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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/04/2024, 04/23/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are considered by examiner.
Claim Objections
Applicant is advised that should claims 1-3, 10 be found allowable, claims 11-13, 15 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10, 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 10, 15 claims “for at least two pixels in parallel” in the limitation “determine respective depth bins for at least two pixels in parallel.” It is unclear if “in parallel” is meant as two pixels in the same row of an image, two pixels are processed at the same time or if depth is meant to be determined between pixels of input frames. For purposes of examination the two pixels in parallel is interpreted as a pixel-pair to define a depth relationship between a target frame and a reference frame.
Thus, Applicant has failed to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
No claim is dependent on claim 10 or claim 15.
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, 7-11, 15-16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ren et al (US 2021/0124985) in view of Dunn et al (WO 2021/243281).
Regarding Claim 1, Ren et al teach a system (computer vision training system; Fig 1 and ¶ [0049]), comprising:
an image capturing device configured to capture a first image and a second image (camera is used to capture multi-frame/multi-view images 104; Fig 1 and ¶ [0049]);
a navigation system configured to generate pose data (pose estimator 302 is contained in learning module 114, in memory 110 and used to determine pose data; Fig 1-3 and ¶ [0056], [0076]-[0077]);
one more processors communicatively coupled to the image capturing device and the navigation system (processor 108 and associated processing circuits 106 are coupled to memory 110, which includes module 114 with estimator 302 and camera to receive images; Fig 1-3 and ¶ [0049]-[0052]); and
a non-transitory, computer readable medium communicatively coupled to the one or more processors (memory 110 coupled to processor/circuits 106, 108; Fig 1 and ¶ [0051]-[0052]), wherein the non-transitory, computer readable medium stores one or more instructions (memory 110 with instructions for system 102 executed by processor/circuits 106, 108; Fig 1, 5A, 5B and ¶ [0051]-[0053], [0105]) which, when executed by the one or more processors, cause one or more processors to:
estimate an optical flow between the first image and the second image (the learning module 114 determines the optical flow motion corresponding to the images (converted from low to high resolution); Fig 5A and ¶ [0105]-[0106]);
determine a rigid flow per image for each depth bin (described as estimated depth values, specification ¶ [0029]-[0030]) of a group of depth bins based on the pose data (the estimated depth data (depth bin, see also Zhou et al US 2020/0074657 further describing bins cited in pertinent prior art below) from pose estimator 302 is used to determine a rigid-motion of the object from the input image frames; Fig 3 and ¶ [0076]-[0077]),
wherein each depth bin corresponds to a particular depth range (the depth of an object in the image data is estimated (given the broadest reasonable interpretation that an estimation is an approximation in a range ¶ [0151]) with a depth estimation network 308 in the supervised learning module 112 (and thereafter used by unsupervised learning module for rigid-motion estimation by pose estimator 302); ¶ [0075]-[0077]); and
generate a dense depth map by determining a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (the depth estimation from the system 102 is used to estimate a pixelwise depth map for an image; Fig 1, 3, 6 and ¶ [0070], [0124]).
Ren et al does not explicitly teach a navigation system or to generate a dense depth map by determining a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel.
Dunn et al is analogous art pertinent to the technological problem addressed in the current application and teaches a navigation system (visual odometry of camera poses (given BRI) to determine position and rotation (pose) for autonomous navigation system relative to object; ¶ [0006]-[0008]) and to generate a dense depth map by determining a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (depth map Q is determined based on the optical flow chain X based on the rigidness map W and determining optical flow displacement; ¶ [0053]-[0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al with Dunn et al including a navigation system and to generate a dense depth map by determining a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel. By utilizing camera pose and dense scene structure data, visual odometry is applied to determine multi-view relationships among input visual data, including analysis of rigid flow and estimated optical flow for tracking local and global environment geometry information as applied to autonomous driving system thereby enhancing safe planning and navigational operation execution, as recognized by Dunn et al (¶ [0006]-[0009]).
Regarding Claim 7, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), wherein the image capturing device, the navigation system, the one or more processors, and the non-transitory computer readable medium are positioned on a vehicle (Dunn et al, the camera and associated sensors to perform the visual odometry are based on car-mounted camera data used for navigation, planning and operation for autonomous vehicle driving applications ¶ [0009]).
Regarding Claim 8, Ren et al in view of Dunn et al teach the system of claim 7 (as described above), wherein a sample rate of the image capturing device is adjusted based on (interpreted as at least one of the following three limitations:) a speed of the vehicle, a location of the vehicle, and/or a phase of travel (Dunn et al, the frequency of the frames observed are associated with the camera motion (speed of autonomous vehicle while driving is directly correlated with the camera speed) and moving speed influences accuracy of estimated depth pixels, and an optimization window is used for frame observation frequency; ¶ [0069]-[0070]).
