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 .
Claim Objections
Claim 9 is objected to because of the following informalities:
In claim 9, line 5, “images of the images” should read -- images --.
Appropriate correction is required.
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.
Claim(s) 1-4, 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camus (US 20050131646 A1) in view of Kwan (US 20190392555 A1), hereinafter after Kwan.
-Regrading claim 1, Camus discloses a method comprising (Abstract; FIGS. 1-4): converting images corresponding to sensor data to aligned images (FIG. 2; [0021], “preprocessor 206 calibrates the stereo cameras, captures and digitizes imagery, warps the images into alignment … stereo matching”; [0025], “imagery generated from each of the cameras is warped into alignment to facilitate producing disparity images”; [0026], “disparity images are created for each pair of frames”; [0034]); blending the aligned pixels to compute difference values indicating the error ([0022], “creating disparity image”; [0031], “determining a difference between each of the pixels in the depth image and each similarly positioned pixels in the object template. If the difference at each pixel is less than a predefined amount, the pixel is deemed a match”; [0034]); generating using the difference values, a detection map that identifies regions that corresponding to the one or more objects ([0022], “Each of the disparity images contains the point-wise motion”; [0026], “The disparity image … an indication of which of the disparity pixels in the image …”; [0027], “The disparity images are used to produce a depth map … from a disparity map … pixels belonging to objects in the image will have a depth …”; [0032], “A match score … indicative of the probability that the pixel is indicative of the object … regions … indicate a potential pedestrian” ); detecting one or more objects in an environment using the detection map (FIGS. 2-3; [0023]; [0027]; FIG. 4, steps 410-415) and performing one or more operations for a machine based at least on the detecting of the one or more objects (FIG. 3; FIG. 4, steps 420-430).
Camus does not disclose converting frames images corresponding to sensor data to aligned images having aligned pixels that represent a spatial alignment across the images of first features of a common surface in an environment to a common image plane that corresponds to the common surface, the aligned pixels indicating an error caused by the spatial alignment transforming second features that correspond to one or more objects situated over the common surface to the common image plane.
In the same field of endeavor, Kwan teaches a method for generating high-resolution stereo images and depth map in multi-camera systems having multiple cameras with different resolutions and view angles (Kwan: Abstract; FIGS. 1-19). Kwan further teaches converting frames images corresponding to sensor data to aligned images having aligned pixels that represent a spatial alignment across the images of first features of a common surface in an environment to a common image plane that corresponds to the common surface, the aligned pixels indicating an error caused by the spatial alignment transforming second features that correspond to one or more objects situated over the common surface to the common image plane (Kwan: FIGS. 1-5
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; [0042], “alignment approach is using RANSAC… Assuming the left camera image is the reference image, the right camera image content is then projected into a new image that is aligned with the reference image using the geometric transformation”; [0043], “reduces the registration errors to subpixel levels”; [0050], “Both SIFT and SURF features can be used … the feature correspondence can be achieved with RANSAC, which matches feature points that belong to the same physical locations … outliers are removed …”; [0051], “stereo rectification … applying a homography …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Camus with the teaching of Kwan by using Scale Invariant Features Transform (SIFT) or Speeded Up Robust Features (SURF) features with Random Sample Consensus (RANSAC) in order to improve the performance of stereo images matching.
-Regarding claim 2, Camus in view of Kwan teaches the method of claim 1. The combination further teaches wherein the detecting of the one or more objects includes determining one or more bounding shapes for one or more one or more groups of pixels of the detection map, and the one or more operations are performed based at least on the one or more bounding shapes (Camus: FIGS. 2-4; [0037], “returns “bounding boxes””; [0038]; [0041]; [0042]).
-Regarding claim 3, Camus in view of Kwan teaches the method of claim 1.
Camus does not disclose wherein the converting includes rectifying at least one of the images to the common image plane.
