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 .
Applicant’s Response
In Applicant’s Response dated 2/3/26, the Applicant amended claims 1, 4, 12, 13, canceled claim 3 and argued Claims previously rejected in the Office Action dated 11/26/25.
In light of the Applicant’s amendments and remarks, the 35 USC 101 rejections have been withdrawn.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claims 1, 2 and 4-13 are rejected under 35 U.S.C. 103 as being unpatentable over Crawford et al., United States Patent Publication 2022/0270762 (hereinafter “Crawford”), in view of Gupta et al., United States Patent Publication 20120207378 (hereinafter “Gupta”).
Claim 1:
Crawford discloses:
An image processing apparatus comprising at least one processor,
wherein the processor is configured to:
receive a medical image (see paragraph [0077]). Crawford teaches receiving a medical image;
derive running vectors representing running directions of the plurality of tubular structures (see paragraphs [0013] and [0014]), Crawford teaches creating directional vectors representing certain anatomical features found in the medical image; and
display the medical image including the plurality of tubular structures being separate along the running direction thereof on a display (see paragraph [0081] and figures 7A-7D, 8A-8D). Crawford teaches displaying a medical image with tubular structures of an aortas/vessels.
Crawford fails to expressly teaches separating the structures using the running vectors to separate the tubular structures.
Gupta discloses:
derive, at each pixel of the plurality of tubular structures, running vectors representing running directions of the plurality of tubular structures (see paragraphs [0005] and [0014]) Gupta teaches creating directional vectors representing the direction of the blood vessels within the image; and
separate the plurality of tubular structures using the running vectors (see paragraph [0013]). Gupta teaches segmenting the vessels using the identified vectors in the images.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method disclosed by Crawford to include separating the tubular structures using the running vectors for the purpose of automatic segmentation to increase efficiency of clinicians, accurate detection of blood vessels, as taught by Gupta.
Claim 2:
Crawford discloses:
wherein the processor is configured to derive the running vector using a trained model for deriving the running vector at each pixel of the plurality of tubular structures from the medical image (see paragraph [0066] and [0067]). Crawford teaches using machine learning to segment the image based on the pixels of the anatomical features.
Claim 4:
Crawford discloses:
obtain a direction vector from a first pixel to a second pixel (see paragraphs [0084] and [0085]). Crawford teaches obtaining a direction based on a start and end point which could be a pixel; and
based on an angle between the direction vector and the running vector at least one of the first pixel or the second pixel, determine the likelihood that the same label is assigned to the first pixel and the second pixel to separate the plurality of tubular structures (see paragraphs [0013] and [0023]). Crawford teaches using an anatomical feature identification algorithm to determine based on a features like angles to match similar features with the likelihood that they will be labeled the similarity. Crawford also teaches using the running travel vector and directional vector to determine if they will be the same label.
Claims 5-6:
Crawford discloses:
wherein the processor is configured to, in separating the plurality of tubular structures using a graph cut process by selecting a pixel group including N (> 3) pixels are in the same direction or continuously change and which are adjacent to each other and minimizing an N-th order energy with labels of the pixels included in the pixel group as variables, the variables being represented by 0 or 1, set the N-th order energy to be lower in a case where all of variables corresponding to the pixels included in the pixel group are 0 or all of the variables corresponding to the pixels included in the pixel group are 1 than in a case where all of the variables are not 0 and all of the variables are not 1; and wherein the processor is configured to, in separating the plurality of tubular structures using a graph cut process by selecting a pixel group including N (>3) pixels having a shortest weighted path based on the likelihood that the same label is assigned and minimizing an N-th order energy with labels of the pixels included in the pixel group as variables, the variables being represented by 0 or 1, set the N-th order energy to be lower in a case where all of variables corresponding to the pixels included in the pixel group are 0 or all of the variables corresponding to the pixels included in the pixel group are 1 than in a case where all of the variables are not 0 and all of the variables are not 1 (see paragraph [0072] and [0073]). Crawford teaches using a graph cut process that identifies the anatomical features as nodes on a graph, has method that groups similar anatomical structures that have similar properties. For example, Crawford recites Anatomical feature identification module may then use an anatomical feature identification algorithm to explore the anatomical knowledge dataset to identify the patient specific anatomical features within the medical images by establishing links between the grouped labeled pixels with existing knowledge within the anatomical knowledge dataset. For example, the existing knowledge may include known information regarding various anatomic features such as tissue types, e.g., bone, blood vessel, or organ, etc., represented as nodes within a graph database of the anatomical knowledge dataset. Crawford also teaches anatomical feature identification module 116 initially may group the pixels labeled by segmentation module 114, e.g., by establishing links between the different labeled/classified pixels based on similarities between the labeled pixels. For example, all the pixels labeled “bone” may be grouped/linked together in a first group, all the pixels labeled “organ” may be grouped/linked together in a second group, and all the pixels labeled “blood vessel” may be grouped/linked together a third group.
Crawford fails to expressly teaches separating the structures using the running vectors to separate the tubular structures.
Gupta discloses:
running vectors representing running directions of the plurality of tubular structures (see paragraphs [0005] and [0014]) Gupta teaches creating directional vectors representing the direction of the blood vessels within the image; and
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method disclosed by Crawford to include tubular structures using the running vectors for the purpose of automatic segmentation to increase efficiency of clinicians, accurate detection of blood vessels, as taught by Gupta.
