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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2023 206 855.9, filed on 07/19/2023.
Response to Amendment
Applicant’s Amendments filed on 07/14/2026 has been entered and made of record.
Status of Claims
Currently pending Claim(s):
Amended claim(s):
1-15
2, 4, 7 and 14
Response to Arguments
This office action is responsive to Applicant's Arguments/Remarks made in an Amendments received on 07/14/2026.
In view of the amendments filed on 07/14/2026 to the drawings, the objections to the drawings are withdrawn.
In view of the amendments filed on 07/14/2026 to claims 2, 4, and 14, the objections to the claims are withdrawn.
In view of the new claim amendments and applicant arguments, Remarks filed on 07/14/2026, with respect to the 35 U.S.C. 112(b) claim rejections have been carefully considered and the claims rejections to claims 2 and 7 under 35 U.S.C. 112(b) are withdrawn.
In view of Applicant's lack of written response with respect to 35 U.S.C. 112(f) claim interpretation, the interpretation made to claim 12 is maintained. Applicant has not clarified why the claimed limitations do not invoke 112(f). According to MPEP 2181, 35 USC 112(f) is applicable to claim limitation if it meets the 3-prong analysis set forth in the previous Office Action. Applicant did not specifically point out why any of these prongs have not been met, and as such, the claims continue to be treated under 112(f). Applicant is welcome to amend the claim so that the limitations no longer invoke 112(f) by, e.g., modifying the "means" or generic placeholder with specific structure, with careful consideration that no new matter is introduced.
In view of applicant's argument, Remarks filed on 07/14/2026, with respect to independent claims 1, 12, and 15 under 35 U.S.C. 103, arguments have been fully considered but they are not persuasive. Applicant argues on pages 7-8:
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The Examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., ”A sequence of two-dimensional images capturing topological features from the perspective of a camera is fundamentally different from a physical, geometric course of a medical instrument…rather than the physical geometric course of the instrument itself, Zhao inherently fails to teach bringing the geometric course of the main duct and branches into register with the course of the medical instrument.”) are not recited in the pending claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The claim does not recite that the course of the medical instrument is a “geometric course”. Therefore, Zhao teaches “a course of the medical instrument” at paragraph [0048] “A sequence of images that are topologically equivalent (e.g., a sequence in which the same set of blobs or lumens appears) is referred to herein as a “tracklet”. Thus, the path that the medical device moves through the branched anatomical structure is a sequence of tracklets.” The sequence of tracklets is the path of the medical device which is representative of the course of the medical instrument. Further, as the claim does not recite “bringing the geometric course of the main duct and branches into register with the course of the medical instrument”, Zhao teaches “bringing the course of the main duct and of the first branch and the second branch into register with the course of the medical instrument” at paragraph [0047] “the method compares information of topologies, feature attributes, behaviors, and/or relationships of lumens seen in a sequence of captured images with corresponding information of topologies, feature attributes, behaviors, and/or relationships of lumens in the computer model of the branched anatomical structure. A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”.
Applicant argues on page 8:
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The Examiner respectfully disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., ”the geometric course of a medical instrument and the course of an anatomical branch” and “calculating similarity measures that compare a medical instrument's course to the respective courses of the anatomical branches” [Emphasis added]) are not recited in the pending claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Further, the claim does not calculate the similarity measures that compare a medical instrument's course to the respective courses of the anatomical branches; instead, the claim recites “calculating a first similarity measure of a portion of the course of the medical instrument in the first branch or the second branch with a first portion of the course of the first branch” and “a second similarity measure of the portion of the course of the medical instrument in the first branch or the second branch with a second portion of the course of the second branch”. The pending claim does not compare the medical instrument’s course to a respective branch instead the branches are recited as “first branch or the second branch” [Emphasis added]. As such, Zhao was relied on to teach “calculating a first similarity measure of a portion of the course of the medical instrument in the first branch or the second branch [with a first portion of the course of the first branch] and a second similarity measure of the portion of the course of the medical instrument in the first branch or the second branch [with a second portion of the course of the second branch]” at paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.” and paragraph [0078] “The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”. Whereas, Kaftan was relied on to teach calculating similarity measures with the first and second portions of the first and second branches at column 4 [lines 18-23] “acquire tree-like structures representing a physical object or model; extract a path from a first tree-like structure and a path from a second tree-like structure; compare the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determine if the paths match based on the similarity measurement.“.
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Applicant argues on page 8:
The Examiner respectfully disagrees. Zhao does not rely solely on “the topological matching of two-dimensional image "blobs" representing lumens to synthetic images”. Rather, Zhao teaches that the feature attributes used for comparison may include three-dimensional features extracted from multiple image frames at paragraph [0075] “In addition to feature attributes determined from topological and geometrical features of extracted blobs as described above, other feature attributes may be defined and used in the methods described herein which are related to feature points identified in captured images. As an example, a scale invariant feature transform (SIFT) may be used to extract feature points from the captured images. The feature attributes used to compare the real image and the computer model can also be three-dimensional features extracted from multiple image frames using Structure from Motion (SfM) techniques.”. As such, one skilled in the art could have combined the teaching of Zhao with Kaftan without discarding the core topological blob-matching algorithm of Zhao and the motivation for doing so would have been to automatically and efficiently compare corresponding structures within tree-based models as suggested by Kaftan (see Kaftan, Column 9 [lines 18-25]).