In the same field of endeavor, Kwan teaches a method for generating high-resolution stereo images and depth map in multi-camera systems having multiple cameras with different resolutions and view angles (Kwan: Abstract; FIGS. 1-19). Kwan further wherein the converting includes rectifying at least one of the images to the common image plane (Kwan: FIGS. 4-5, 12; [0036]; [0051], “stereo rectification …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Camus with the teaching of Kwan by using Scale Invariant Features Transform (SIFT) or Speeded Up Robust Features (SURF) features with Random Sample Consensus (RANSAC) in order to improve the performance of stereo images matching.
-Regarding claim 4, Camus in view of Kwan teaches the method of claim 1. The combination further teaches wherein the detecting the one or more objects includes combining pixels of the detection map into one or more groups of the pixels based at least on similarities between the difference values within the one or more groups, and the one or more objects correspond to the one or more groups (Camus: [0015], “The disparity images … depth maps … processed … a list of possible objects …. corresponding to all nearby high correlation scores …”; [0030], “similarity metric”; [0031], “matching”; [0034], “a measure of similarity”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camus (US 20050131646 A1) in view of Kwan (US 20190392555 A1), hereinafter after Kwan, and further in view of Liu et al (2014 ICSP), hereinafter Liu.
-Regarding claim 5, Camus in view of Kwan teaches the method of claim 1.
Camus in view of Kwan does not teach wherein the frames are captured using a single camera.
However, Liu is an analogous art pertinent to the problem to be solved in this application and teaches an object detection method based on disparity (Liu: Abstract; FIGS. 1-4). Liu further teaches wherein the frames are captured using a single camera (Liu: p. 770, 1st Col., 1st paragraph, “motion detection and object extraction method based on disparity or displacement between frames, which applied single camera without calibration”; FIGS. 1-3).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Camus in view of Kwan with the teaching of Liu by using a single camera to capture frames in order to perform motion detection and object detection without using complex calibration of the camera (Liu, p. 770, 1st Col., 1st paragraph).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camus (US 20050131646 A1) in view of Kwan (US 20190392555 A1), hereinafter after Kwan, and further in view of Klaus (US 20190087635 A1).
-Regarding claim 6, Camus in view of Kwan teaches the method of claim 1.
Camus in view of Kwan does not teach wherein the blending includes subtracting the aligned images from one another to produce a difference image that corresponds to the disparity image. However, a person of ordinary skills in the art would understand that it a common practice to calculate disparity image by subtracting the aligned images from one another.
Klaus is an analogous art pertinent to the problem to be solved in this application and teaches an object detection and avoidance method for aerial vehicles (Klaus: Abstract; FIGS. 1A-7B). Klaus further teaches wherein the blending includes subtracting the aligned images from one another to produce a difference image that corresponds to the detection map (Klaus: FIGS, 1F, 1H; FIG. 3, steps 320, 335; FIGS. 4-5; [0021], “aligned”; [0027], “disparities between the pixels appearing within the images 145-1, 145-2 may also be used to calculate a difference image 125. The difference image 125 may be calculated by subtracting absolute intensities of the images 145-1, 145-2, or by deriving intensity gradients for the images 145-1, 145-2 and comparing the intensity gradients to one another”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Camus in view of Kwan with the teaching of Klaus by blending pixel values of aligned pixels generating difference image in order to extract change areas from the difference image to indicate the presence of one or more objects in region of interests for object detection (Klaus: [0013]; [0026]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camus (US 20050131646 A1) in view of Kwan (US 20190392555 A1), hereinafter after Kwan, and further in view of Shu et al (2009 First International Conference on Information Science and Engineering, pp. 1203-1206), hereinafter Shu.
-Regarding claim 8, Camus in view of Kwan teaches the method of claim 1. The combination further teaches wherein the detection map includes a detection map in which first sets of the difference values having the differences determined to be greater than a threshold value are encoded with a first value and second sets of the disparity values having the differences determined to be less than the threshold value are encoded with a second value (Camus: [0039]-[0040]; [0043]).