Regarding Claim 9, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), wherein the image capturing device is further configured to capture a third image (Ren et al, the camera is used to capture multi-frame/multi-view images 104, including a third image; Fig 1 and ¶ [0049]);
wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors (Ren et al, processor 108 and associated processing circuits 106 are coupled to memory 110 to execute instructions to process images; Fig 1-3 and ¶ [0049]-[0052]) to:
generate a second dense depth map based on a second optical flow and a second rigid flow determined using the first image, the third image, and pose data corresponding to the first image and the third image (Ren et al, the estimated depth data (depth bin) from pose estimator 302 is used to determine a rigid-motion of the object from the input image frames, the optical flow motion of the objects (object motion from t to t-1 and to t+1) and collectively determine the pixelwise depth map for an image, including for a third image; Fig 1, 3, 6 and ¶ [0070]-[0079], [0105]-[0107], [0124]);
comparing depth estimations for particular points in the dense depth map and the second dense depth map (Ren et al, depth data is estimated between the images within the supervised learning 112 to determine supervised loss; Fig 3 and ¶ [0072], [0076]-[0081]); and
deleting or verifying or judiciously combining the depth estimations based on the comparison (Ren et al, the depth estimations are optimized by comparing between frames and collectively combining, in a joint optimization process 120, the loss functions (pose and depth is considered) to iteratively update the models; Fig 3 and ¶ [0089]).
Regarding Claim 10, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), wherein the one or more processors are configured to determine respective depth bins for at least two pixels in parallel (Ren et al, a depth relationship is determined between a pixel-pair of an input image from a ground truth; ¶ [0082]-[0084]).
Regarding Claim 11, Ren et al teach a system (computer vision training system; Fig 1 and ¶ [0049]), comprising:
one or more inputs communicatively coupled to an image capturing device and a navigation system (processing circuits 106 are coupled to memory 110, which includes module 114 with estimator 302 and to receive camera images; Fig 1-3 and ¶ [0049]-[0052]); one or more processors (processor 108 and associated processing circuits 106 are coupled to memory 110, which includes module 114 with estimator 302 and camera to receive images; Fig 1-3 and ¶ [0049]-[0052]); and a non-transitory, computer readable medium communicatively coupled to the one or more processors (memory 110 coupled to processor/circuits 106, 108; Fig 1 and ¶ [0051]-[0052]), wherein the non-transitory, computer readable medium stores one or more instructions (memory 110 with instructions for system 102 executed by processor/circuits 106, 108; Fig 1, 5A, 5B and ¶ [0051]-[0053], [0105]) which, when executed by the one or more processors, cause one or more processors to:
estimate an optical flow between a first image and a second image received from the image capturing device (the learning module 114 determines the optical flow motion corresponding to the images (converted from low to high resolution) received from the camera (¶ [0049]-[0050]); Fig 5A and ¶ [0105]-[0106]);
determine a rigid flow per image for each depth bin (described as estimated depth values, specification ¶ [0029]-[0030]) of a group of depth bins based on the pose data received from the navigation system (the estimated depth data (depth bin, see also Zhou et al US 2020/0074657 further describing bins cited in pertinent prior art below) from pose estimator 302 is used to determine a rigid-motion of the object from the input image frames; Fig 3 and ¶ [0076]-[0077]),
wherein each depth bin corresponds to a particular depth range (the depth of an object in the image data is estimated (given the broadest reasonable interpretation that an estimation is an approximation in a range ¶ [0151]) with a depth estimation network 308 in the supervised learning module 112 (and thereafter used by unsupervised learning module for rigid-motion estimation by pose estimator 302); ¶ [0075]-[0077]); and
generate a dense depth map by determining a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (the depth estimation from the system 102 is used to estimate a pixelwise depth map for an image; Fig 1, 3, 6 and ¶ [0070], [0124]).
Ren et al does not explicitly teach a navigation system or to determine a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel.
Dunn et al is analogous art pertinent to the technological problem addressed in the current application and teaches a navigation system (visual odometry of camera poses (given BRI) to determine position and rotation (pose) for autonomous navigation system relative to object; ¶ [0006]-[0008]) and to determine a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (depth map Q is determined based on the optical flow chain X based on the rigidness map W and determining optical flow displacement; ¶ [0053]-[0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al with Dunn et al including a navigation system and to determine a depth bin for each pixel that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel. By utilizing camera pose and dense scene structure data, visual odometry is applied to determine multi-view relationships among input visual data, including analysis of rigid flow and estimated optical flow for tracking local and global environment geometry information as applied to autonomous driving system thereby enhancing safe planning and navigational operation execution, as recognized by Dunn et al (¶ [0006]-[0009]).
Regarding Claim 15, Ren et al in view of Dunn et al teach the system of claim 11 (as described above), wherein the one or more processors are configured to determine respective depth bins for at least two pixels in parallel (Ren et al, a depth relationship is determined between a pixel-pair of an input image from a ground truth; ¶ [0082]-[0084]).