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Applicant argues on page 9:
The Examiner respectfully disagrees. Naik teaches “wherein a length of the medical instrument (page 216 right column paragraph 1 of Central venous catheter insertion technique “The optimal length of the catheter insertion ‘L’ was calculated as: Optimal length (L) = Calculated Measurement (X) + Depth of vein from skin (Y)”) is detected and the method is only carried out (page 216 left column first paragraph “A correct length to be inserted, thus, needs to be pre-determined before inserting the catheter.”) once the length of the medical instrument is greater than a length of the course of the main duct of the data set. (Optimal length (L) = Calculated Measurement (X) + Depth of vein from skin (Y)”.).
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The length of the instrument is determined prior to inserting the catheter and is the sum of calculated measurement ‘X’ and the depth of vein from skin (i.e. a length of the course of the main duct of the data set). As such, the optimal length is greater than the depth of the vein and is determined prior to insertion. As shown above, in Figures 1a and 1b of Naik, the calculated “X” and the depth of the vein from the skin are used to determine the optimal length of the catheter insertion “L”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “dynamically tracking the inserted length of the instrument in real- time”) are not recited in the pending claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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Applicant argues on page 9:
The Examiner respectfully disagrees. Naik was relied on to teach “wherein a risk measure is determined as a function of a length of the medical instrument in the first branch or second branch” at page 215 end of right column continued to page 216 beginning of left column “The ideal position of the catheter is in a large central vein outside the pericardial reflection and parallel to its long axis, such that it does not impinge on the vessel/heart wall.[2] It is imperative that the tip be proximal to the boundaries of the pericardial sac to prevent cardiac tamponade.[3] On the other hand, if the catheters are placed too proximally, there is an elevated risk of thrombosis.”. The risk is associated with the length/location of the catheter being placed proximal to the boundaries of the pericardial sac and is not simply a general clinical acknowledgement of risk, but instead is elevated depending on the catheter. Further, Zhao was relied on to teach the similarity measures at paragraph [0046] “for registering a computer model of a branched anatomical structure to a medical device so that a position of the medical device in the branched anatomical structure may be determined as the medical device moves through the branched anatomical structure. In this case, the position of the medical device in the branched anatomical structure indicates the lumen of the branched anatomical structure that the medical device is currently in.” and paragraph [0078] “The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”.
The Applicant's argument does not address the actual reasoning of the Examiner's rejections. Instead, the Applicant attacks the reference singly for lacking teachings that the Examiner relied on a combination of references to show. It is well established that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (See In re Keller, 642 F.2d 413). The court requires that references must be read, not in isolation, but for what they fairly teach in combination with the prior art as a whole (See In re Keller, 642 F.2d 413, 425 (CCPA 1981); In re Merck & Co., 800 F.2d 1091 (Fed. Cir. 1986)).
Applicant Argues on page 9:
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The Examiner respectfully disagrees. The Examiner does not rely solely on Naik to teach claim 11. Instead, the Examiner relies on Zhao to teach “the detection of the course of the medical instrument and used for calculating the similarity measures” at paragraph [0061] “Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure…when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”. Further, the Examiner relies on Naik to teach wherein a length of the medical instrument is detected independently at page 216 left column first paragraph “A correct length to be inserted, thus, needs to be pre-determined before inserting the catheter.” and Page 216 right column paragraph 1 of Central venous catheter insertion technique…“The distance between the skin to AxV was measured and this distance was designated as ‘Y,’ which was added to the calculated measurement ‘X’ [Figure 1b]. The optimal length of the catheter insertion ‘L’ was calculated as: Optimal length (L) = Calculated Measurement (X) + Depth of vein from skin (Y)”.
As explained above, the Applicant's argument does not address the actual reasoning of the Examiner's rejections. Instead, the Applicant attacks the reference singly for lacking teachings that the Examiner relied on a combination of references to show. It is well established that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (See In re Keller, 642 F.2d 413). The court requires that references must be read, not in isolation, but for what they fairly teach in combination with the prior art as a whole (See In re Keller, 642 F.2d 413, 425 (CCPA 1981); In re Merck & Co., 800 F.2d 1091 (Fed. Cir. 1986)).
Finally, Applicant argues on page 10:
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The Examiner respectfully disagrees, as explained above, Zhao does not rely solely on “2D topological image tracking”. Rather, Zhao teaches that the feature attributes used for comparison may include three-dimensional features extracted from multiple image frames at paragraph [0075] “In addition to feature attributes determined from topological and geometrical features of extracted blobs as described above, other feature attributes may be defined and used in the methods described herein which are related to feature points identified in captured images. As an example, a scale invariant feature transform (SIFT) may be used to extract feature points from the captured images. The feature attributes used to compare the real image and the computer model can also be three-dimensional features extracted from multiple image frames using Structure from Motion (SfM) techniques.”. Further, Breininger was relied on to teach measure are in each case based on a Hausdorff metric paragraph [0027] “the registration facility is configured to perform the registration as a 2D-3D registration by minimizing a 2D form or 2D formulation of the line distance metric, for example, the modified 2D Hausdorff distance, and to use a 2D projection image as the live image and to determine the at least one simulated course line, i.e. the simulated course line of the examination object and/or the instrument”. Whereas, Zhao in view of Kaftan was relied on to teach the remainder of the claim, as explained in the rejection below.