Camus in view of Kwan does not teach wherein the disparity image includes a binary image.
However, Shu is an analogous art pertinent to the problem to be solved in this application and teaches a method to build dense disparity maps with high accuracy (Shu: Abstract; Figs. 1-8). Shu further teaches wherein the disparity image includes a binary image (Shu: Page 1, 2nd Col., 1st paragraph).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Camus in view of Kwan with the teaching of Shu by using the disparity image which includes a binary image in order to solve the problems of weak texture regions and disparity maps unsmooth.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Camus (US 20050131646 A1) in view of Kwan (US 20190392555 A1), hereinafter after Kwan, and further in view of Li et al (US 20190039633 A1), hereinafter Li.
-Regrading claim 9, Camus in view of Kwan teaches the method of claim 1.
Camus in view of Kwan does not teach determining geometry corresponding to one or more first objects in an environment depicted in the images; estimating, using the geometry, a scale of one or more features in one or more images, and based on at least on the scale, matching the one or more features across a temporal sequence of the images to determine one or more matched features.
However, Li is an analogous art pertinent to the problem to be solved in this application and teaches a method for detecting and reporting railroad track anomalies (Li: Abstract; FIGS. 1-5). Li further teaches determining geometry corresponding to one or more first objects in an environment depicted in the images (FIG. 1; FIG. 5, step 540; [0012], “determine the railroad track's geometry …”); estimating, using the geometry, a scale of one or more features in one or more images, and based on at least on the scale, matching the one or more features across a temporal sequence of the images ([0023], “process videos … detect railroad track …”; [0025], “continuously capture images … frames of a video …”; [0032]; [0041]; [0050], “real-time”) to determine one or more matched features (Li: (FIGS. 3, 5; [0034], “the normalization applied to match … transforming the received image 300 through scaling, translation … transform the received image 300 by matching features (e.g., RANSAC features) of pixels and/or regions in the image 300 to features in the corresponding reference image, determining a mapping between the received image 300 and the reference image that takes the matching features in the received image 300 to those in the reference image, and applying the mapping to transform the received image 300”; [0041]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Camus in view of Kwan with the teaching of Li by normalizing the images in order to improve the performance of the alignment.
Claim(s) 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klaus (US 20190087635 A1) in view of in view of Li et al (US 20190039633 A1), hereinafter Li, and further in view of Liu et al (US 11004212 B1), hereinafter Liu1;
-Regarding claim 10, Klaus discloses a system comprising: one or more processors (FIG. 1I, processor 112; FIG. 2, processor 212) to perform operations including (Abstract; FIGS.1A-7B): determining geometry corresponding to one or more first objects in an environment depicted in frames associated with sensor data ([0017], “capture one or more images of surfaces within a vicinity … recognize one or more edges, contours, outlines, colors, textures, silhouettes, shapes or other characteristics of the target marker 160”; FIGS. 1A-1I, 3); estimating, using the geometry corresponding to one or more first objects I an environment depicted in frames associated with sensor data (FIGS. 1C, 1E; [0016], “search for obstacles at the landing area when the aerial vehicle 110 reaches a predetermined altitude threshold … evaluate the landing area”; [0019], “define a landing area 165 upon detecting the target marker 160 at the destination 170”; [0021], “capture images … ten frames per second …”; [0023]); matching the one or more first features to one or more second features across the frames (Klaus: FIG. 1E. 1G, 1H, 3, 6; [0026], “matching algorithm … construct … landing area 165”; [0027]; [0029]); detecting one or more second objects in the environment based at least on the matching (FIGS. 1E, G, H, 1I; FIG. 3, steps 320-370; [0012]; [0014], “A plurality of obstacles (or obstructions) … within a vicinity of the target marker 160”; [0027]; [0071]); and performing one or more operations corresponding to a machine based at least on the detecting of the one or more second objects (FIG. 1I; FIG. 3, steps 360-370; [0028]-[0029]; [0078]).
