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 .
Response to Arguments
Applicant’s response to the last Office Action, filed 7/15/2026, has been entered and made of record.
Applicant has amended no claims. Claims 4, 9, 15, 19, and 24-25 stand as cancelled. Claims 23 and 26 stand as withdrawn. Claims 1-3, 5-8, 10-14, 16-18, and 20-22 are currently pending.
Applicant’s arguments, filed 7/15/2026, with respect to the rejection of claim 1 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Thrun (U.S. Patent Pub. No. 2005/0128197).
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-6, 10-11, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Thrun (U.S. Patent Pub. No. 2005/0128197) in view of Rad (U.S. Patent Pub. No. 2018/0268601).
Regarding Claim 1, Thrun teaches a computer-implemented method of estimating a 3D object pose, the method comprising (¶19 The present invention also provides a method for determining a probable 3D map of an occluded surface of an object:)
receiving 3D data comprising a full or partial view of a 3D object, the 3D object exhibiting reflective symmetry about an unknown 2D symmetry plane (Fig. 5, 501; ¶39 In a first step 501 a 3D map of the visible portion of an object is obtained.;)
applying symmetry detection to the 3D data, and thereby calculating, in 3D space, an estimated 2D symmetry plane for the 3D object; and (Fig. 5, 502; ¶42 In step 502, the method next identifies symmetries within the 3D map of the visible regions. In general, symmetries may be global to the entire object, or local to a part of an object; ¶44 The symmetry type is chosen from a small set of admissible types, which characterize the typical symmetry types found in common objects, e.g. mirror planes and rotation axes. The numerical parameter vector specifies the location of the symmetry elements e.g., the orientation and location of a mirror plane relative to the 3D map of the visible surface.)
Thrun does not explicitly disclose applying 3D pose detection to the 3D data based on the estimated 2D symmetry plane, thereby computing a 3D pose estimate of the 3D object that is informed by the reflective symmetry of the 3D object.
Rad is in the same field of art of image analysis. Further, Rad teaches applying 3D pose detection to the 3D data based on the estimated 2D symmetry plane, thereby computing a 3D pose estimate of the 3D object that is informed by the reflective symmetry of the 3D object (¶28 The system determines a three-dimensional (“3D”) pose of an object from a two-dimensional (“2D”) input image which contains the object. The system can reduce the processing time for determining a 3D pose of a symmetric (e.g., rotationally symmetric or having a plane of symmetry) or nearly-symmetric (e.g., rotationally nearly-symmetric) object)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Thrun by explicitly applying pose detection to the data that is taught by Rad; thus, one of ordinary skilled in the art would be motivated to combine the references to correctly determine the pose of a symmetrical object (Rad ¶6).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Thrun in view of Rad discloses the method of claim 1, wherein an initial 3D pose estimate is computed, and wherein a refined 3D pose estimate is computed based on the initial 3D pose estimate and the estimated 2D symmetry plane (Rad, ¶62 the pose estimation engine 338 has a refiner block 337 that receives an initial 3D pose estimate generated in the pose estimation engine 338 and generates an updated 3D pose.)
The reasons for combining Thrun and Rad are similar to that stated in the rejection of claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claims 3, 5-6, 10-11, and 22 below.
Regarding Claim 3, Thrun in view of Rad discloses the method of claim 2, wherein the refined 3D pose estimate is computed based on a subset of the 3D data, wherein the subset of the 3D data is extracted based on the initial 3D pose estimate and an estimated extent of the 3D object (Rad, Figs. 3&6; ¶71 The first input is the image window W (or patch). (The same image window W or patch that is used by the CNN f.sub.Θ to determine the initial pose estimate). The second part of the input depends on the current estimate of the pose. For example, either a binary mask or a color rendering of the target object as seen from the current estimate can be used as the second input to g.sub.μ. The neural network g.sub.μ. determines the update to the 2D projections by generating a respective mask or rendering of the object for each respective one of a plurality of poses around and including the estimated 3D pose of the object; ¶92 In blocks 1114 and 1116, the 2D projections are refined by inputting the image or the patch of the image, and the estimated 3D pose of the object to a neural network in the refiner block 337 (FIG. 3).)
Regarding Claim 5, Thrun in view of Rad discloses the method of claim 3, wherein the initial 3D pose estimate and the estimated extent are provided in the form of a detected 3D bounding object for the 3D object (Rad, ¶91 estimating the 3D pose of the object includes, at block 1110, determining a first 3D bounding box of the mirror image of the object in the obtained image.)
Regarding Claim 6, Thrun in view of Rad discloses the method of claim 5, wherein the 3D bounding object is determined in the form of a 3D bounding box by applying a 3D bounding box detector to the 3D data (Rad, ¶91 estimating the 3D pose of the object includes, at block 1110, determining a first 3D bounding box of the mirror image of the object in the obtained image.)