Regarding Claim 16, Ren et al teach a method (method of using computer vision training system; Fig 1 and ¶ [0049]), comprising:
capturing a first image and a second image (camera is used to capture multi-frame/multi-view images 104; Fig 1 and ¶ [0049]);
generating first pose data corresponding to the first image and second pose data corresponding to the second image (pose estimator 302 is contained in learning module 114, in memory 110 and used to determine pose data; Fig 1-3 and ¶ [0056], [0076]-[0077]);
estimating an optical flow between the first image and the second image (the learning module 114 determines the optical flow motion corresponding to the images (converted from low to high resolution); Fig 5A and ¶ [0105]-[0106]);
determining a rigid flow per image for each depth bin of a plurality of depth bins (the estimated depth data (depth bin, see also Zhou et al US 2020/0074657 further describing bins cited in pertinent prior art below) from pose estimator 302 is used to determine a rigid-motion of the object from the input image frames; Fig 3 and ¶ [0076]-[0077]);
determining a depth bin of the plurality of depth bins for each respective pixel of a plurality of pixels that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (the depth off an object in the image data is estimated with a depth estimation network 308 in the supervised learning module 112 (and thereafter used by unsupervised learning module for rigid-motion estimation by pose estimator 302); ¶ [0075]-[0077]); and
generating a dense depth map based on the determined depth bin for the plurality of pixels (the depth estimation from the system 102 is used to estimate a pixelwise depth map for an image; Fig 1, 3, 6 and ¶ [0070], [0124]).
Ren et al does not explicitly teach a plurality of pixels that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel.
Dunn et al is analogous art pertinent to the technological problem addressed in the current application and teaches a plurality of pixels that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel (depth map Q is determined based on the optical flow chain X based on the rigidness map W and determining optical flow displacement; ¶ [0053]-[0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al with Dunn et al including a plurality of pixels that minimizes a difference between the estimated optical flow and the determined rigid flow for the respective pixel. By utilizing camera pose and dense scene structure data, visual odometry is applied to determine multi-view relationships among input visual data, including analysis of rigid flow and estimated optical flow for tracking local and global environment geometry information as applied to autonomous driving system thereby enhancing safe planning and navigational operation execution, as recognized by Dunn et al (¶ [0006]-[0009]).
Regarding Claim 19, Ren et al in view of Dunn et al teach the method of claim 16 (as described above), further comprising adjusting a sample rate for capturing a first image and a second image based on (interpreted as at least one of the following three limitations:) a speed of the vehicle, a location of the vehicle, and/or a phase of travel (Dunn et al, the frequency of the frames observed are associated with the camera motion (speed of autonomous vehicle while driving is directly correlated with the camera speed) and moving speed influences accuracy of estimated depth pixels, and an optimization window is used for frame observation frequency; ¶ [0069]-[0070]).
Claims 2-6, 12-14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ren et al (US 2021/0124985) in view of Dunn et al (WO 2021/243281) and Frahm et al (US 2013/0060540).
Regarding Claim 2, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), including the one or more instructions executed by the one or more processors (Ren et al, memory 110 with instructions for system 102 executed by processor/circuits 106, 108; Fig 1, 5A, 5B and ¶ [0051]-[0053], [0105]).
Ren et al in view of Dunn et al do not explicitly teach to generate an image-based height above ground level measurement based on the dense depth map and the pose data by transforming the dense depth map from a camera frame to a body frame coordinate system.
Frahm et al is analogous art pertinent to the technological problem addressed in the current application and teaches to generate an image-based height above ground level measurement based on the dense depth map and the pose data by transforming the dense depth map from a camera frame to a body frame coordinate system (a height map is generated based on the depth map of objects in the image based a reference plane, using a 3D cartesian coordinate and accounting for positional data via estimator 28 determined by the pixel data; Fig 2A, 3 and ¶ [0073]-[0075], [0108]-[0113]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al in view of Dunn et al with Frahm et al including to generate an image-based height above ground level measurement based on the dense depth map and the pose data by transforming the dense depth map from a camera frame to a body frame coordinate system. By using a height map model and consideration given to positional estimations, a dense scene geometry may be generated thereby allowing for efficient three-dimensional reconstruction and improved ground video surveying, as recognized by Frahm et al (¶ [0004]-[0006]).
Regarding Claim 3, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), including the one or more instructions executed by the one or more processors (Ren et al, memory 110 with instructions for system 102 executed by processor/circuits 106, 108; Fig 1, 5A, 5B and ¶ [0051]-[0053], [0105]).
Ren et al in view of Dunn et al do not explicitly teach to generate terrain data based on the dense depth map and the pose data.