Accordingly, the rejection of claims 1-15 under 35 U.S.C. 103 is maintained.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a memory facility” in claim 12
“an imaging modality” in claim 12
“a data processing facility” in claim 12
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Claim 12: “a memory facility” corresponds to figure 1 element 2 “The memory facility may be a data memory that may store and, when required, return the corresponding data records” (Paragraph [0033]) and “depicts a system according to embodiments including a medical instrument 1, a memory facility 2, an imaging modality 3, and a data processing facility 4.” (Paragraph [0044])”.
Claim 12: “an imaging modality” corresponds to figure 1 element 3 “the imaging modality includes an X-ray apparatus, an MRI apparatus, or an ultrasonography apparatus. The X-ray apparatus may for example be a C-arm device or an angiography device.” (Paragraph [0035]) and “depicts a system according to embodiments including a medical instrument 1, a memory facility 2, an imaging modality 3, and a data processing facility 4.” (Paragraph [0044]).
Claim 12: “a data processing facility” corresponds to figure 1 element 4 “a computer program or computer program product including commands that, on execution by the above-stated system, cause the latter to carry out a method likewise recited above.” (Paragraph [0036]) and “depicts a system according to embodiments including a medical instrument 1, a memory facility 2, an imaging modality 3, and a data processing facility 4.” (Paragraph [0044]).
Claim 13 is similarity interpreted for its dependency from claim 12.
Claim 14 further limits “an imaging modality” in claim 12 to the use of an X-ray apparatus, an MRI apparatus, a CT apparatus, or an ultrasonography apparatus.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1, 2, 4-6, 8, 10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2017/0311844 A1) (hereinafter, “Zhao”) in view of Kaftan et al. (US 7,646,903 B2) (hereinafter, “Kaftan”).
Regarding claim 1, Zhao discloses a method for locating a distal end of a medical instrument, the method comprising (Paragraph [0013] “method for determining a position of the medical device in the branched anatomical structure, the method comprising: determining a most likely match between information extracted from a sequence of images that have been captured from a perspective of a distal end of a medical device as the medical device moves through a plurality of lumens in the branched anatomical structure”):
providing a data set that describes a course (suitable navigational path determined by analyzing the acquired images of the anatomical structure or the generated 3-D computer model in Paragraph [0040] equates to data set that describes a course) of a main duct, of a first branch from the main duct, and of a second branch (branched anatomical structure having a plurality of natural body passages or lumens equate to the first branch and second branch) (Paragraph [0042] “the branched anatomical structure is a pair of lungs having a plurality of natural body passages or lumens including a trachea, bronchi, and bronchioles.”; Paragraph [0076] “if the centroid of the blob/lumen 1802 is “A”, the centroid of the blob/lumen 1803 is “B” and the centroid of the lumen 1801 through which the medical device is currently moving through is “C”, then as the distal end of the medical device is steered towards the right lumen 1803, the distance between the centroids of blobs/lumens 1801, 1803 is expected to get smaller. Thus, by tracking the distances between the centroids, A and B, of the blobs/lumens 1802, 1803, to the centroid, C, of the lumen 1801, the direction that the distal end of the medical device is being steered may be determined.) from the main duct (Paragraph [0038] “a set of images of a patient is acquired using an appropriate imaging technology from which a three-dimensional (3-D) computer model of the branched anatomical structure may be generated.”; Paragraph [0040] “surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer model so as to take into account any damage to the patient that the medical device 110 may cause as it moves towards the target as well as the shortest time and/or shortest path.”; (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure.”);
detecting a course of the medical instrument (sequence of tracklets in Paragraph [0048] equates to course of medical instrument) in the main duct and in one of the first branch or the second branch (Paragraph [0048] “A sequence of images that are topologically equivalent (e.g., a sequence in which the same set of blobs or lumens appears) is referred to herein as a “tracklet”. Thus, the path that the medical device moves through the branched anatomical structure is a sequence of tracklets.”);
bringing the course of the main duct and of the first branch and the second branch into register with the course of the medical instrument (Paragraph [0047] “the method compares information of topologies, feature attributes, behaviors, and/or relationships of lumens seen in a sequence of captured images with corresponding information of topologies, feature attributes, behaviors, and/or relationships of lumens in the computer model of the branched anatomical structure. A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”);
calculating a first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) of a portion of the course of the medical instrument in the first branch or the second branch [with a first portion of the course of the first branch] and a second similarity measure (score for each potential match in Paragraph [0078] equates to second similarity measure) of the portion of the course of the medical instrument in the first branch or the second branch [with a second portion of the course of the second branch] (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”; (Paragraph [0078] The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”); and
determining an item of location information about the portion of the course of the medical instrument from the first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) and second similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) (Paragraph [0047] “A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”; Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”; (Paragraph [0078] The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”).
