parts; such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10)
Examiner’s Note: The teaching notes that non anatomy specific elements may be in data sets used for alignment (registration), such as the heart valve as taught by Mollus. In order to solve the alignment issues the non-anatomy specific elements may cause due to the differences between the data sets, the suggested fix is to perform a binary weighting scheme. Within the art of binarization, it is conventional to label the anatomy as “1” such as a lumen and non-anatomy as “0” such as background. The Examiner contends that Averbuch teaches, “(weighting) on the stream of LG locations” (Col. 8, Line 41-42) and Mollus teaches a “(binary) weighting scheme”. It would have been obvious to add the binary weighting scheme to the weighting scheme of Averbuch.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the binary weighting scheme of Mollus to the weighting method of Averbuch, Rusinkiewicz and Shushan in order to speed up processing by being able to focus processing on the anatomy-specific parts (See Examiner’s note above and Page 12, Line 4-10); such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10).
Claim 4/3/1: Averbuch teaches further comprising wherein the assigning of the weight value [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42).
Averbuch fails to teach a distance threshold. However, Rusinkiewicz teaches –
comparing the distance between the location point and the model to a distance threshold [greater than some threshold] (Page 5, Bullet Point Number 4) in order to eliminate outliers and increase accuracy (Page 5, Section 3.4);
assigning, to the location point, a keep if the distance between the location point and the skeleton model is less than the distance threshold (See Page 5-6, Section 3.4); and
assigning, to the location point, a rejection if the distance between the location point and the model is greater than or equal to the distance threshold (See Page 5-6, Section 3.4) in order to eliminate outliers and increase accuracy (Page 5, Section 3.4)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the weighting of Averbuch to include the thresholding as taught by Rusinkiewicz in order to eliminate outliers and increase accuracy (Page 5, Section 3.4).
Averbuch, Rusinkiewicz and Shushan fail to teach binary weighting. However, Mollus teaches –
assigning, to the location point, a binary weight value equal to 1 [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10); and
assigning, to the location point, a binary weight value equal to 0 [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10) in order to speed up processing by being able to focus processing on the anatomy-specific parts; such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10)
Examiner’s Note: The binary weighting scheme of Mollus is very similar to the rejection pairing of Rusinkiewicz. The Disclosure of Mollus is merely labelling the rejected pairs of Rusinkiewicz as zero and the difference is merely nomenclature; between one/zero and rejected/accepted.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the binary weighting scheme of Mollus to the weighting method of Averbuch, Rusinkiewicz and Shushan in order to speed up processing by being able to focus processing on the anatomy-specific parts (See Examiner’s note above and Page 12, Line 4-10); such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10).
Claim 15/13: Averbuch teaches wherein the weighting of each sensor location point comprises assigning a weight value to the sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42).
Averbuch, Rusinkiewicz and Shushan fail to teach wherein the set of weight values are binary. However, Mollus teaches wherein the set of weight values are binary [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10) in order to speed up processing by being able to focus processing on the anatomy-specific parts; such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10)
Examiner’s Note: The teaching notes that non anatomy specific elements may be in data sets used for alignment (registration), such as the heart valve as taught by Mollus. In order to solve the alignment issues the non-anatomy specific elements may cause due to the differences between the data sets, the suggested fix is to perform a binary weighting scheme. Within the art of binarization, it is conventional to label the anatomy as “1” such as a lumen and non-anatomy as “0” such as background. The Examiner contends that Averbuch teaches, “(weighting) on the stream of LG locations” (Col. 8, Line 41-42) and Mollus teaches a “(binary) weighting scheme”. It would have been obvious to add the binary weighting scheme to the weighting scheme of Averbuch.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the binary weighting scheme of Mollus to the weighting method of Averbuch, Rusinkiewicz and Shushan in order to speed up processing by being able to focus processing on the anatomy-specific parts (See Examiner’s note above and Page 12, Line 4-10); such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10).
Claim 16/13: Averbuch teaches wherein the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42).
Averbuch fails to teach a distance threshold. However, Rusinkiewicz teaches –
comparing the distance between the location point and the model to a distance threshold [greater than some threshold] (Page 5, Bullet Point Number 4);
assigning, to the location point, a keep if the distance between the location point and the model is less than the distance threshold (See Page 5-6, Section 3.4); and
assigning, to the location point, a rejection if the distance between the location point and the model is greater than or equal to the distance threshold (See Page 5-6, Section 3.4) in order to eliminate outliers and increase accuracy (Page 5, Section 3.4)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the weighting of Averbuch to include the thresholding as taught by Rusinkiewicz in order to eliminate outliers and increase accuracy (Page 5, Section 3.4).
