DETAILED ACTION
This action is responsive to the Amendments and Remarks received 09/25/2025 in which claims 7 and 19 are cancelled, claims 1, 8, 9, 17, and 20 are amended, and no claims are added as new claims.
Response to Arguments
On page 7 of the Remarks, Applicant argues that Applicant presented proper dependent claims. Examiner disagrees. The Office of Patent Quality Assurance (OPLA) has indicated in a factually-related instance Examiner’s position is proper. Applicant also contends that Applicant advised Examiner to issue a rejection under 35 U.S.C. 112(d). Examiner does not recall such an advisement. Despite Applicant’s arguments, Applicant amended claims 17 and 20 so that they do not refer to any other claim. Therefore, the issue is moot.
On pages 7–8 of the Remarks, Applicant contends, “The Examiner confirmed that the proposed amendments would overcome the rejections under 35 U.S.C. 112 as presented in the Office Action….” Examiner notes there were several rejections under 35 U.S.C. 112 in the previous Office Action. The only rejection substantively discussed in the interview appears to have been the rejection of claim 7 under 35 U.S.C. 112(a) according to both Applicant’s interview agenda and Examiner’s Interview Summary Record. Therefore, Applicant’s characterization appears to be too broad.
The rejection under 35 U.S.C. 112(d) is withdrawn in view of the amendments. Applicant’s traversal of the rejection is moot in view of the amendments. Remarks, 8.
The rejection under 35 U.S.C. 112(a) is withdrawn in view of the amendments. Remarks, 8.
The rejection under 35 U.S.C. 112(b) persists. See rejection, infra. Remarks, 8.
On page 10 of the Remarks, Applicant contends Jiang does not teach segmentation. Examiner disagrees. As explained in the rejection, infra, Jiang teaches semantic object segmentation. Jiang’s Fig. 24 shows a picture of a stop sign within a grid of image blocks. Segmentation of an image, is, by definition in this art, segmenting an image into blocks. Furthermore, segmentation can also be defined by segmenting an image into regions having objects of interest therein. Jiang’s Fig. 24 shows blobs, which is also a term of art that refers to image segmentation, which are regions defined by a bounding box. Finally, segmenting an image is one of the most fundamental concepts in this art. For all the foregoing reasons, Examiner finds it unreasonable to assert image segmentation is not found in the prior art. Accordingly, Examiner is unpersuaded of patentability under 35 U.S.C. 103.
On page 12 of the Remarks, Applicant contends the skilled artisan would be led to segment both stereo images and not on only one image. First, Applicant does not argue that which is claimed. There is no requirement that segmentation be only performed on one image. Second, Applicant does not explain why the skilled artisan would think segmentation would be required for both images. Attorney arguments and conclusory statements unsupported by factual evidence are entitled to little probative value. In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997). Therefore, the argument is unpersuasive of error.
On page 13 of the Remarks, Applicant contends Jiang does not teach a spatial transform and there is no construction of a new image using an image and a similarity map. Examiner disagrees. As explained in the rejection, infra, Jiang teaches adding spatial offset values according to a disparity map to produce an offset version of the camera 1 image. The offset version of the camera 1 image is expected to be the same or similar to the image of camera 2. Therefore, Applicant’s argument that Jiang does not teach a spatial transform of an image is belied by the teachings of Jiang. Accordingly, Examiner is unpersuaded of error.
On page 13 of the Remarks, Applicant contends Jiang does not teach or suggest producing a list of obstacles and Filias does not teach or suggest using a segmented image. Examiner disagrees such an argument is availing. Applicant argues against the references individually. The combination of Jiang and Filias teaches or suggests using a list of obstacles along with actual object detection using stereo vision, said stereo vision utilizing image segmentation to recognize said obstacles. Therefore, the combination teaches or suggests as obvious Applicant’s claimed features. Accordingly, the rejection is sustained under 35 U.S.C. 103.
On page 13 of the Remarks, Applicant contends Jiang does not teach the integrating step. Examiner disagrees such an argument is availing. The rejection relies on the teachings of Sinha to teach or suggest the averred limitation. Therefore, Applicant’s argument is unpersuasive of error.
Other claims are not argued separately. Remarks, 14.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112:
(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.