Klaus does not disclose a scale indicating a change in a size of one or more features across a sequence of the frames and using the scale to compensate for the change in the size of the one or more first feature.
In the same field of endeavor, Li teaches a method for detecting and reporting railroad track anomalies (Li: Abstract; FIGS. 1-5). Li further teaches determining geometry corresponding to one or more first objects in an environment depicted in the images (FIG. 1; FIG. 5, step 540; [0012], “determine the railroad track's geometry …”); estimating, using the geometry, a scale indicating a change in a size of one or more features across a sequence of the frames and using the scale to compensate for the change in the size of the one or more first feature (FIGS. 3, 5; [0023], “process videos … detect railroad track …”; [0025], “continuously capture images … frames of a video …”; [0032]; [0041]; [0050], “real-time”; [0034], “the normalization applied to match … transforming the received image 300 through scaling, translation … transform the received image 300 by matching features (e.g., RANSAC features) of pixels and/or regions in the image 300 to features in the corresponding reference image, determining a mapping between the received image 300 and the reference image that takes the matching features in the received image 300 to those in the reference image, and applying the mapping to transform the received image 300”; [0041]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Klaus with the teaching of Li by normalizing the images in order to improve the performance of the alignment.
Klaus in view of Li does not teach the scale as a function of a proximity of a machine to the one or more first features in the environment. However, Klaus discloses determining and matching target market or landing areas by determining pixel disparities between previous captured images by an unmanned aerial vehicle (UAV) (Klaus: FIGS. 1, 3). A person of ordinary skills in the art would understand that a scale of the captured images by UAV for normalization and aligning with reference image has to be as a function of a distance between the UAV and target marker due to the movement of the UAV.
Liu1 is an analogous art pertinent to the problem to be solved in this application and teaches a method for tracking a position and orientation of a target object (Liu1: FIGS. 1-13). Liu1 further teaches the scale as a function of a proximity of a machine to the one or more first features in the environment (Liu: FIGS. 1-3, 512; Col. 1, last paragraph – Col. 2, 1st paragraph, “template matching … scale”; Col. 2 line 37 – Col. 3, line 27, “size the region of interest … template matching”; Col. 5, lines 64 – Col. 6, line 5, “a camera-captured object scale depends only on the movement of the target object … different scale is due to perspective scaling … target object 104 may appear to be bigger or smaller due to perspective and the variance in distance between the target object 104 and camera …”; Col. 9, line 63 – Col. 10, line 10, “perspective scaling is performed to correct the scaling … template matching is performed).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Klaus in view of Li with the teaching of Liu1 by using scale as a function of a proximity of a machine to the one or more first features in the environment in order to provide a correct scale for image or feature normalization.
-Regarding claim 16, Klaus discloses at least one processor comprising (Abstract; FIGS. 1A-7B): processing circuitry to perform one or more operations for a machine based at least on detecting one or more objects using one or more first matched features matched to one or more second features across frames associated with sensor data ([0017], “capture one or more images of surfaces within a vicinity … recognize one or more edges, contours … shapes or other characteristics of the target marker 160”; [0019], “landing area 165”; [0021], “aligned with fields of view that include the target marker 160 and the landing area 165, and overlap at least in part, and may be configured to capture images at any frame rate …”; [0026]-[0027]; FIGS. 1A-1I, 3-5), the one or more matched features is estimated based at least on analyzing the sensor data (FIGS. 1A-1I, 3-5).
Klaus does not disclose a scale indicating a change in a size of one or more features across a sequence of the frames and using the scale to compensate for the change in the size of the one or more first feature.