Claim 7:
Crawford discloses:
wherein the processor is configured to:
derive a running vector at a plurality of center pixels along a center of the plurality of tubular structures (see paragraph [0013]). Crawford teaches generating the directional vector based on the center pixels of the anatomical structures;
derive a shortest path tree from an origin of a class representing each of the plurality of tubular structures such that an angle formed by an edge connecting the plurality of center pixels and the running vector is minimized (see paragraph [0013]). Crawford teaches deriving the shortest path tree by determining start and end points of the isolated anatomical feature and a direction of travel from the start point to the end point; raycasting at predefined intervals along an axis in at least three directions perpendicular to the direction of travel and determining distances between intersections of each ray cast and the 3D surface mesh model; calculating a center point at each interval by triangulating the distances between intersections of each ray cast and the 3D surface mesh model; adjusting the direction of travel at each interval based on a directional vector between adjacent calculated center points, such that raycasting at the predefined intervals occur in at least three directions perpendicular to the adjusted direction of travel at each interval; and calculating a centerline of the isolated anatomical feature based on the calculated center points from the start point to the end point; and
separate the plurality of tubular structures by cutting the shortest path tree such that the plurality of center pixels are in the same class as an origin having a closer path and a higher likelihood that the same label is assigned (see paragraph [0015]). Crawford teaches determining start and end points of the isolated anatomical feature; taking slices at predefined intervals along an axis from the start point to the end point; calculating a cross-sectional area of each slice defined by a perimeter of the isolated anatomical feature; and generating a heat map of the isolated anatomical feature based on the cross-sectional area of each slice.
Crawford fails to expressly teaches separating the structures using the running vectors to separate the tubular structures.
Gupta discloses:
separate the plurality of tubular structures such that the plurality of center pixels are in the same class as an origin (see paragraph [0013]). Gupta teaches segmenting the vessels using the identified vectors in the images.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method disclosed by Crawford to include separating the tubular structures using the running vectors for the purpose of automatic segmentation to increase efficiency of clinicians, accurate detection of blood vessels, as taught by Gupta.
Claim 8:
Crawford fails to expressly teaches separating the structures using the running vectors to separate the tubular structures.
Gupta discloses:
wherein the processor is configured to separate pixels other than pixels along the running vectors into tubular structures different from each other (see paragraph [0013]). Gupta teaches segmenting the vessels using the identified vectors in the images.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method disclosed by Crawford to include separating the tubular structures using the running vectors for the purpose of automatic segmentation to increase efficiency of clinicians, accurate detection of blood vessels, as taught by Gupta.
Claim 9:
Crawford discloses:
wherein the processor is configured to separate the plurality of tubular structures such that a boundary of the plurality of tubular structures is derived between pixels other than pixels where the running vectors intersect each other (see figure 8C, [0011], [0118] and [0121]). Crawford teaches separating anatomical feature such that it determines the boundaries such as the start and end of the feature.
Claim 10:
Crawford discloses:
wherein the processor is configured to separate the plurality of tubular structures using a trained model in which machine learning is performed so as to minimize a loss in a direction in which the running vectors are continuous, based on the medical image and the running vectors (see paragraph [0066], [0067], [0127] and [0128]). Crawford teaches using machine learning to segment the image based on the pixels of the anatomical features. Crawford also teaches improving functions to minimize loss during machine learning to segment the image.
Claim 11:
Crawford discloses:
wherein the plurality of tubular structures include at least two of an artery, a vein, a portal vein, a ureter, or a nerve (see paragraph [0111] and [0114]). Crawford teaches multiple arteries.
Claims 12, 13:
Although Claim 12 is a method claim and Claim 13 is a non-transitory computer-readable claim, they are interpreted and rejected for the same reasons as the apparatus of Claim 1.
Response to Arguments
Applicant's arguments filed 2/3/26 have been fully considered but they are not persuasive.
101 Rejections:
Applicant argues Accordingly, these operations cannot be practically performed in the human mind and are fundamentally different from mental observation, judgment, or evaluation.
The Examiner agrees.
Based on the Applicants amendments, the 35 USC 101 rejections have been withdrawn.
102 Rejections
Applicant argues Crawford's direction-related information is merely used to define measurement paths or cutting planes along an already isolated anatomical feature, and is not used to distinguish or separate multiple anatomical structures within an image.
The Examiner agrees that Crawford is used for measurement paths and cutting planes.
The Examiner introduced new art, Gupta, to teach separating the vessels using directional vectors (see the above rejection for Claim 1). The combination of Crawford and Gupta teaches the elements of the claims.
Applicant argues Crawford does not disclose or suggest determining, based on an angle formed between two vectors, whether two pixels should be assigned the same label, nor using such an angle-based determination to separate tubular structures. Therefore, Applicant respectfully submits that "based on an angle between the direction vector and the running vector at least one of the first pixel or the second pixel, determine the likelihood that the same label is assigned to the first pixel and the second pixel to separate the plurality of tubular structures" as expressly recited in Claim 4 is not disclosed or suggested by Crawford.
The Examiner disagrees.
Crawford automatically process the medical images using a segmentation algorithm to label pixels of the medical images and to generate scores indicative of a likelihood that the pixels were labeled correctly; use an anatomical feature identification algorithm to probabilistically match associated groups of the labeled pixels against an anatomical knowledge dataset to classify one or more patient specific anatomical features within the medical images (see paragraph [0023]). Crawford using a mathematical feature to determine if the based on segmentation features (such as angles ) if the structures are labeled correctly. Thus, Crawford teaches this argued limitation.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at (571)272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIONNA M BURKE/Examiner, Art Unit 2178 5/11/26
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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