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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered.
Response to Amendment
The amendment filed 11/25/2025 has been entered and made of record. Claims 1-4, 6-9, and 11-19 remain pending in this application.
Response to Arguments
Applicant’s arguments with respect to the amended claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Thus, the newly cited prior art of Govari (US 2021/0174522) is used in combination with the previously cited prior arts, and together does meet each limitation of the amended claims as fully disclosed below.
Based on these facts, the action is made Non-Final.
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 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Georgakis et al (WO 2020/014170 A1) in view of Govari (US 2021/0174522), and Bier et al (“Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views,” 2019).
Regarding claim 1, Georgakis teaches a method performed by data processing apparatus for training a device for estimating the relative pose of an imaging device and an object in a two-dimensional image (Georgakis, Paragraph 011, learning to estimate a 3D pose from an RGB image), the method comprising:
identifying a 3D model of the object (Georgakis, Paragraphs 011 and 013, images are rendered using a CAD model of an object);
identifying landmarks on the 3D model of the object, wherein identifying the landmarks on the 3D model of the object comprises
rendering a collection of two-dimensional images of the object by projecting, onto two dimensions, the 3D model of the object on which landmarks are to be identified (Georgakis, Figure 3 and Paragraph 013, two depth images (i.e., a collection of two-dimensional images) are rendered from the 3D object into 2D CAD images from different views (i.e., projecting the 3D model of the object), in order to detect keypoints in the rendered pictures),
assigning different regions of the object in the two-dimensional images to respective parts of the object (Georgakis, Paragraph 015, a number of sectors are defined in each rendered picture of the object),
determining distinguishable regions of the parts of the object using the assigned regions (Georgakis, Paragraphs 017-018, from the defined sectors, identify keypoint-sectors in the images which contain high information for pose estimation; [019]: use centroids of the keypoint-sectors as keypoints to project back to 3D), and
projecting the distinguishable regions back onto the 3D model of the objects to identify the landmarks on the 3D model of an object (Georgakis, Paragraph 019, “un-project” 2D centroids from the rendered images back onto the 3D object in order to classify 3D keypoints).
However, Georgakis does not specifically teach where Govari teaches wherein the two-dimensional images are each rendered in a frame of reference that reflects a real-world environment in which the object is likely to be found (Govari, Paragraph 0011, 2D images are generated via a frame of reference in order to be rendered indicating location of landmarks on the surface that are identified).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis using Govari’s teachings by incorporating of a rendering for a two-dimensional image reflecting a real-world environment detected by Georgakis, in order to accurately and correctly identify regions on the image for locating objects.
Further, Georgakis, in combination with Govari, does not specifically teach where Bier teaches after the identification of the landmarks on a 3D model of an object, projecting the 3D model into a collection of two-dimensional images with knowledge of the location of the landmarks from the 3D model on the projection (Bier, Page 1466-1467, Data generation: in order to train a landmark-detection network, project a 3D model into a plurality of 2D images from different viewpoints, with the corresponding 3D landmark labels from the 3D model; See Figure 3); and
training a landmark-detection machine learning model to identify, in the collection of two-dimensional images, the landmarks projected from the 3D model, wherein the landmark-detection machine learning model is part of a device for estimating the relative pose of an imaging device (Bier, Introduction of Last paragraph (beginning on p. 1465) and Figure 3, a convolutional neural network is trained to identify landmarks in a 2D image projected from a 3D model; also Page 1467, 2D/3D registration: the detected landmarks are used to identify a correspondence in the 3D space (i.e., the relative pose)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis and Govari using Bier’s teachings by incorporating training a landmark detection model using images rendered from the 3D landmarks detected by Georgakis, in order to generate a collection of 2D training data using knowledge from 3D ground truth landmarks (see Bier > Introduction > last para).
Regarding claim 2, Georgakis, as modified by Govari and Bier, further teaches the method of claim 1, further comprising:
estimating relative poses of the object in two-dimensional images using the device that includes the landmark-detection machine learning model (Georgakis, Paragraphs 023-025, the keypoint detector determines relative-pose scores for the proposed keypoints, based on how well they determine a pose of a pictured object);
determining a correctness of the estimates of the relative poses (Georgakis, Paragraph 023, the relative-pose score indicates a measure of relative poses of features); and
further training the landmark-detection machine learning model based on the correctness of the estimates of the relative poses (Georgakis, Paragraph 022, the keypoint detector is iteratively trained until the relative-pose scores reach a predetermined level).