In the same field of endeavor, Li teaches a method for detecting and reporting railroad track anomalies (Li: Abstract; FIGS. 1-5). Li further teaches determining geometry corresponding to one or more first objects in an environment depicted in the images (FIG. 1; FIG. 5, step 540; [0012], “determine the railroad track's geometry …”); estimating, using the geometry, a scale indicating a change in a size of one or more features across a sequence of the frames and using the scale to compensate for the change in the size of the one or more first feature (FIGS. 3, 5; [0023], “process videos … detect railroad track …”; [0025], “continuously capture images … frames of a video …”; [0032]; [0041]; [0050], “real-time”; [0034], “the normalization applied to match … transforming the received image 300 through scaling, translation … transform the received image 300 by matching features (e.g., RANSAC features) of pixels and/or regions in the image 300 to features in the corresponding reference image, determining a mapping between the received image 300 and the reference image that takes the matching features in the received image 300 to those in the reference image, and applying the mapping to transform the received image 300”; [0041]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Klaus with the teaching of Li by normalizing the images in order to improve the performance of the alignment.
Klaus in view of Li does not teach the scale as a function of a proximity of a machine to the one or more first features in the environment. However, Klaus discloses determining and matching target market or landing areas by determining pixel disparities between previous captured images by an unmanned aerial vehicle (UAV) (Klaus: FIGS. 1, 3). A person of ordinary skills in the art would understand that a scale of the captured images by UAV for normalization and aligning with reference image has to be as a function of a distance between the UAV and target marker due to the movement of the UAV.
Liu1 is an analogous art pertinent to the problem to be solved in this application and teaches a method for tracking a position and orientation of a target object (Liu1: FIGS. 1-13). Liu1 further teaches the scale as a function of a proximity of a machine to the one or more first features in the environment (Liu: FIGS. 1-3, 512; Col. 1, last paragraph – Col. 2, 1st paragraph, “template matching … scale”; Col. 2 line 37 – Col. 3, line 27, “resize the region of interest … template matching”; Col. 5, lines 64 – Col. 6, line 5, “a camera-captured object scale depends only on the movement of the target object … different scale is due to perspective scaling … target object 104 may appear to be bigger or smaller due to perspective and the variance in distance between the target object 104 and camera …”; Col. 9, line 63 – Col. 10, line 10, “perspective scaling is performed to correct the scaling … template matching is performed).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Klaus in view of Li with the teaching of Liu1 by using scale as a function of a proximity of a machine to the one or more first features in the environment in order to provide a correct scale for image or feature normalization.
-Regarding claim 11, Klaus in view of Li, and further in view of Liu1 teaches the system of claim 10. The modification further teaches wherein the one or more first objects correspond to one or more lanes in the environment or one or more freespace regions in the environment (Klaus: FIGS. 1A-1I, 3-5).
-Regarding claims 12 and 18, Klaus in view of Li, and further in view of Liu1 teaches the system of claim 10, and the processor of claim 16. The modification teaches wherein the matching uses the one or more instances extracted from the one or more ROIs (Klaus: FIGS. 1A-1I, 3-5; [0032]; [0084]; [0087]-[0088]).
Klaus in view of Li not teaching adjusting, based at least on the scale, one or more dimensions of one or more regions of interest (ROIs) in the one or more frames; and based at least on the adjusting, extracting one or more instances of the one or more features from the one or more ROIs.
Liu1 is an analogous art pertinent to the problem to be solved in this application and teaches a method for tracking a position and orientation of a target object (Liu1: FIGS. 1-13). Liu1 further teaches based at least on the scale, one or more dimensions of one or more regions of interest (ROIs) in the one or more frames; and based at least on the adjusting, extracting one or more instances of the one or more features from the one or more ROIs (Liu: FIGS. 1-3, 512; Col. 1, last paragraph – Col. 2, 1st paragraph; Col. 2 line 37 – Col. 3, line 27, “resize the region of interest … template matching”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Klaus in view of Li with the teaching of Liu1 by using scale as a function of a proximity of a machine to the one or more first features in the environment in order to provide a correct scale for image or feature normalization.
-Regarding claim 13 and 19, Klaus in view of Li, and further in view of Liu1 teaches the system of claim 10, and he processor of claim 16.