Claims 1–18 and 20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Specifically, one of the limitations recites, “and rectifying the first segmented image to obtain a first rectified segmented image.” It is unclear to what the first segmented image is rectified. Is it rectified with itself? How does one rectify a single image by itself? Because the skilled artisan cannot be reasonably certain regarding the metes and bounds of the claimed subject matter, the claim is in violation of 35 U.S.C. 112(b). Because no dependent claims appear to resolve the lack of clarity regarding the rejected limitation, claims dependent on claim 1 are likewise rejected.
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 of this title, 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 1–6, 12, 13, 16–18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang (US 2021/0327092 A1), Filias (US 2012/0016580 A1), and Sinha (US 2019/0362514 A1).
Examiner finds Applicant’s invention is nothing more than a list of prior art technology combined in known ways to effectuate computer vision for autonomous navigation. Horizontal epipolar lines occur when stereo cameras are arranged along the horizontal axis of the images and rectification aligns left and right images so that finding correspondences is simplified because a search for a match is limited to a one-dimensional search along the epipolar line. The process of calculating a disparity map starts with finding corresponding pixels between images using a process called stereo matching. Then the disparity is calculated as the difference between matching pixels’ horizontal positions. A disparity map arranges disparity values at pixel locations. A depth map assigns depths to disparity values based on known camera properties. The foregoing is well-represented in the prior art and known to the skilled artisan prior to Applicant’s priority date.
Regarding claim 1, the combination of Jiang, Filias, and Sinha teaches or suggests a method for detecting obstacles, implemented in at least one processing unit and comprising the steps of: acquiring raw stereo images, representative of an environment of a vehicle and produced by stereo cameras, the raw stereo images comprising a raw left image and a raw right image (Jiang, ¶ 0070: teaches raw images from left and right stereo cameras); implementing a semantic segmentation algorithm to produce a first segmented image from a first raw image, the first raw image being the raw left image or the raw right image (Jiang, Fig. 25: teaches a segmented left image in which a bounding box is assigned to a recognized, segmented object and is then used to find the corresponding object in the right image; Jiang, Fig. 24 and ¶ 0097: teaches object recognition systems can be used to segment an image such that a traffic sign recognition system can be effectuated; Examiner notes that a traffic sign recognition system semantically labels or identifies the traffic sign as a stop sign or semantically some other type of sign; Examiner notes Applicant’s original claim 15 admits the claimed semantic segmentation algorithm is a prior art modular component like U-net, HRNet, or NRNet + OCR neural networks); rectifying the raw stereo images to obtain rectified stereo images, and rectifying the first segmented image to obtain a first rectified segmented image (Jiang, ¶ 0070: teaches the raw stereo images are rectified to align the images); implementing a disparity calculation algorithm between the rectified stereo images, to produce a disparity map (Jiang, ¶ 0073: teaches calculating disparity for producing a disparity map); implementing a spatial transformation of the first rectified segmented image, by using the disparity map, to produce a second rectified segmented image, corresponding to the side opposite that of the first raw image, and thus produce rectified segmented stereo images (Jiang, ¶ 0093: teaches using the disparity map values to implement a spatial transform from the image of camera 1 to the corresponding point in the image of camera 2, stating, “Adding the disparity values to the Camera 1 keypoint column values yield the expected location for the Camera 2 keypoint column values.”); producing a list of predefined instances of obstacles present in the vehicle's environment from rectified segmented stereo images (Jiang, ¶¶ 0056 and 0063: teaches vehicle mounted stereo vision systems can be beneficial for recognizing and avoiding collisions with obstacles such as pedestrians, cyclists, and other vehicles; Jiang does not appear to specifically teach making a list of obstacles, however Filias, in the same field of endeavor, teaches a list of obstacles in a database that may supplement optical detection of obstacles during the piloting of vehicles; Filias, ¶¶ 0016 and 0060: teach the list of obstacles can be supplemented by the active telemeter sensor, which can be a stereovision system (¶ 0073)); implementing a three-dimensional reconstruction algorithm, using the disparity map, to produce three-dimensional coordinates for each pixel of the raw stereo images (Sinha, ¶ 0042: teaches using a disparity map to perform 3D image reconstruction); integrating, by using the three-dimensional coordinates, the predefined instances of obstacles in intermediate images obtained from rectified stereo images, to produce augmented images intended to provide assistance to the piloting of the vehicle (Sinha, ¶ 0045: teaches the 3D image reconstruction can be integrated into an augmented reality headset used for vehicle navigation and can include depth information for specified objects for collision avoidance during navigation).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Jiang, with those of Filias, because both references are drawn to the same field of endeavor such that one wishing to practice stereovision for autonomous or assisted vehicle navigation would be led to their relevant teachings and because Filias is merely explaining that a pre-defined list of obstacles for a given environment of the vehicle can be used together with active real-time sensors of the environment to help identify and locate those obstacles during vehicle navigation. Thus, the combination is a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Jiang and Filias used in this Office Action unless otherwise noted.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Jiang and Filias, with those of Sinha, because all three references are drawn to the same field of endeavor such that one wishing to practice stereovision for autonomous or assisted vehicle navigation would be led to their relevant teachings, because both Jiang and Sinha are particularly drawn to stereo matching for computing disparity maps (e.g. ¶ 0003) and because Sinha is merely explaining stereo image reconstruction is beneficial to computer-assisted driving such that incorporating Sinha’s teachings into Jiang’s assisted vehicle navigation system represents a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Jiang, Filias, and Sinha used in this Office Action unless otherwise noted.