Regarding claim 3, Georgakis, as modified by Govari and Bier, further teaches the method of claim 2, wherein:
the relative poses of the object are estimated in the collection of two-dimensional images into which the 3D model is projected (Georgakis, Paragraph 023-025, the keypoint detector determines relative-pose scores for the proposed keypoints in the projected 2D images, based on how well they determine a pose of a pictured object).
Regarding claim 4, Georgakis, as modified by Govari and Bier, further teaches the method of claim 3, wherein determining the correctness of the estimates of the relative poses comprises:
constraining relative poses of the projections of the 3D model into the collection of two-dimensional images (Georgakis, Paragraph 019, keypoint locations (and thus estimated poses) are constrained in the 2D images of the 3D object); and
classifying any estimate of the relative pose that does not satisfy the constraints as incorrect (Georgakis, Paragraph 019, wrong pose estimations are penalized in the keypoint detector network).
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Georgakis in view of Govari, and Chen et al (US 10,657,647 B1, provided by Applicant’s Information Disclosure Statement – IDS submitted 3/21/2024).
Regarding claim 11, Georgakis teaches a method performed by data processing apparatus for identifying landmarks on a 3D model of an object (Georgakis, Paragraphs 011 and 019, keypoints are identified on a 3D object for pose estimation), the method comprising:
rendering a collection of two-dimensional images of an object by projecting the 3D model of the object onto two dimensions (Georgakis, Paragraph 013, several pictures are rendered by projecting the 3D object into 2D from different views, in order to detect keypoints in the rendered pictures), wherein the rendering does not comprise preserving constituent parts of the 3D model in the rendered two-dimensional images;
assigning different regions of the object in the two-dimensional images to respective parts of the object (Georgakis, Paragraph 015, a number of sectors are defined in each rendered picture of the object) not based on the preserved constituent parts of the 3D model;
determining distinguishable regions of the parts of the object using the assigned regions (Georgakis, Paragraphs 017-018, from the defined sectors, identify keypoint-sectors in the images which contain high information for pose estimation, and use centroids of the keypoint-sectors as keypoints to project back to 3D); and
projecting the distinguishable regions back onto the 3D model of the object to identify the landmarks on the 3D model of an object (Georgakis, Paragraph 019, “un-project” 2D centroids from the rendered images back onto the 3D object in order to classify 3D keypoints).
However, Georgakis does not specifically teach where Govari teaches wherein the two-dimensional images are each rendered in a frame of reference that reflects a real-world environment in which the object is likely to be found (Govari, Paragraph 0011, 2D images are generated via a frame of reference in order to be rendered indicating location of landmarks on the surface that are identified).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis using Govari’s teachings by incorporating of a rendering for a two-dimensional image reflecting a real-world environment detected by Georgakis, in order to accurately and correctly identify regions on the image for locating objects.
Further, Georgakis, in combination with Govari, together does not specifically teach wherein Chen teaches wherein the rendering comprises preserving constituent parts of the 3D model in the rendered two-dimensional images (Chen, Col 25, Lines 26-38, a 3D base object model is used to create a 2D segmented target object image, See also Figure 11 and Col 17, Lines 37-60, the base object model segments are constituent parts which are preserved when rendering the segmented target object image); and
wherein different regions of the object are assigned based on the preserved constituent parts of the 3D model (Chen, Figures 11-12 and Col 25, Lines 57-62, the segmented target object image defines the contours around individual parts from the base object model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis and Govari using Chen’s teachings by incorporating preserving constituent parts when rendering and assigning regions to the two-dimensional images of Georgakis, in order to perform processing on individual regions corresponding to object parts (see Chen, Col 26, Lines 1-6). Additionally, the intersection between individual parts is a desirable place to locate object landmarks (see Chen, Col 18, Lines 33-45).