Regarding Claim 10, Thrun in view of Rad discloses the method of claim 1, wherein a set of proposed 3D bounding objects is generated for the 3D object, and computing the 3D pose estimate comprises using the estimated 2D symmetry plane to select a 3D bounding box of the proposed set of bounding boxes (Rad, ¶32 estimating the 3D pose of the object includes determining a first 3D bounding box of the mirror image of the object in the obtained image, determining a second 3D bounding box wherein the second 3D bounding box is a mirror image of the first 3D bounding box, determining a plurality of 2D projections of the second 3D bounding box, and estimating the 3D pose of the object using the plurality of 2D projections.)
Regarding Claim 11, Thrun in view of Rad discloses the method of claim 10, wherein the 3D bounding box is selected based on at least one assumption about a location and/or orientation of the 3D object relative to the estimated 2D symmetry plane (Rad, ¶32 estimating the 3D pose of the object includes determining a first 3D bounding box of the mirror image of the object in the obtained image, determining a second 3D bounding box wherein the second 3D bounding box is a mirror image of the first 3D bounding box, determining a plurality of 2D projections of the second 3D bounding box, and estimating the 3D pose of the object using the plurality of 2D projections.)
Regarding Claim 22, Thrun in view of Rad discloses the method of claim 1, implemented in an offline ground truthing pipeline to generate 3D object ground truth for the 3D data (Rad, ¶58 During the training phase, the 3D locations are used—they are projected to the image, using a ground truth pose, to get the 2D projections. (“Ground truth” refers to information provided by direct observation, i.e. empirical evidence. In this example, training is performed using a set of labeled data comprising images with respective known 2D projections of 3D bounding box corner locations, which are considered ground truth poses.)
Claims 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Thrun (U.S. Patent Pub. No. 2005/0128197) in view of Rad (U.S. Patent Pub. No. 2018/0268601) in view of Liu (U.S. Patent Pub. No. 2021/0397857).
Regarding Claim 20, Thrun in view of Rad teaches the method of claim 1 wherein the 3D object is assumed to be a vehicle exhibiting a single unknown 2D symmetry plane running along the length of the vehicle (Rad, ¶4 For instance, determining a pose of an object in an image can be used to facilitate effective operation of various systems. Examples of such applications include augmented reality (“AR”), robotics, automotive, aviation, and processes and operation of devices using machine vision, in addition to many other applications.)
Thrun in view of Rad hints at but does not explicitly disclose wherein the object detected is a vehicle.
Liu is in the same field of art of image analysis. Further, Liu teaches wherein the object detected is a vehicle (Fig. 6; ¶77 teaches determining 3d position of points on a car from the image).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Thrun in view of Rad by detecting a car that is taught by Liu; thus, one of ordinary skilled in the art would be motivated to combine the references to safely maneuver an autonomous vehicle through traffic or on a highway (Liu ¶2).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 21, Thrun in view of Rad in view of Liu discloses the method of claim 1, implemented in a real-time perception system (Liu, ¶32 The long-distance perception system 100 includes one or more cameras 102 installed on or in an autonomous vehicle 101. Each camera 102 can generate high-resolution images in real-time while the autonomous vehicle 101 is in operation, such as driving on the road or stopping at a stop sign.)
The reasons for combining Thrun in view of Rad in view of Liu are similar to that stated in the rejection of claim 20.
Allowable Subject Matter
Claims 7-8, 12-14, 16-18 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.
Regarding claim 7, No prior art teaches the method of claim 2, wherein the refined 3D pose
estimate is computed by transforming the initial 3D pose estimate based on at least one assumption about a location and/or orientation of the 3D object relative to the estimated 2D symmetry plane.
Regarding claim 8, No prior art teaches the method of claim 5, wherein the refined 3D pose
estimate is computed by transforming the 3D bounding object such that the estimated 2D symmetry plane lies along a predefined axis of the transformed 3D bounding object.
Regarding claim 12, No prior art teaches wherein applying symmetry detection to the 3D data
comprises generating an output tensor, by processing a voxel array in a convolutional neural network, the voxel array encoding a full or partial view of the 3D object, wherein each element of the output tensor corresponds to a portion of a 3D volume containing the 3D object, and contains a predicted offset of that portion from the unknown 2D symmetry plane.
Regarding claim 13, No prior art teaches dividing a voxel representation of the 3D data into
horizontal slices; extracting for each horizontal slice a first feature tensor, by processing the horizontal slice in a 2D convolutional neural network (CNN) based on 2D convolutions within the 2D CNN, each 2D convolution performed by sliding a filter across a horizontal plane in only two dimensions, the first feature tensor comprising one or more first feature maps encoding local features of any portion of the 3D object occupying that horizontal slice;
generating a second feature tensor for each horizontal slice, by providing the first feature tensors as a sequenced input to a convolutional recurrent neural network (CRNN), with the first feature tensors ordered to match a vertical order of the horizontal slices; wherein applying symmetry detection comprises processing the second feature tensor of each horizontal slice in a decoder, in order to compute multiple predicted offsets for multiple portions of the slice, the predicted offset for each portion of the slice being a predicted offset between that portion of the slice and the unknown 2D symmetry plane.
Conclusion
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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