Regarding claim 2, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the rectification step comprises a distortion correction and uses first parameters comprising extrinsic and intrinsic parameters of the stereo cameras (Jiang, ¶ 0012: teaches compensation for extrinsic and intrinsic camera parameters to achieve a calibrated system are required for accurate stereovision; see also ¶¶ 0071–0072 and 0074wherein raw and rectified images may be used as inputs into the calibration of the extrinsic and intrinsic parameters of the stereo camera system).
Regarding claim 3, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein epipolar lines of the rectified stereo images and of the rectified segmented stereo images are horizontal (Jiang, ¶ 0074 and Fig. 15B: describes and illustrates the conventional diagram for horizontally-related epipolar lines in stereo vision; The horizontal relationship is how stereo cameras related along horizontal axis given a baseline distance creates the disparity leading to depth perception; see e.g. Jiang, ¶ 0056).
Regarding claim 4, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, further comprising the step, preceding the implementation of the disparity calculation algorithm, of projecting the rectified stereo images into a system associated with a headset of a pilot of the vehicle (Sinha, ¶ 0045: teaches the 3D image reconstruction can be integrated into an augmented reality headset used for vehicle navigation and can include depth information for specified objects for collision avoidance during navigation).
Regarding claim 5, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the three-dimensional reconstruction algorithm uses second parameters comprising extrinsic and intrinsic parameters of the stereo cameras, as well as navigation data produced by navigation sensors of an inertial measuring unit of the vehicle (Jiang, ¶¶ 0010 and 0091: teaches IMUs (accelerometers) to compensate for ego-motion of the cameras relative to one another for extrinsic parameter calibration is well-known in the art; Jiang, e.g. ¶ 0091: teaches compensating for the extrinsic and intrinsic camera parameters are critical to properly relating the coordinates between the camera images).
Regarding claim 6, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the three-dimensional coordinates of the reconstructed pixels are defined in a local geographic system associated with the vehicle and yaw-corrected (Jiang, ¶¶ 0009, 0039, 0071, 0074, 0091, 0094, and 0096: teaches yaw correction is common for vehicle mounted stereovision systems).
Regarding claim 12, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the integration step comprises the steps, for each predefined obstacle instance (Obst), of determining, by using coordinates of the predefined obstacle instance and the three-dimensional coordinates of the reconstructed pixels, a distance between said predefined obstacle instance and the vehicle (1), as well as dimensions of said predefined obstacle instance (Jiang, ¶ 0097: teaches that with objects of known dimensions, a distance to that object may be determined; Examiner finds the skilled artisan likewise understands that if the distance is known, say from the depth map determined from stereo vision, then the object’s dimensions can be calculated similarly using the known distance).
Regarding claim 13, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the intermediate images are the rectified stereo images (Jiang, ¶ 0070: teaches the raw stereo images are rectified to align the images).
Regarding claim 16, the combination of Jiang, Filias, and Sinha teaches or suggests the method for detecting obstacles according to claim 1, wherein the stereo cameras are infrared cameras (Jiang, ¶ 0065: teaches the stereovision system can employ infrared cameras).