Regarding claim 13, Georgakis, as modified by Govari and Chen, further teaches the method of claim 11, further comprising reducing a number of the distinguishable regions prior to projection back onto the 3D model (Georgakis, Paragraphs 017 and 024-025, proposed keypoints (i.e., distinguishable regions) that do not score high for pose estimation are discarded).
Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Georgakis in view of Govari, Bier, further in view of Sim et al (“Learning Visual Landmarks for Pose Estimation,” 1999).
Regarding claim 6, Georgakis teaches a method performed by data processing apparatus for identifying landmarks on a 3D model of an object (Georgakis, Paragraphs 011 and 019, keypoints are identified on a 3D object for pose estimation), the method comprising:
detecting landmarks on the object in the two-dimensional image using a landmark-detection machine learning model, wherein the landmark-detection machine learning model has been trained by a process that includes
identifying a 3D model of the object (Georgakis, Paragraph 011 and 013, images are rendered using a CAD model of an object),
identifying landmarks on the 3D model of the object, wherein identifying the landmarks on the 3D model of the object comprises
i) rendering a collection of two-dimensional images of the object by projecting the 3D model of the object onto two dimensions without knowledge of the location of the landmarks from the 3D model on the projection (Georgakis, Paragraph 013, several pictures are rendered by projecting the 3D object into 2D from different views, in order to detect keypoints in the rendered pictures),
ii) assigning different regions of the object in the two-dimensional images to respective parts of the object (Georgakis, Paragraph 015, a number of sectors are defined in each rendered picture of the object),
iii) determining distinguishable regions of the parts of the object using the assigned regions (Georgakis, Paragraphs 017-018, from the defined sectors, identify keypoint-sectors in the images which contain high information for pose estimation, and use centroids of the keypoint-sectors as keypoints to project back to 3D), and
iv) projecting the distinguishable regions back onto the 3D model of the objects to identify the landmarks on the 3D model of an object (Georgakis, Paragraph 019, “un-project” 2D centroids from the rendered images back onto the 3D object in order to classify 3D keypoints);
filtering the plurality of landmarks to establish a plurality of subsets of the detected landmarks (Georgakis, Paragraphs 017 and 024-025, proposed keypoints that do not score high for pose estimation are discarded (i.e., filtered)).
However, Georgakis does not specifically teach where Govari teaches wherein the two-dimensional images are each rendered in a frame of reference that reflects a real-world environment in which the object is likely to be found (Govari, Paragraph 0011, 2D images are generated via a frame of reference in order to be rendered indicating location of landmarks on the surface that are identified).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis using Govari’s teachings by incorporating of a rendering for a two-dimensional image reflecting a real-world environment detected by Georgakis, in order to accurately and correctly identify regions on the image for locating objects.
Further, Georgakis, in combination with Govari, together does not specifically teach where Bier teaches a method for estimating the relative pose of an imaging device and an object in a two-dimensional image (Bier, Introduction Last Paragraph (beginning on Page 1465), estimate a pose of an object in an X-ray image);
after the dentification of the landmarks on a 3D model of an object, projecting the 3D model into a collection of two-dimensional images with knowledge of the location of the landmarks from the 3D model on the projection (Bier, Figure 3 and Page 1466-1467, Data generation: in order to train a landmark-detection network, project a 3D model into a plurality of 2D images from different viewpoints, with the corresponding 3D landmark labels from the 3D model); and
training a landmark-detection machine learning model to identify, in the collection of two-dimensional images, the landmarks projected from the 3D model, wherein the landmark-detection machine learning model is part of a device for estimating the relative pose of an imaging device (Bier, Introduction Last Paragraph (beginning on Page 1465) and Figure 3, a convolutional neural network is trained to identify landmarks in a 2D image projected from a 3D model; and also Page 1467, 2D/3D registration: the detected landmarks are used to identify a correspondence in the 3D space (i.e., the relative pose)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis and Govari using Bier’s teachings by incorporating training a landmark detection model using images rendered from the 3D landmarks detected by Georgakis, in order to generate a collection of 2D training data using knowledge from 3D ground truth landmarks (see Bier, Introduction, Last Paragraph).