However, Zhao fails to teach [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch.
Kaftan teaches [calculating a first similarity measure] with a first portion of the course of the first branch (path from a first tree-like structure in Column 4 [lines 18-23] equates to course of the first branch) and [calculating a second similarity measure] with a second portion of the course of the second branch (path from a second tree-like structure in Column 4 [lines 18-23] equates to course of the second branch) (Column 4 [lines 18-23] “acquire tree-like structures representing a physical object or model; extract a path from a first tree-like structure and a path from a second tree-like structure; compare the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determine if the paths match based on the similarity measurement. “).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao’s reference to include [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch taught by Kaftan’s reference. The motivation for doing so would have been to automatically and efficiently compare corresponding structures within tree-based models as suggested by Kaftan (see Kaftan, Column 9 [lines 18-25]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kaftan with Zhao to obtain the invention specified in claim 1.
Regarding claim 2, which claim 1 is incorporated, Zhao discloses wherein the data set is created from at least one X-ray image, magnetic resonance imaging (MRI), a computed tomography (CT) image, or ultrasonogram (Paragraph [0038] “a set of images of a patient is acquired using an appropriate imaging technology from which a three-dimensional (3-D) computer model of the branched anatomical structure may be generated. Examples of such an imaging technology include, but are not limited to, fluoroscopy, Magnetic Resonance Imaging, thermography, tomography, ultrasound, Optical Coherence Tomography, Thermal Imaging, Impedance Imaging, Laser Imaging, and nano-tube X-ray imaging.”; Paragraph [0040] “surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer model”).
Regarding claim 4, which claim 1 is incorporated, Zhao discloses wherein the courses of the main duct and of the branches are respective two-dimensional (2D) (Paragraph [0040] “The surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer mode”; Paragraph [0041] “a view of the auxiliary display screen 152 during navigation of the medical device 110 to a target area in an anatomical structure. The view may be either a 2-D or 3-D view of a computer model 420 of the branched anatomical structure and a computer model 410 of the medical device 110, which is updated in real-time as the medical device 110 moves through the anatomical structure.”).
Regarding claim 5, which claim 1 is incorporated, Zhao discloses wherein the course of the main duct and the course of the first branch and second branch are saved in the data set as concatenated line portions (sequence of tracklets in Paragraph [0082] equates to concatenated line portions) (Paragraph [0054] “FIGS. 18 and 19 illustrate two sequential images in a tracklet. In FIG. 18, a bifurcation comprising blobs/lumens 1802, 1803 is seen from a distance while the medical device is moving through a lumen 1801 towards the bifurcation. In FIG. 19, the same bifurcation is seen from a closer distance as the medical device has moved through the lumen 1801 with its distal end being steered towards blob/lumen 1803. In this example, blobs 1802, 1803 may be easily tracked between the images shown in FIGS. 18 and 19, because their positions relative to each other remain the same (i.e., blob 1802 continues to be the left lumen of the bifurcation and blob 1803 continues to be the right lumen of the bifurcation).”; Paragraph [0082] “determines a most likely match for the sequence of tracklets resulting from looping through blocks 501-510 by taking into account information of one or more adjacent tracklets in the sequence of tracklets making up the path of the medical device as it moves through the branched anatomical structure. Information of prior tracklets has been stored in the memory”).
Regarding claim 6, which claim 1 is incorporated, Zhao discloses wherein the first similarity measure and the second similarity measure (score for each potential match in Paragraph [0078] equates to first and second similarity measure) are in each case based on an angle of the portion of the course of the medical instrument in the first branch or second branch in a predetermined coordinate system (Paragraph [0067] “information of an angle φ indicating how much a line 2411 corresponding to the horizontal line of the received image (which has been captured within lumen 2413) deviates from a reference line 2412 that is perpendicular to a gravity vector may be provided by the orientation sensor 2223.”; Paragraph [0074] “angles between pairs of line segments may define other feature attributes of the image.”; Paragraph [0077- 0078] “The quantitative comparisons performed by the method in block 507 indicate how “close” the feature attributes of blobs in the current tracklet match corresponding feature attributes of blobs in synthetic images of the potential node matches…quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.).
Regarding claim 8, which claim 1 is incorporated, Zhao discloses wherein, on the basis of the similarity measures, the location information is calculated as a probability with which the medical instrument is situated in the first branch or second branch (Paragraph [0078] The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”), and where the probability or a value based on the probability is provided to a user (Paragraph [0041] “a view of the auxiliary display screen 152 during navigation of the medical device 110 to a target area in an anatomical structure. The view may be either a 2-D or 3-D view of a computer model 420 of the branched anatomical structure and a computer model 410 of the medical device 110, which is updated in real-time as the medical device 110 moves through the anatomical structure. Also shown is an indication 421 of the target. Thus, the auxiliary screen 152 assists the surgeon to steer the medical device 110 through the anatomical structure to the target.”).