Claim 17 lists the same elements as claim 1, but is drawn to a system rather than a method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claim 18 lists the same elements as claim 17, but is drawn to a vehicle rather a system. Therefore, the rationale for the rejection of claim 17 applies to the instant claim.
Claim 20 lists the same elements as claim 1, but is drawn to a CRM rather than a method. Therefore, the rationale for the rejection of claim 1 applies to the instant claim.
Claims 8, 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang, Filias, Sinha, and Fua et al., “A parallel stereo algorithm that produces dense depth maps and preserves image features,” Machine Vision and Applications, vol. 6, pgs. 35–49 (1993).
Regarding claim 8, the combination of Jiang, Filias, Sinha, and Fua teaches or suggests the method for detecting obstacles according to claim 1, further comprising the step, preceding the implementation of the reconstruction algorithm, of verifying a validity of a disparity value of each pair of homologous pixels comprising a left pixel of a left image and a right pixel of a right image (Fua, Section 2.1: teaches checking validity of disparity values by correlating twice, once for the disparity between the left image and right image, and again for the disparity between the right image and the left image, i.e. “We perform the correlation twice by reversing the roles of the two images and consider as valid only those matches for which we measure the same depth at corresponding points when matching from I1 into I2 and I2 into I1.”), wherein the verification of the validity of the disparity of the pair of homologous pixels comprises the step of verifying that: dispg(xg , y) = dispd(xg - dispg(xg , y), y) and dispd(xd , y) = dispg(xd - dispd(xd , y), y) dispg being an estimated disparity by taking the left image as reference, dispd being an estimated disparity by taking the right image as reference, xg being a coordinate of the left pixel and xd being a coordinate of the right pixel (Fua, Section 2.1: teaches checking validity of disparity values by correlating twice, once for the disparity between the left image and right image, and again for the disparity between the right image and the left image, i.e. “We perform the correlation twice by reversing the roles of the two images and consider as valid only those matches for which we measure the same depth at corresponding points when matching from I1 into I2 and I2 into I1.”).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Jiang, Filias, and Sinha, with those of Fua, because all four references are drawn to the same field of endeavor such that one wishing to practice stereovision and disparity mapping would be led to their relevant teachings and because Fua is merely teaching what was already known about validating disparity values between images such that incorporating Fua’s teachings with those of Jiang’s and Sinha’s vehicle navigation systems represents a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Jiang, Filias, Sinha, and Fua used in this Office Action unless otherwise noted.
Regarding claim 9, the combination of Jiang, Filias, Sinha, and Fua teaches or suggests the method for detecting obstacles according to claim 1, further comprising the step, preceding the implementation of the reconstruction algorithm, of verifying a validity of a disparity value of each pair of homologous pixels comprising a left pixel of a left image and a right pixel of a right image (Fua, Section 2.1: teaches checking validity of disparity values by correlating twice, once for the disparity between the left image and right image, and again for the disparity between the right image and the left image, i.e. “We perform the correlation twice by reversing the roles of the two images and consider as valid only those matches for which we measure the same depth at corresponding points when matching from I1 into I2 and I2 into I1.”), wherein the verification of the validity of the disparity of the pair of homologous pixels comprises the step of implementing a post-accumulation mechanism (Examiner finds there is no explanation provided by Applicant’s Specification regarding what this mean; see published Spec. ¶ [0105]; Examiner finds the artisan would interpret Applicant’s post-accumulation mechanism to be disparity map refinement that utilizes perturbations or differently-sized moving windows to gain confidence/reliability in the disparity values whilst filtering out noise) by projecting disparity images passed over a current disparity image (Fua, Section 2.1: describes computing several disparity maps by shifting one of the images up or down and retaining valid matches having highest correlation scores; Fua, Section 2.2: describes merging disparity maps, whether through different resolutions or perturbations; This is post-accumulation).
Regarding claim 14, the combination of Jiang, Filias, Sinha, and Fua teaches or suggests the method for detecting obstacles according to claim 1, wherein the integration step comprises, for each predefined obstacle instance, the step of inlaying a cross on a barycentre of said predefined obstacle instance (Fua, Figs. B.1a and B.1b: illustrate inlayed crosses on barycenters of objects in an image; Examiner notes the crosses are merely a visual aid, much like bounding boxes to help the user visually perceive what the computer vision system is identifying and tracking; These types of pointers or crosshairs are ubiquitous in this field of endeavor as demonstrated by the prior art).