Additionally, neither Georgakis, Govari, nor Bier specifically teach where Sim teaches calculating, using a set of detected landmarks, candidate relative poses of the object in the two-dimensional image (Sim, Figure 2 and Section 6, Paragraphs 1-2, candidate relative poses are estimated from a plurality of landmarks); and
estimating the relative pose of an imaging device and an object based on at least one of the candidate relative poses (Sim, Figure 2 and Section 3, On-line localization: a final pose estimate is obtained by averaging multiple candidate pose estimates; see also Section 6, Paragraphs 4-5 and Figure 7 (right side of p. 1976), procedure for averaging candidate pose estimates).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Georgakis (as modified by Govari and Bier) using Sim’s teachings by incorporating estimating a relative pose of an object from a plurality of candidate poses based on the subset of detected landmarks of Georgakis (as modified by Govari and Bier), in order to utilize independent position information given by the subset of landmarks.
Regarding claim 7, Georgakis, as modified by Govari, Bier, and Sim, further teaches the method of claim 6, further comprising filtering the candidate relative poses of the object (Sim, Section 6, Paragraphs 4-5 and Figure 7 (right side of p. 1976), candidate pose estimates of an object are filtered to remove outliers).
Regarding claim 8, Georgakis, as modified by Govari, Bier and Sim, further teaches the method of claim 7, wherein criteria for filtering the candidate relative poses reflect real-world conditions in which a real image is likely to be taken (Sim, Section 6, Paragraphs 4-5 and Figure 7 (right side of p. 1976), candidate pose estimates of an object are filtered to remove outliers, which are likely unrealistic pose estimations).
Regarding claim 9, Georgakis, as modified by Govari, Bier and Sim, further teaches the method of claim 6, wherein estimating the relative pose of the image device and the object comprises averaging multiple of the candidate relative poses (Sim, Figure 2 and Section 3, On-line localization: a final pose estimate is obtained by averaging multiple candidate pose estimates; see also Sim, Section 6, Paragraphs 4-5 and Figure 7 (right side of p. 1976), procedure for averaging candidate pose estimates).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari, Chen, and further in view of Harris et al (“A combined corner and edge detector,” 1988).
Regarding claim 12, neither Georgakis nor Govari, or Chen specifically teaches where Harris teaches the method of claim 11, wherein determining the distinguishable regions of the parts comprises detecting corners of projections of the parts in the two-dimensional images (Harris, Abstract, method for detecting corners in images).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Chen) using Harris’ teachings by incorporating detecting corners as the distinguishable regions of Georgakis (as modified by Govari and Chen), in order to detect features that are discrete and reliable across images.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari, and Chen, in view of Greenspan et al (“Automatic Detection of Anatomical landmarks in Uterine Cervix Images,” 2009).
Regarding claim 14, neither Georgakis nor Govari, or Chen specifically teaches where Greenspan teaches the method of claim 13, wherein reducing the number of the distinguishable regions comprises filtering distinguishable regions that are close to an outer boundary of the object (Greenspan, First Paragraph on Page 463, detected regions near the boundaries of an object of interest are discarded when the desired landmark is known to be in the center).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Chen) using Greenspan’s teachings by incorporating filtering the distinguishable regions near the outer boundary of Georgakis (as modified by Govari and Chen), in order to reduce false detections of landmarks that are known to be in the center of objects.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari, and Chen, in view of Grimm et al (“Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration,” 2020).
Regarding claim 15, neither Georgakis, Govari, nor Chen specifically teaches where Grimm teaches the method of claim 13, wherein reducing the number of the distinguishable regions comprises:
clustering, onto the 3D model, back-projections of the distinguishable regions from a plurality of the two-dimensional images in the collection (Grimm, Abstract and Section II, Phase 2.b, Paragraph 1: landmark predictions are back-projected and clustered on a 3D model; See also Section II, Phase 2.a: the landmark predictions come from a set of 2D images before being back-projected; Further, see the middle block of Figure 2, where landmarks are predicted from DDR images, then clustered, then back-projected; and
discarding outliers of the distinguishable regions (Grimm, Section II, Phase 2.b, Paragraphs 1-2: outlier points according to a threshold are discarded when clustering landmarks).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Chen) using Grimm’s teachings by incorporating clustering to reduce the distinguishable regions of Georgakis (as modified by Govari and Chen), in order to account for an inherent prediction inaccuracy between landmarks detected from different images of the same object.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari, Chen, in further view of Bier.