Averbuch, Rusinkiewicz and Shushan fail to teach binary weighting. However, Mollus teaches –
assigning, to the location point, a binary weight value equal to 1 [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10); and
assigning, to the location point, a binary weight value equal to 0 [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10) in order to speed up processing by being able to focus processing on the anatomy-specific parts; such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10)
Examiner’s Note: The binary weighting scheme of Mollus is very similar to the rejection pairing of Rusinkiewicz. The Disclosure of Mollus is merely labelling the rejected pairs of Rusinkiewicz as zero and the difference is merely nomenclature; between one/zero and rejected/accepted.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the binary weighting scheme of Mollus to the weighting method of Averbuch, Rusinkiewicz and Shushan in order to speed up processing by being able to focus processing on the anatomy-specific parts (See Examiner’s note above and Page 12, Line 4-10); such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10).
Claim(s) 7-8 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averbuch et al. (U.S. Patent 10,346,976), Rusinkiewicz et al. (S. Rusinkiewicz; enclosed prior) and Shushan et al. (U.S. Patent Application 2015/0157267 A1) and further in view of Zhu et al. (U.S. Patent Application 2010/0036233 A1).
Claim 7/1: Averbuch teaches wherein:
the skeleton model [BT skeleton] (Figure 1, Element 110) represents a centerline of a lumen of the luminal network [generating a BT skeleton…with an algorithm that automatically detects the trachea inside the CT volume, a three-dimensional image created from a plurality of CT scans, and…the segmented and filtered data is skeletonized--center lines of the perceived airways are defined and used to build an anatomically valid virtual model of the airways] (Col. 2, Line 54-63), and the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42), and
the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
Averbuch, Rusinkiewicz and Shushan fail to teach sorting the points. However, Zhu teaches –
sorting the set of sensor location points based on the distances between the sensor location points and the centerline [10% of the control points are trimmed to account for noise in the data (the trimmed points have the largest distance between the point and the surface of the scan)] (Para 0068), and
Examiner’s Note: The sorting is into two bins: trimmed points and the remaining points.
Averbuch in view of Zhu teaches –
assigning a weight value to the sensor location point (Para 0105 of Averbuch) based on the sorted set of sensor location points (Para 0068 of Zhu) in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Examiner’s Note: Zhu would trim the points with the largest distance as noisy and then the weights assignments as taught by Acerbuch would be performed on data with less noise.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the sorting as taught by Zhu on the points as taught by Averbuch, Rusinkiewicz and Shushan in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Claim 8/7/1: Averbuch failed to sorting based on a percentage. However, Zhu teaches wherein the sorting of the set of sensor location points is sorted into a first group [remaining 90% of the control points] and a second group [10% of the control points] (Para 0068) so that the first group includes a percentage of the sensor location points having the shortest distances to the centerline of the lumen and the second group includes the remainder of the sensor location points in the set of sensor location points [the trimmed points have the largest distance between the point and the surface of the scan] (Para 0068; since the second group 10% control points are described as largest distance than the smallest distance would be the remaining 90% of control points) into a first group [remaining 90% of the control points] and remaining sensor location points into a second group in order to improve the accuracy of the data set in order to improve patient outcomes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the sorting as taught by Zhu on the points as taught by Averbuch, Rusinkiewicz and Shushan in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Claim 19/13: Averbuch teaches wherein:
the skeleton model [BT skeleton] (Figure 1, Element 110) represents a centerline of a lumen of the luminal network [generating a BT skeleton…with an algorithm that automatically detects the trachea inside the CT volume, a three-dimensional image created from a plurality of CT scans, and…the segmented and filtered data is skeletonized--center lines of the perceived airways are defined and used to build an anatomically valid virtual model of the airways] (Col. 2, Line 54-63), and the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
Averbuch, Rusinkiewicz and Shushan fail to teach sorting the points. However, Zhu teaches –
sorting the set of sensor location points into a first group [remaining 90% of the control points] and a second group 10% of the control points] (Para 0068) so that the first group includes a percentage of the sensor location points having the shortest distances to the centerline of the lumen [10% of the control points are trimmed to account for noise in the data (the trimmed points have the largest distance between the point and the surface of the scan)] (Para 0068) and the second group includes the remainder of the sensor location points in the set of sensor location points [the trimmed points have the largest distance between the point and the surface of the scan] (Para 0068; since the second group 10% control points are described as largest distance than the smallest distance would be the remaining 90% of control points) in order to improve the accuracy of the data set in order to improve patient outcomes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the sorting as taught by Zhu on the points as taught by Averbuch, Rusinkiewicz and Shushan in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Averbuch in view of Zhu teaches –
assigning a weight value to the sensor location point based (Para 0105 of Averbuch) on the sorted set of sensor location points (Para 0068 of Zhu) in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Examiner’s Note: Zhu would trim the points with the largest distance as noisy and then the weights assignments as taught by Acerbuch would be performed on data with less noise.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the sorting as taught by Zhu on the points as taught by Averbuch and Rusinkiewicz in order to remove noisy points within the data (Para 0068) in order to improve the accuracy of the data set in order to improve patient outcomes.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averbuch et al. (U.S. Patent 10,346,976), Rusinkiewicz et al. (S. Rusinkiewicz; enclosed prior), Shushan et al. (U.S. Patent Application 2015/0157267 A1) and Zhu et al. (U.S. Patent Application 2010/0036233 A1) and further in view of Mollus et al. (W/O 2015/091299 A1; enclosed prior).