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang, Filias, Sinha, and Matzner (US 2020/0167938 A1).
Regarding claim 10, the combination of Jiang, Filias, Sinha, and Matzner teaches or suggests the method for detecting obstacles according to claim 1, comprising the step of determining that a group of pixels of a rectified segmented stereo image (Iser) forms a predefined obstacle instance when said pixels are connected together (Examiner notes that connecting pixels exhibiting similar characteristics, especially movement along a common trajectory, is how centroids/blobs/segmented objects/bounding boxes are identified within images using computer vision; Matzner, ¶ 0011: teaches, in a stereo vision application used for collision avoidance, connecting a group of adjacent pixels into blobs can segment an image into obstacles for tracking, including predefined obstacles such as wind turbines; see also Matzner, ¶ 0037: teaching two rectified images being matched to create a disparity map).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Jiang, Filias, and Sinha, with those of Matzner, because all four references are drawn to the same field of endeavor such that one wishing to practice stereovision and disparity mapping would be led to their relevant teachings and because Matzner is merely teaching what was already known about connecting pixels exhibiting similar characteristics into groups of pixels (blobs) representing objects or obstacles for purposes of tracking in stereo vision applications. Thus, incorporating Matzner’s teachings with those of Jiang’s and Sinha’s vehicle navigation systems represents a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Jiang, Filias, Sinha, and Matzner used in this Office Action unless otherwise noted.
Regarding claim 11, the combination of Jiang, Filias, Sinha, and Matzner teaches or suggests the method for detecting obstacles according to claim 10, wherein two pixels are connected together, if one of the two pixels belongs to the vicinity of the other, and if the two pixels belong to one same class (Matzner, ¶ 0011: teaches, in a stereo vision application used for collision avoidance, connecting a group of adjacent pixels into blobs can segment an image into obstacles for tracking, including predefined obstacles such as wind turbines).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Jiang, Filias, Sinha, and Tang (US 2024/0127455 A1).
Regarding claim 15, the combination of Jiang, Filias, Sinha, and Tang teaches or suggests the method for detecting obstacles according to claim 1, wherein the semantic segmentation algorithm uses a U-Net, HRNet, or HRNet+OCR neural network (Tang, ¶¶ 0022 and 0046: teach HRNet is an existing semantic segmentation neural network model).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Jiang, Filias, and Sinha, with those of Tang, because Tang is merely describing a prior art neural network solution to semantically segmenting images admitted by Applicant to be prior art. Thus, incorporating Tang’s teachings with those of Jiang’s and Sinha’s vehicle navigation systems represents a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Jiang, Filias, Sinha, and Tang used in this Office Action unless otherwise noted.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang (US 2024/0370991 A1) teaches image segmentation can use any one of the available segmentation models including Unet, HrNet, Deeplab, etc. (¶ 0064).
Tang (US 2024/0127455 A1) teaches HRNet may be used for semantic segmentation (e.g. ¶ 0022, 0046).
Praljak (US 2023/0221239 A1) teaches HR-net is the most well-known state-of-the-art segmentation model (¶ 0068).
Parra Pozo (2022/0413433 A1) teaches stereoscopic cameras, including IR cameras, on a separate device from a headset wherein the camera images are fed into the headset and can compute distances to objects (¶ 0080).
Kroepfl (US 11,698,272 B2) teaches stereo IR cameras and IMUs as part of an autonomous vehicle navigation system (col. 7, ln. 60–col. 8, ln. 28).
Alphonsus et al., “Disparity Map Adjustment: a Post-Processing Technique,” 2018 IEEE Symposium on Computers and Communications (ISCC), 2018. This publication teaches rectified stereo images and propagating the most common disparity values in a segmented stereo image to achieve disparity refinement, which is a post-accumulation mechanism (Section V).
Wang (US 2022/0073101 A1) teaches an environmental map and a list of obstacles and their geometric information (e.g. ¶ 0064) provided to an augmented reality display (e.g. ¶ 0165).
THIS ACTION IS MADE FINAL. 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 extension fee 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 Michael J Hess whose telephone number is (571)270-7933. The examiner can normally be reached Mon - Fri 9:00am-5:30pm.
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/MICHAEL J HESS/Examiner, Art Unit 2481
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