Frahm et al is analogous art pertinent to the technological problem addressed in the current application and teaches to generate terrain data based on the dense depth map and the pose data (the depth map and ground level (heigh map) data is used to determine city modeling including the terrain in a texture map; ¶ [0068]-[0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al in view of Dunn et al with Frahm et al including to generate terrain data based on the dense depth map and the pose data. By using a height map model and consideration given to positional estimations, a dense scene geometry may be generated thereby allowing for efficient three-dimensional reconstruction of a terrain with improved ground video surveying, as recognized by Frahm et al (¶ [0004]-[0006], [0069]).
Regarding Claim 4, Ren et al in view of Dunn et al and Frahm et al teach the system of claim 3 (as described above), wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to update terrain data in a terrain database in real-time for situational awareness, path planning, obstacle avoidance, and/or emergency landing (Frahm et al, the method for generation of the city models based on the heigh map and texture generation are mapped and updated in real-time; ¶ [0085]-[0086], [0105], [0196]).
Regarding Claim 5, Ren et al in view of Dunn et al teach the system of claim 1 (as described above), including the one or more instructions executed by the one or more processors (Ren et al, memory 110 with instructions for system 102 executed by processor/circuits 106, 108; Fig 1, 5A, 5B and ¶ [0051]-[0053], [0105]).
Ren et al in view of Dunn et al do not explicitly teach to select the group of depth bins based on a previous height above ground level measurement.
Frahm et al is analogous art pertinent to the technological problem addressed in the current application and teaches to select the group of depth bins based on a previous height above ground level measurement (the height map is used to define a height of a pixel point along a given ray based on a reference plane (from ground level ¶ [0069]-[0070]) and the height values is applied to a voxel weight to approximate the depth value (depth bin) of a given point; ¶ [0073]-[0079], [0118]-[0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Ren et al in view of Dunn et al with Frahm et al including to select the group of depth bins based on a previous height above ground level measurement. By using a height based on a ground reference point, a dense scene geometry may be generated thereby allowing for efficient three-dimensional reconstruction and improved ground video surveying for vehicle operation, as recognized by Frahm et al (¶ [0004]-[0006], [0152]).
Regarding Claim 6, Ren et al in view of Dunn et al and Frahm et al teach the system of claim 2 (as described above), wherein the system further comprises an altimeter or other height above ground level sensor configured to determine a second height above ground level measurement (Frahm et al, the image capture system 20 is used to generate a height map model and a textured three-dimensional mesh representation with the system including camera 24 and position estimator 28; Fig 2A, 3 and ¶ [0105]-[0110]);
wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: combine the image-based height above ground level measurement and the second height above ground level measurement; and/or compare the image-based height above ground level measurement and the second height above ground level measurement (Frahm et al, the height map model and the textured 3D mesh representation may be output to a display as a 3D rendering 56 (interpreted that to generate a rendering there would be a comparison between the two representations); ¶ [0108]-[0110]).
Regarding Claim 12, Ren et al in view of Dunn et al teach the system of claim 11 (as described above), with further limitations taught identical to claim 2 (as described above).
Regarding Claim 13, Ren et al in view of Dunn et al teach the system of claim 11 (as described above), with further limitations taught identical to claim 3 (as described above).
Regarding Claim 14, Ren et al in view of Dunn et al teach the system of claim 11 (as described above), with further limitations taught identical to claim 5 (as described above).
Regarding Claim 20, Ren et al in view of Dunn et al teach the method of claim 16 (as described above), with further limitations taught identical to claim 4 (as described above).
Allowable Subject Matter
Claims 17, 18 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.
Regarding Claim 17, the prior art was not readily identified to teach the entirety of the claim limitation in combination with the claim in which it depends and specifically was not identified to teach at least the following limitations:
dividing a depth range into the plurality of depth bins; and determining the rigid flow per image for each depth bin of a plurality of depth bins based on the determined difference between the first pose data corresponding to the first image and the second pose data corresponding to the second image.
Regarding Claim 18, the prior art was not readily identified to teach the entirety of the claim limitation in combination with the claim in which it depends and specifically was not identified to teach at least the following limitations:
wherein the dense depth map is generated in a frame of an image capturing device, the method further comprising: translating the dense depth map from the frame of the image capturing device to a frame of a body of a vehicle to generate a translated dense depth map; and determining a depth bin for a center pixel of the translated dense depth map.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou et al (US 2020/0074657) teach a method and system for optically focusing a camera to determine depth of objects identified in the pixel array including generation of depth maps from the image data and determining ranges of the pixel bins, described in at least ¶ [0048] but does not teach the use of optical flow, rigid flow or pose used to determine depth and terrain.
Zou et al (DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency) teach a method and system for depth prediction and optical flow estimation using camera image data and incorporating methodology of comparing rigid flow to estimated optical flow to determine disparities and consistency loss using machine learning.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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