Regarding claim 16, neither Georgakis nor Govari or Chen specifically teaches where Bier teaches the method of claim 11, wherein rendering the collection of two-dimensional images of the object comprises:
permuting the object (Bier, Page 1466-1467, Data generation: the object volume is translated, flipped, or viewed at a different angle); and
projecting the permutations of the 3D model onto two dimensions (Bier, Page 1466-1467, Data generation: a collection of 2D images is created with the applied permutations).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Chen) using Bier’s teachings by incorporating object permutations when rendering the two-dimensional images of Georgakis (as modified by Govari and Chen), in order to render a variety of training data using limited resources (see Bier, Page 1466-1467, Data generation, Paragraph 1).
Regarding claim 17, neither Georgakis nor Govari or Chen specifically teaches where Bier teaches the method of claim 11, wherein rendering the collection of two-dimensional images of the object comprises varying to rendering to mimic variation in a characteristic of an imaging apparatus, to mimic variation in a characteristic of image processing applicable to two-dimensional images, or to mimic variation in an imaging condition (Bier, Page 1466-1467, Data generation: when rendering a collection of 2D images, the object volume is translated, flipped, or viewed at a different angle).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Chen) using Bier’s teachings by incorporating variety in imaging conditions when rendering the two-dimensional images of Georgakis (as modified by Govari and Chen), in order to train a network model to be accurate regardless of the viewing condition (Bier, Page 1466-1467, Data generation, Paragraph 2).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari and Bier, in further view of Chen.
Regarding claim 18, neither Georgakis nor Govari or Bier specifically teaches where Chen teaches the method of claim 1, wherein:
the rendering of the collection of two-dimensional images of the object comprises preserving constituent parts of the 3D model in the rendered two-dimensional images (Chen, Figure 11 and Col 25, Lines 26-38: a 3D base object model is used to create a 2D segmented target object image; See also Col 17, Lines 37-60, the base object model segments are constituent parts which are preserved when rendering the segmented target object image); and
the different regions of the object in the two-dimensional images are assigned to respective parts of the object based on the preserved constituent parts of the 3D model (Chen, Figures 11-12 and Col 25, Lines 57-62, the segmented target object image defines the contours around individual parts from the base object model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari and Bier) using Chen’s teachings by incorporating preserving constituent parts when rendering and assigning regions to the two-dimensional images of Georgakis (as modified by Govari and Bier), in order to perform processing on individual regions corresponding to object parts (see Chen, Col 26, Lines 1-6). Additionally, the intersection between individual parts is a desirable place to locate object landmarks (see Chen, Col 18, Lines 33-45).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Georgakis, as modified by Govari, Bier and Sim, and further in view of Chen.
Regarding claim 19, neither Georgakis, Govari, Bier, nor Sim specifically teaches where Chen teaches the method of claim 6, wherein:
the rendering of the collection of two-dimensional images of the object comprises preserving constituent parts of the 3D model in the rendered two-dimensional images (Chen, Figure 11 and Col 25, Lines 26-38: a 3D base object model is used to create a 2D segmented target object image; See also Col 17, Lines 37-60, the base object model segments are constituent parts which are preserved when rendering the segmented target object image); and
the different regions of the object in the two-dimensional images are assigned to respective parts of the object based on the preserved constituent parts of the 3D model (Chen, Figures 11-12 and Col 25, Lines 57-62, the segmented target object image defines the contours around individual parts from the base object model).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Georgakis (as modified by Govari, Bier, and Sim) using Chen’s teachings by incorporating preserving constituent parts when rendering and assigning regions to the two-dimensional images of Georgakis (as modified by Goviari, Bier, and Sim), in order to perform processing on individual regions corresponding to object parts (see Chen, Col 26, Lines 1-6). Additionally, the intersection between individual parts is a desirable place to locate object landmarks (see Chen, Col 18, Lines 33-45).
Conclusion
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/VINCENT RUDOLPH/ Supervisory Patent Examiner, Art Unit 2671
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