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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 39, 46, 48, 49, 56 and 58 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al., “Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images”, Computerized Medical Imaging and Graphics, July 2006, vol. 30, issue 5, pp 299-313, cited by applicant submitted IDS, dated 07/08/2024.
As to claim 39, Zhou et al. discloses a system comprising: a memory (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster, inherently a memory storing instructions must be present for the CPU or PC cluster to execute in the instructions for processing the steps 1-5 in figure 1 at page 301) storing instructions; and a processor (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster) communicatively coupled to the memory (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster, inherently a memory storing instructions must be present for the CPU or PC cluster to execute in the instructions for processing the steps 1-5 in figure 1 at page 301) and configured to execute the instructions to perform a process comprising: determining, based on a set of seeds (see 3.2.1, “seed points”, see 3.4, “seed points”, and see 3.5.2, “We have classified the bronchus and vessels into five lobar groups by the above steps. Based on that information, we divide the whole lung into five lobar regions using a Voronoi division algorithm.”, seeds are inherently and an implicit feature of Voronoi division, i.e., Voronoi diagram divides a plane into regions based on distance to a set of points called seeds, i.e., Voronoi division partitions a plane into regions where each region contains all points closest to a specific seed point, forming Voronoi cells) determined from data representative of a labeled first tubular structure (see figure 1, i.e., “pulmonary vessels”) comprising a blood vessel structure (see page 305, 2nd col. i.e., “blood vessels”) and a labeled second tubular structure (i.e., “Lobar and segmental bronchi, see page 300, 1st col.) comprising an airway vessel structure (see page 300 1st col. at 2 4th par. “bronchial airway”) in a model of at least a portion of an anatomical structure (see figure 1, i.e., “initial region segmentation”) comprising a lung (see figure 1, and see page 300, 1st col. at 2 “Lung”), a partitioning of the model into segments comprising bronchopulmonary segments (see page 312, 1st col. i.e., “lung segments” and see pages 306, 2nd col. through page 307, 1st col., i.e., lobar regions and see figure 1, step 5, “lobar regions”); and outputting data representative of the segments (see figure 1, “output” and see figure 8h, i.e., final lung lobes).
As to claim 46, Zhou et al. discloses wherein: the partitioning defines boundaries of the segments; and each labeled tubular branch of the labeled first tubular structure is contained within a respective set of the boundaries of the segments (see page 306, 2nd col. “boundary”, see page 307, 1st col. “boundary”, see page 311, 1st col. and 2nd col., i.e., “lobar boundaries”).
As to claim 48, Zhou et al. discloses a method (see figure 1) comprising: determining, by a computing system (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster)) and based on a set of seeds (see 3.2.1, “seed points”, see 3.4, “seed points”, and see 3.5.2, “We have classified the bronchus and vessels into five lobar groups by the above steps. Based on that information, we divide the whole lung into five lobar regions using a Voronoi division algorithm.”, seeds are inherently and an implicit feature of Voronoi division, i.e., Voronoi diagram divides a plane into regions based on distance to a set of points called seeds, i.e., Voronoi division partitions a plane into regions where each region contains all points closest to a specific seed point, forming Voronoi cells) determined from data representative of a labeled first tubular structure (see figure 1, i.e., “pulmonary vessels”) comprising a blood vessel structure (see page 305, 2nd col. i.e., “blood vessels”) in a model of at least a portion of an anatomical structure (see figure 1, i.e., “initial region segmentation”) comprising a lung (see figure 1, and see page 300, 1st col. at 2 “Lung”), a partitioning of the model into segments comprising bronchopulmonary segments (see page 312, 1st col. i.e., “lung segments” and see pages 306, 2nd col. through page 307, 1st col., i.e., lobar regions and see figure 1, step 5, “lobar regions”); and outputting, by the computing system (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster)), data representative of the segments (see figure 1, “output” and see figure 8h, i.e., final lung lobes).
As to claim 49, Zhou et al. discloses wherein the set of seeds (see 3.2.1, “seed points”, see 3.4, “seed points”, and see 3.5.2, “We have classified the bronchus and vessels into five lobar groups by the above steps. Based on that information, we divide the whole lung into five lobar regions using a Voronoi division algorithm.”, seeds are inherently and an implicit feature of Voronoi division, i.e., Voronoi diagram divides a plane into regions based on distance to a set of points called seeds, i.e., Voronoi division partitions a plane into regions where each region contains all points closest to a specific seed point, forming Voronoi cells) is determined further based on data representative of an additional labeled tubular structure in the model (see figure 1, i.e., “pulmonary vessels”).
As to claim 56, Zhou et al. discloses wherein: the partitioning defines boundaries of the segments; and each labeled tubular branch of the labeled tubular structure is contained within a respective set of the boundaries of the segments (see page 306, 2nd col. “boundary”, see page 307, 1st col. “boundary”, see page 311, 1st col. and 2nd col., i.e., “lobar boundaries”).