Claim 9/8/7/1: Averbuch teaches wherein the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
Averbuch, Rusinkiewicz, Shushan and Zhu fail to teach binary weighting. However, Mollus teaches –
assigning, to the location point, a weight value equal to 1 if the location point is in the first group [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10); and
assigning, to the location point, a weight value equal to 0 if the location point in the second group [an anatomy-specific (binary) weighting scheme] (Page 12, Line 4-10) in order to speed up processing by being able to focus processing on the anatomy-specific parts; such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10)
Examiner’s Note: The binary weighting scheme of Mollus is very similar to the rejection pairing of Rusinkiewicz. The Disclosure of Mollus is merely labelling the rejected pairs of Rusinkiewicz as zero and the difference is merely nomenclature; between one/zero and rejected/accepted.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the binary weighting scheme of Mollus to the weighting method of Averbuch, Rusinkiewicz, Shushan and Zhu in order to speed up processing by being able to focus processing on the anatomy-specific parts (See Examiner’s note above and Page 12, Line 4-10); such as in the example not including the dynamic heart valve in the fitted model (Page 12, Line 4-10).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Averbuch et al. (U.S. Patent 10,346,976), Rusinkiewicz et al. (S. Rusinkiewicz; enclosed prior) and Shushan et al. (U.S. Patent Application 2015/0157267 A1) and further in view of Camarillo et al. (U.S. Patent Application 2020/0297444 A1).
Claim 22/1: Averbuch teaches the skeleton model [BT skeleton] (Figure 1, Element 110) represents a centerline of a lumen of the luminal network [generating a BT skeleton…with an algorithm that automatically detects the trachea inside the CT volume, a three-dimensional image created from a plurality of CT scans, and…the segmented and filtered data is skeletonized--center lines of the perceived airways are defined and used to build an anatomically valid virtual model of the airways] (Col. 2, Line 54-63), and the weighting of each sensor location point [Perform cleaning, decluttered and classification (weighting) on the stream of LG locations] (Col. 8, Line 41-42)
Averbuch, Rusinkiewicz and Shushan fail to teach weighting based on articulation data. However, Camarillo teaches –
the sensor data includes articulation data [Robotic command and kinematics data 94 may also be used by the localization module 95 to provide localization data 96 for the robotic system] (Para 0115) indicating a proximity of the distal end of the instrument to the centerline of the luminal network for each respective sensor location point [The localization module 95 may use the input data 91-94] (Para 0117) [a probabilistic approach where the localization module 95 assigns a confidence weight to the location determined from each of the input data 91-94] (Para 0117), and
weighting the sensor location points is further based on the articulation data so that sensor location points associated with the distal end of the instrument that are relatively closer to the centerline are weighted more heavily than sensor location points associated with the distal end of the instrument that are relatively further away from the centerline [the confidence of the location determined by the EM data 93 can be decrease and the localization module 95 may rely more heavily on the vision data 92 and/or the robotic command and kinematics data 94] (Para 0117) [the localization/navigation system 300 can determine a confidence level of the position and/or orientation of the endoscope] (Para 0148) in order to provide the physician with the ability to perform the procedure with improved ease of use such that one or more of the instruments of the system can be controlled by a single user (Para 0044).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Averbuch, Rusinkiewicz and Shushan to include the weighting based on articulation data as taught by Camaillo in order to provide the physician with the ability to perform the procedure with improved ease of use such that one or more of the instruments of the system can be controlled by a single user (Para 0044).
Allowable Subject Matter
Claim(s) 5 & 17 is/are allowed. The prior art taught weighting based on diameters of the lumen but not the specifics of larger diameters being weighted less than smaller diameters.
Response to Arguments
Applicant’s arguments, see Page 8, filed 03/05/2026, with respect to the Rejection under 35 USC § 112(a) have been fully considered and are persuasive. The rejection under 35 USC § 112(a) of the claims has been withdrawn.
Applicant’s arguments, see Page 8, filed 03/05/2026, with respect to the Rejection under 35 USC § 112(b) have been fully considered and are persuasive. The rejection under 35 USC § 112(b) of the claims has been withdrawn.
Applicant's arguments filed 03/05/2026 have been fully considered but they are not persuasive. The Examiner respectfully disagrees that Shushan fails to teach the new claim limitations. The rejection above has been amended in response to the new claim amendments. The Applicant did not submit arguments as to why Shushan fails to teach the new claim amendments. The Examiner contends that Shushan analyzes each individual sensor location (Figure 6, Element 126) and the addition of each individual sensor to the overall cumulative error (Figure 6, Element 140). The arguments are unconvincing. The Examiner may find the arguments convincing if the Applicant explained in detail how Figure 6 of Shushan fails to read on the claim limitations.
The rejection is deemed proper and is hereby maintained.
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.
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/Helene Bor/Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
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