Regarding claim 10, which claim 1 is incorporated, Zhao discloses wherein detection of the course of the medical instrument is carried out automatically by an imaging modality and a data processing facility (Paragraph [0041] “during navigation of the medical device 110 to a target area in an anatomical structure. The view may be either a 2-D or 3-D view of a computer model 420 of the branched anatomical structure and a computer model 410 of the medical device 110, which is updated in real-time as the medical device 110 moves through the anatomical structure. Also shown is an indication 421 of the target. Thus, the auxiliary screen 152 assists the surgeon to steer the medical device 110 through the anatomical structure to the target.”).
Regarding claim 12, Zhao discloses a system comprising: a medical instrument (Paragraph [0012] “medical system comprising: a memory storing information of a computer model of a branched anatomical structure; and a processor programmed to register the computer model to a medical device for determining a position of the medical device in the branched anatomical structure”);
a memory facility configured to provide a data set that describes a course (suitable navigational path determined by analyzing the acquired images of the anatomical structure or the generated 3-D computer model in Paragraph [0040] equates to data set that describes a course) of a main duct, of a first branch from the main duct, and of a second branch (branched anatomical structure having a plurality of natural body passages or lumens equate to the first branch and second branch) (Paragraph [0042] “the branched anatomical structure is a pair of lungs having a plurality of natural body passages or lumens including a trachea, bronchi, and bronchioles.”; Paragraph [0076] “if the centroid of the blob/lumen 1802 is “A”, the centroid of the blob/lumen 1803 is “B” and the centroid of the lumen 1801 through which the medical device is currently moving through is “C”, then as the distal end of the medical device is steered towards the right lumen 1803, the distance between the centroids of blobs/lumens 1801, 1803 is expected to get smaller. Thus, by tracking the distances between the centroids, A and B, of the blobs/lumens 1802, 1803, to the centroid, C, of the lumen 1801, the direction that the distal end of the medical device is being steered may be determined.) from the main duct (Paragraph [0012] “ a memory storing information of a computer model of a branched anatomical structure; and a processor programmed to register the computer model to a medical device…”; Paragraph [0040] “surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer model so as to take into account any damage to the patient that the medical device 110 may cause as it moves towards the target as well as the shortest time and/or shortest path.”; (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure.”)
an imaging modality configured to detect a course of the medical instrument (sequence of tracklets in Paragraph [0048] equates to course of medical instrument) in the main duct and in one of the first branch or the second branch (Paragraph [0048] “A sequence of images that are topologically equivalent (e.g., a sequence in which the same set of blobs or lumens appears) is referred to herein as a “tracklet”. Thus, the path that the medical device moves through the branched anatomical structure is a sequence of tracklets.”); and
a data processing facility configured for (Paragraph [0012] “and a processor programmed to register the computer model to a medical device for determining a position of the medical device in the branched anatomical structure by determining a most likely match between information which has been extracted from a sequence of images that has been captured by an image capturing device”):
bringing the course of the main duct and of the first branch and second branch into register with the course of the medical instrument (Paragraph [0047] “the method compares information of topologies, feature attributes, behaviors, and/or relationships of lumens seen in a sequence of captured images with corresponding information of topologies, feature attributes, behaviors, and/or relationships of lumens in the computer model of the branched anatomical structure. A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”);
calculating a first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) of a portion of the course of the medical instrument in the first branch or the second branch [with a first portion of the course of the first branch] and a second similarity measure (score for each potential match in Paragraph [0078] equates to second similarity measure) of the portion of the course of the medical instrument in the first branch or the second branch [with a second portion of the course of the second branch] (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”); and
determining an item of location information about the portion of the course of the medical instrument from the first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) and the second similarity measure (score for each potential match in Paragraph [0078] equates to second similarity measure) (Paragraph [0047] “A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”; Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”; (Paragraph [0078] The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”).
However, Zhao fails to teach [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch.
Kaftan teaches [calculating a first similarity measure] with a first portion of the course of the first branch (path from a first tree-like structure in Column 4 [lines 18-23] equates to course of the first branch) and [calculating a second similarity measure] with a second portion of the course of the second branch (path from a second tree-like structure in Column 4 [lines 18-23] equates to course of the second branch) (Column 4 [lines 18-23] “acquire tree-like structures representing a physical object or model; extract a path from a first tree-like structure and a path from a second tree-like structure; compare the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determine if the paths match based on the similarity measurement. “).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao’s reference to include [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch taught by Kaftan’s reference. The motivation for doing so would have been to automatically and efficiently compare corresponding structures within tree-based models as suggested by Kaftan (see Kaftan, Column 9 [lines 18-25]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kaftan with Zhao to obtain the invention specified in claim 12.
Regarding claim 13, which claim 12 is incorporated, Zhao discloses wherein the medical instrument is part of a catheter, a sphincterotome, a duodenoscope, or a guidewire (Paragraph [0033] “The medical device 110 may be an endoscope, catheter or other medical instrument having a flexible body and steerable tip.”).