Klaus in view of Li does not teach wherein the machine is closer to the one or more second objects for a first frame of the frames than for a second frame of the frames.
Liu1 is an analogous art pertinent to the problem to be solved in this application and teaches a method for tracking a position and orientation of a target object (Liu1: FIGS. 1-13). Liu1 further teaches wherein the machine is closer to the one or more second objects for a first frame of the frames than for a second frame of the frames (Liu: FIGS. 1-3, 512; Col. 1, last paragraph – Col. 2, 1st paragraph, “template matching … scale”; Col. 2 line 37 – Col. 3, line 27, “resize the region of interest … template matching”; Col. 5, lines 64 – Col. 6, line 5, “a camera-captured object scale depends only on the movement of the target object … different scale is due to perspective scaling … target object 104 may appear to be bigger or smaller due to perspective and the variance in distance between the target object 104 and camera …”; Col. 9, line 63 – Col. 10, line 10, “perspective scaling is performed to correct the scaling … template matching is performed).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Klaus in view of Li with the teaching of Liu1 by using scale as a function of a proximity of a machine to the one or more first features in the environment in order to provide a correct scale for image or feature normalization.
-Regarding claim 14, Klaus in view of Li, and further in view of Liu1 teaches the system of claim 10.
The modification further teaches converting the frames to aligned images based on the one or more matched features (Klaus: FIGS. 1D, 4; [0021]; [0043]; [0084]); blending pixel values of aligned pixels between the aligned images to compute difference values indicating differences between the pixel values across the aligned pixels (Klaus: FIGS, 1F, 1H; FIG. 3, steps 320, 335; FIGS. 4-5; [0021], “aligned”; [0027], “disparities between the pixels appearing within the images 145-1, 145-2 may also be used to calculate a difference image 125. The difference image 125 may be calculated by subtracting absolute intensities of the images 145-1, 145-2, or by deriving intensity gradients for the images 145-1, 145-2 and comparing the intensity gradients to one another”); generating a disparity image having disparity values corresponding to the difference values and indicating the differences between the pixel values across the aligned pixels (Klaus: [0027]; [0076]); and determining one or more bounding shapes for one or more one or more groups of the pixels of the disparity image (Klaus: FIGS. 1A-1I).
-Regarding claims 15 and 20, Klaus in view of Li, and further in view of Liu1 teaches the system of claim 10, and he processor of claim 16. The modification further teaches wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Klaus: FIGS. 1A-3; [0012]).
-Regarding claim 17, Klaus in view of Li, and further in view of Liu1 teaches the processor of claim 16.
Klaus in view of Li teaches wherein the target marker and landing area (or regions of interest or ROIs) are estimated based at least on one or more of: at least one road profile associated with the one or more features; at least one lane geometry associated with the one or more features; or ego-motion associated with the one or more features (Klaus: FIGS. 1A-5).
Klaus in view of Li does not teach scaling one or more features such as regions of interest (ROIs).
Liu1 is an analogous art pertinent to the problem to be solved in this application and teaches a method for tracking a position and orientation of a target object (Liu1: FIGS. 1-13). Liu1 further teaches scaling one or more features such as regions of interest (ROIs) (Liu: FIGS. 1-3, 512; Col. 1, last paragraph – Col. 2, 1st paragraph; Col. 2 line 37 – Col. 3, line 27, “resize the region of interest … template matching”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Klaus in view of Li with the teaching of Liu1 by using scale as a function of a proximity of a machine to the one or more first features in the environment in order to provide a correct scale for image or feature normalization.
Allowable Subject Matter
Claim 7 is 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.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al (Integration of optimal spatial distributed tie-points in RANSAC-based image registration, European Journal of Remote Sensing, 10 Feb 2020), hereinafter Zhang teaches an outlier method to find correct matching results of optimal distribution based on RANSAC algorithm.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/XIAO LIU/ Primary Examiner, Art Unit 2664
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