As to claim 58, Zhou et al. discloses a non-transitory computer-readable medium storing instructions (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster, inherently a non-transitory computer-readable medium storing instructions must be present for the CPU or PC cluster to execute in the instructions for processing the steps 1-5 in figure 1 at page 301) that, when executed, direct a processor (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster) of a computing device to perform a process (see page 311, 2nd col. at section 4.6, 2nd par. a computer (CPU: AMD Opteron ™ Model 242 or PC cluster) comprising: determining, based on a set of seeds (see 3.2.1, “seed points”, see 3.4, “seed points”, and see 3.5.2, “We have classified the bronchus and vessels into five lobar groups by the above steps. Based on that information, we divide the whole lung into five lobar regions using a Voronoi division algorithm.”, seeds are inherently and an implicit feature of Voronoi division, i.e., Voronoi diagram divides a plane into regions based on distance to a set of points called seeds, i.e., Voronoi division partitions a plane into regions where each region contains all points closest to a specific seed point, forming Voronoi cells) determined from data representative of a labeled tubular structure (see figure 1, i.e., “pulmonary vessels”) comprising a blood vessel structure (see page 305, 2nd col. i.e., “blood vessels”) and an additional labeled tubular structure (i.e., “Lobar and segmental bronchi, see page 300, 1st col.) comprising an airway vessel structure (see page 300 1st col. at 2 4th par. “bronchial airway”) in a model of at least a portion of an anatomical structure (see figure 1, i.e., “initial region segmentation”) comprising a lung (see figure 1, and see page 300, 1st col. at 2 “Lung”), a partitioning of the model into segments comprising bronchopulmonary segments (see page 312, 1st col. i.e., “lung segments” and see pages 306, 2nd col. through page 307, 1st col., i.e., lobar regions and see figure 1, step 5, “lobar regions”); and outputting, data representative of the segments (see figure 1, “output” and see figure 8h, i.e., final lung lobes).
Allowable Subject Matter
Claims 40-45, 47, 50-55 and 57 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 40, the closest prior art of record, namely, Zhou et al. discussed above, does not disclose, teach or suggest, wherein the determining the partitioning comprises: determining a first partitioning based on the set of seeds; adjusting the first partitioning to determine a set of augmented seeds; and determining, based on the set of augmented seeds, a second partitioning of the model into the segments, as recited in claim 40.
Claims 41-45 are objected to because claims 41-45 are dependent on objected to claim 40 discussed above.
Regarding claim 47, the closest prior art of record, namely, Zhou et al. discussed above, does not disclose, teach or suggest, further comprising: determining a volume of one or more of the segments; and determining, based on the volume, an estimation of function loss based on a removal of one or more of the segments, as claimed in claim 47.
Regarding claim 50, the closest prior art of record, namely, Zhou et al. discussed above, does not disclose, teach or suggest, wherein the determining the partitioning comprises: determining a first partitioning based on the set of seeds; adjusting the first partitioning to determine a set of augmented seeds; and determining, based on the set of augmented seeds, a second partitioning of the model into the segments, as recited in claim 50.
Claims 51-55 are objected to because claims 51-55 are dependent on objected to claim 50 discussed above.
Regarding claim 57, the closest prior art of record, namely, Zhou et al. discussed above, does not disclose, teach or suggest, further comprising: determining, by the computing system, a volume of one or more of the segments; and determining, by the computing system and based on the volume, an estimation of function loss based on a removal of one or more of the segments, as claimed in claim 57.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Buelow et al. (US 8,805,044 B2) teaches a method of identifying at least part of a pulmonary artery tree (402) comprises receiving (102) a bronchial tree structure (500) and receiving (104) a pulmonary vessel structure (400). A pair of a first bronchial segment (602) and a first vessel segment (604) is identified (106), wherein the first bronchial segment and the first vessel segment are adjacent with respect to position and orientation. The first vessel segment is identified (108) as arterial segment of the pulmonary artery tree. A spatial transformation is applied (110) such that the first bronchial segment and the first vessel segment substantially coincide (602'). Respective further vessel segments (606, 608) are identified (112) adjacent to bronchial segments (610, 612), wherein the bronchial segments are comprised in the bronchial tree (see the abstract).
NAGATA et al. (US 2020/0242776 A1) teaches a medical image processing apparatus includes: a memory; and a processor configured to execute a process. The process includes: acquiring volume data including one or more organs; and performing processing relating to segment division of the one or more organs. The performing includes: acquiring a first tree structure included in the one or more organs; acquiring a second tree structure included in the one or more organs; and generating a plurality of first segments obtained by dividing the one or more organs based on the first tree structure and the second tree structure. At least a part of a branch of the first tree structure passes through a central portion of the plurality of first segments, and at least a part of a branch of the second tree structure passes along a boundary between the plurality of first segments (see the abstract).
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/DOV POPOVICI/ Primary Examiner, Art Unit 2681
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