Regarding claim 14, which claim 12 is incorporated, Zhao discloses wherein the imaging modality comprises an X-ray apparatus, an a magnetic resonance imaging (MRI), a computed tomography (CT) apparatus, or an ultrasonography apparatus (Paragraph [0038] “a set of images of a patient is acquired using an appropriate imaging technology from which a three-dimensional (3-D) computer model of the branched anatomical structure may be generated. Examples of such an imaging technology include, but are not limited to, fluoroscopy, Magnetic Resonance Imaging, thermography, tomography, ultrasound, Optical Coherence Tomography, Thermal Imaging, Impedance Imaging, Laser Imaging, and nano-tube X-ray imaging.”; Paragraph [0040] “surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer model”).
Regarding claim 15, Zhao discloses a non-transitory computer implemented storage medium that stores machine-readable instructions for locating a distal end of a medical instrument executable by at least one processor, the machine-readable instructions comprising (Paragraph [0032] “the sensor(s) processor 130, image processor 140, display processor 150, and main processor 160 may be implemented in a single processor or their respective functions distributed among a plurality of processors, wherein each of such processors may be implemented as hardware, firmware, software or a combination thereof. As used herein, the term processor is understood to include interface logic and/or circuitry for translating and/or communicating signals into and/or out of the processor as well as conventional digital processing logic. The memory 161 may be any memory device or data storage system as conventionally used in computer systems.”):
providing a data set that describes a course (suitable navigational path determined by analyzing the acquired images of the anatomical structure or the generated 3-D computer model in Paragraph [0040] equates to data set that describes a course) of a main duct, of a first branch from the main duct, and of a second branch from (branched anatomical structure having a plurality of natural body passages or lumens equate to the first branch and second branch) (Paragraph [0042] “the branched anatomical structure is a pair of lungs having a plurality of natural body passages or lumens including a trachea, bronchi, and bronchioles.”; Paragraph [0076] “if the centroid of the blob/lumen 1802 is “A”, the centroid of the blob/lumen 1803 is “B” and the centroid of the lumen 1801 through which the medical device is currently moving through is “C”, then as the distal end of the medical device is steered towards the right lumen 1803, the distance between the centroids of blobs/lumens 1801, 1803 is expected to get smaller. Thus, by tracking the distances between the centroids, A and B, of the blobs/lumens 1802, 1803, to the centroid, C, of the lumen 1801, the direction that the distal end of the medical device is being steered may be determined.) from the main duct (Paragraph [0038] “a set of images of a patient is acquired using an appropriate imaging technology from which a three-dimensional (3-D) computer model of the branched anatomical structure may be generated.”; Paragraph [0040] “surgeon may determine a suitable navigational path to a target by analyzing the acquired images of the anatomical structure or the generated 3-D computer model so as to take into account any damage to the patient that the medical device 110 may cause as it moves towards the target as well as the shortest time and/or shortest path.”;(Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure.”);
detecting a course of the medical instrument in the main duct and in one of the first branch or the second branch (Paragraph [0048] “A sequence of images that are topologically equivalent (e.g., a sequence in which the same set of blobs or lumens appears) is referred to herein as a “tracklet”. Thus, the path that the medical device moves through the branched anatomical structure is a sequence of tracklets.”);
bringing the course of the main duct and of the first branch and the second branch into register with the course of the medical instrument (Paragraph [0047] “the method compares information of topologies, feature attributes, behaviors, and/or relationships of lumens seen in a sequence of captured images with corresponding information of topologies, feature attributes, behaviors, and/or relationships of lumens in the computer model of the branched anatomical structure. A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”);
calculating a first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) of a portion of the course of the medical instrument in the first branch or the second branch [with a first portion of the course of the first branch] and a second similarity measure (score for each potential match in Paragraph [0078] equates to second similarity measure) of the portion of the course of the medical instrument in the first branch or the second branch [with a second portion of the course of the second branch] (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”); and
determining an item of location information about the portion of the course of the medical instrument from the first similarity measure (score for each potential match in Paragraph [0078] equates to first similarity measure) and second similarity measure (score for each potential match in Paragraph [0078] equates to second similarity measure) (Paragraph [0047] “A most likely match between a path of lumens seen in the sequence of captured images and a path of lumens in the computer model of the branched anatomical structure is then determined to indicate which lumen of the branched anatomical structure the medical device is currently in.”; Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”; (Paragraph [0078] The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”).
However, Zhao fails to teach [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch.
Kaftan teaches [calculating a first similarity measure] with a first portion of the course of the first branch (path from a first tree-like structure in Column 4 [lines 18-23] equates to course of the first branch) and [calculating a second similarity measure] with a second portion of the course of the second branch (path from a second tree-like structure in Column 4 [lines 18-23] equates to course of the second branch) (Column 4 [lines 18-23] “acquire tree-like structures representing a physical object or model; extract a path from a first tree-like structure and a path from a second tree-like structure; compare the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determine if the paths match based on the similarity measurement. “).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao’s reference to include [calculating a first similarity measure] with a first portion of the course of the first branch and [calculating a second similarity measure] with a second portion of the course of the second branch taught by Kaftan’s reference. The motivation for doing so would have been to automatically and efficiently compare corresponding structures within tree-based models as suggested by Kaftan (see Kaftan, Column 9 [lines 18-25]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kaftan with Zhao to obtain the invention specified in claim 15.
Claims 3, 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2017/0311844 A1) (hereinafter, “Zhao”) in view of Kaftan et al. (US 7,646,903 B2) (hereinafter, “Kaftan”) as applied to claim 1 above, and further in view of Naik et al. ("Determination of the optimal length of insertion for central venous catheterization via axillary vein cannulation using preoperative chest X-ray-A prospective feasibility study." Journal of Anaesthesiology Clinical Pharmacology 39.2 (2023): 215-219.) (hereinafter, “Naik”).
Regarding claim 3, which claim 1 is incorporated, Zhao and Kaftan fail to teach wherein a length of the medical instrument is detected and the method is only carried out once the length of the medical instrument is greater than a length of the course of the main duct of the data set.
Naik teaches wherein a length of the medical instrument is detected and the method is only carried out once the length of the medical instrument is greater than a length of the course of the main duct of the data set (Page 216 left column first paragraph “A correct length to be inserted, thus, needs to be pre-determined before inserting the catheter.”; Page 216 left column paragraph 1 of Material and Method “The right clavicular length—defined as the length from the acromioclavicular joint to the sternoclavicular joint, ‘A,’ was calculated using the scale on the radiograph. The vertical distance between the sternal head of the clavicle and the carina was designated as ‘B.’ Considering that the usual insertion site is at the junction of the medial 2/3rd and lateral 1/3rd of the clavicle, the calculated length of insertion, ‘X,’ was obtained by adding 2/3rd of ‘A’ to ‘B’”; Page 216 right column paragraph 1 of Central venous catheter insertion technique…“The distance between the skin to AxV was measured and this distance was designated as ‘Y,’ which was added to the calculated measurement ‘X’ [Figure 1b]. The optimal length of the catheter insertion ‘L’ was calculated as: Optimal length (L) = Calculated Measurement (X) + Depth of vein from skin (Y)”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao in view of Kaftan to include wherein a length of the medical instrument is detected and the method is only carried out once the length of the medical instrument is greater than a length of the course of the main duct of the data set taught by Naik’s reference. The motivation for doing so would have been to accurately estimate the length of insertion to ensure the correct catheter placement as suggested by Naik (see Naik, Page 219 left column paragraph 2).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Naik with Zhao and Kaftan to obtain the invention specified in claim 3.
Regarding claim 9, which claim 1 is incorporated, Zhao discloses the similarity measures and is provided to a user (Paragraph [0041] “a view of the auxiliary display screen 152 during navigation of the medical device 110 to a target area in an anatomical structure. The view may be either a 2-D or 3-D view of a computer model 420 of the branched anatomical structure and a computer model 410 of the medical device 110, which is updated in real-time as the medical device 110 moves through the anatomical structure. Also shown is an indication 421 of the target. Thus, the auxiliary screen 152 assists the surgeon to steer the medical device 110 through the anatomical structure to the target.”; Paragraph [0046] “for registering a computer model of a branched anatomical structure to a medical device so that a position of the medical device in the branched anatomical structure may be determined as the medical device moves through the branched anatomical structure. In this case, the position of the medical device in the branched anatomical structure indicates the lumen of the branched anatomical structure that the medical device is currently in.”; Paragraph [0078] “The quantitative comparisons are preferably converted to a probability or confidence score for each potential match or hypothesis.”)
However, Zhao and Kaftan fail to teach wherein a risk measure is determined as a function of a length of the medical instrument in the first branch or second branch and [the similarity measures and is provided to a user].
Naik teaches wherein a risk measure is determined as a function of a length of the medical instrument in the first branch or second branch and [the similarity measures and is provided to a user] (Page 215 end of right column continued to page 216 beginning of left column “The ideal position of the catheter is in a large central vein outside the pericardial reflection and parallel to its long axis, such that it does not impinge on the vessel/heart wall.[2] It is imperative that the tip be proximal to the boundaries of the pericardial sac to prevent cardiac tamponade.[3] On the other hand, if the catheters are placed too proximally, there is an elevated risk of thrombosis.”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao in view of Kaftan to include wherein a risk measure is determined as a function of a length of the medical instrument in the first branch or second branch and [the similarity measures and is provided to a user] taught by Naik’s reference. The motivation for doing so would have been to accurately estimate the length of insertion to ensure the correct catheter placement as suggested by Naik (see Naik, Page 219 left column paragraph 2).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Naik with Zhao and Kaftan to obtain the invention specified in claim 9.
Regarding claim 11, which claim 1 is incorporated, Zhao discloses the detection of the course of the medical instrument and used for calculating the similarity measures (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”)
However, Zhao and Kaftan fail to teach wherein a length of the medical instrument is detected independently [of the detection of the course of the medical instrument and used for calculating the similarity measures].
Naik teaches wherein a length of the medical instrument is detected independently [of the detection of the course of the medical instrument and used for calculating the similarity measures] (Page 216 left column first paragraph “A correct length to be inserted, thus, needs to be pre-determined before inserting the catheter.”; Page 216 left column paragraph 1 of Material and Method “The right clavicular length—defined as the length from the acromioclavicular joint to the sternoclavicular joint, ‘A,’ was calculated using the scale on the radiograph. The vertical distance between the sternal head of the clavicle and the carina was designated as ‘B.’ Considering that the usual insertion site is at the junction of the medial 2/3rd and lateral 1/3rd of the clavicle, the calculated length of insertion, ‘X,’ was obtained by adding 2/3rd of ‘A’ to ‘B’”; Page 216 right column paragraph 1 of Central venous catheter insertion technique…“The distance between the skin to AxV was measured and this distance was designated as ‘Y,’ which was added to the calculated measurement ‘X’ [Figure 1b]. The optimal length of the catheter insertion ‘L’ was calculated as: Optimal length (L) = Calculated Measurement (X) + Depth of vein from skin (Y)”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao in view of Kaftan to include wherein a length of the medical instrument is detected independently [of the detection of the course of the medical instrument and used for calculating the similarity measures] taught by Naik’s reference. The motivation for doing so would have been to accurately estimate the length of insertion to ensure the correct catheter placement as suggested by Naik (see Naik, Page 219 left column paragraph 2).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Naik with Zhao and Kaftan to obtain the invention specified in claim 11.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2017/0311844 A1) (hereinafter, “Zhao”) in view of Kaftan et al. (US 7,646,903 B2) (hereinafter, “Kaftan”) as applied to claim 1 above, and further in view of Breininger et al (US 2020/0380705 A1) (hereinafter; “Breininger”).
Regarding claim 7, which claim 1 is incorporated, Zhao teaches wherein the first similarity measure and the second similarity measure [are in each case based on a Hausdorff metric] between the portion of the course of the medical instrument in the first branch or second branch [and a respective portion of the course of a respective branch] (Paragraph [0061] “Each tracklet identified in the first part of the method may correspond to a different lumen of the branched anatomical structure. Thus, a sequence of tracklets which have been identified by looping through blocks 501-510 as the medical device moves through the branched anatomical structure provide a pictorial history of images captured as the medical device moves along a path in the branched anatomical structure. Each time a new tracklet is identified, it is added to the sequence of tracklets and the updated sequence of tracklets is matched against the potential paths that the medical device may take as it moves through lumens of the branched anatomical structure. It may happen that two or more potential paths are close matches to the updated sequence. In that case, although the closest match may be designated the most likely match up to that point, when a next tracklet is identified after looping back through blocks 501-504, the updated sequence of tracklets may match more closely with one of the other potential matches to indicate a “true” match versus the previously identified “false” match. Thus, a “false match” error is self-corrected in the method.”).
However, Zhao fails to teach measure are in each case based on a Hausdorff metric and a respective portion of the course of a respective branch.
Kaftan teaches measure are in each case based on a respective portion of the course of a respective branch (Column 4 [lines 18-23] “acquire tree-like structures representing a physical object or model; extract a path from a first tree-like structure and a path from a second tree-like structure; compare the paths of the first and second tree-like structures by computing a similarity measurement for the paths; and determine if the paths match based on the similarity measurement. “).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao’s reference to include measure are in each case based on a respective portion of the course of a respective branch taught by Kaftan’s reference. The motivation for doing so would have been to automatically and efficiently compare corresponding structures within tree-based models as suggested by Kaftan (see Kaftan, Column 9 [lines 18-25]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results.
However, Zhao and Kaftan fail to teach measure are in each case based on a Hausdorff metric.
Breininger teaches measure are in each case based on a Hausdorff metric (Paragraph [0027] “the registration facility is configured to perform the registration as a 2D-3D registration by minimizing a 2D form or 2D formulation of the line distance metric, for example, the modified 2D Hausdorff distance, and to use a 2D projection image as the live image and to determine the at least one simulated course line, i.e. the simulated course line of the examination object and/or the instrument”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Zhao in view of Kaftan to include measure are in each case based on a Hausdorff metric taught by Breininger’s reference. The motivation for doing so would have been to provide a quick and accurate manner for achieving registrations between corresponding structures as suggested by Breininger (see Breininger, Paragraph [0015]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kaftan and Breininger with Zhao to obtain the invention specified in claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fine et al. (US 11,657,330 B2) discloses a system for guiding an invasive medical device relative to anatomical structures by determining the current orientation and position of the medical device relative to the one or more anatomical structures within the patient.
Schmidt et al. (US 2009/0326369 A1) discloses a method for determine the position of a medical instrument, wherein the position of the instrument head is calculated from structural data, length data and the relative position between a reference point and the vascular system.
Landon et al. (US 2022/0160430 A1) discloses a method for generating a model of a bone based on a 2D image which is representative of a bone identified from a library.
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/UROOJ FATIMA/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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