DETAILED ACTIONNotice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/3/2025 has been entered.
Examiner's Note
The instant application has a lengthy prosecution history and the examiner encourages the applicant to have a telephonic interview with the examiner prior to filing a response to the instant office action. Also, prior to the interview the examiner encourages the applicant to present multiple possible claim amendments, so as to enable the examiner to identify claim amendments that will advance prosecution in a meaningful manner.
Acknowledgment
Claim 1 and 13-15, amended on 12/3/2025, are acknowledged by the examiner.
Claim 12, canceled on 12/3/2025, is acknowledged by the examiner.
Response to Arguments
Presented arguments with respect to claims 1, 14, 15, and their dependent claims have been fully considered, but some are rendered moot in view of the new ground of rejection necessitated by amendments initiated by the applicants. Examiner addresses the main arguments of the Applicant as below.
Regarding the drawing objection, the amendment filed on 12/3/2025 addresses the issue for "a correct image". As a result, the drawing objection related to the "correct image" is withdrawn.
Regarding the 35 U.S.C. 112(a) rejection, the Remarks filed on 12/3/2025 is persuasive. As a result, the 35 U.S.C. 112(a) rejection related to the optical distortion correction and the perspective distortion correction is withdrawn.
Objections
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, “optical distortion correction”, “perspective distortion correction”, “historical imagery data” must be shown or the feature(s) must be canceled from the claims 1-11 and 13-20. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-11 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Annovi (US Patent 10,480,939 B2), (“Annovi”), in view of Tian et al. (US Patent Application Publication 2024/0031675 A1), (“Tian”), in view of Pawlicki et al. (US Patent 11,203,340 B2), (“Pawlicki”).
Regarding claim 1, Annovi meets the claim limitations, as follows:
A method of identifying road distress on a road ((systems and methods for accurate detection and assessment of pavement) [Annovi: col. 1, line 15-16]; (a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]) based on imagery of the road (Imaging systems, which use a camera or sets of cameras and lighting systems to record a view of the pavement surface. These systems usually use high resolution line scan cameras for accurate imaging. The individual lines scanned by the camera are stitched after some distance to get a two-dimensional image of the area scanned. They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 43-52], the method comprising (systems and methods for accurate detection and assessment of pavement) [Annovi: col. 1, line 15-16]:
identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] a region of interest from road regions detected (obtain one 3D range map for the entire region of interest) [Annovi: col. 8, line 41-42] in the imagery of the road (obtained using the disparity image which is obtained using the grayscale images) [Annovi: col. 8, line 41-42], wherein the region of interest (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27] includes possible road distresses ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]);
detecting at least a first possible road distresses present (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] in the region of interest ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]) by identifying features associated with physical characteristics ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting and Transverse Profiling: Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) related to road distress in an original image including the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 67 – col 3, line 4]), and wherein the imagery of the road is obtained via a forward looking camera mounted on a vehicle ((First describing the system and method at a high level, FIG. 7 discloses an illustrative system and method in which left and right stereo images 712, 714 are acquired by a vehicle mounted system as described in the specification.) [Annovi: col. 10, line 6-10; Figs. 1-2, 7]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52] – Note: Annovi discloses that the camera can capture the road in the direction where the vehicle travels);
detecting at least a second possible road distresses (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] present in the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]) by identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] features associated with physical characteristics related to road distress ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) in a corrected image obtained by applying at least one of optical distortion correction and perspective distortion correction to the original image including the region of interest (The movement data from the IMU is used to augment the data captured by the image capturing devices to correct for pavement abnormalities and obtain more accurate 3D estimates) [Annovi: col. 6, line 58-61];
combining the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed); and
identifying one or more road distresses (identify the potholes on road) [Annovi: col. 13, line 54-55] from the combining of the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed).
Annovi does not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle
a corrected image obtained applying at least one of optical distortion correction and perspective distortion correction to the original image.
However, in the same field of endeavor Tian further discloses the deficient claim limitations as follows:
applying at least one of optical distortion correction and perspective distortion correction to the original image (In an embodiment, the performing perspective distortion
correction on the first cropped area includes: performing optical distortion correction on the first cropped area to obtain a corrected first cropped area) [Tian: para. 0061].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi with Tian to program the system to implement of Tian’s method.
Therefore, the combination of Annovi with Tian will enable the system to improve image/video output [Tian: para. 0061].
Annovi and Tian do not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle
However, in the same field of endeavor Pawlicki further discloses the deficient claim limitations as follows:
via a forward looking camera mounted on a vehicle (an object detection system or imaging system of the present invention may comprise a forward facing lane departure warning system 110 (FIG. 1), which may include an image sensor or camera 114 and a control 116 positioned on or at vehicle 12. Lane departure warning system 110 is generally shown at the front of the vehicle 12 with camera 114 positioned and oriented to capture an image of the region generally forwardly of the vehicle) [Tian: col. 21, line 32-39; Figs. 1-4].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi and Tian with Pawlicki to program the system to implement of Pawlicki’s method.
Therefore, the combination of Annovi and Tian with Pawlicki will enable the system to capture an image of a scene occurring forwardly of the vehicle and control is operable to process image data of the captured images or frames to detect and monitor or track lane markers or road edges or the like or oncoming or approaching vehicles or objects, and to provide a warning or alert signal to a driver of the vehicle [Tian: col. 21, line 45-51; Figs. 1-4].
Regarding claims 2 and 16, Annovi meets the claim limitations as set forth in claims 1 and 14. Annovi further meets the claim limitations as follow.
determining road regions in the imagery of the road (obtain one 3D range map for the entire region of interest) [Annovi: col. 8, line 41-42] by categorizing different features (Once the pavement has been classified through one or both techniques, the information is stored to a database at 728 for retrieval and further analysis as may be required, whether on board the vehicle, or transmitted by use of a wireless communication module (e.g. wireless module 530 of FIG. 3) to a remote location) [Annovi: col. 13, line 30-35], present in an image (A file compression 506 technique such as GeoTIF, JPEG encoding, ZIP encoding and LZW encoding is applied to minimize the sizes of the combined stereoscopic 3D images, and save them to a data storage device 510 on board) [Annovi: col. 8, line 62-65; Figs. 3, 4A-C, 7], related to different objects into different classes ((detecting objects in an image) [Annovi: col. 9, line 60-61]; (After image capturing, stereoscopic 3D reconstruction and image stitching, the images obtained are contrast normalized intensity images containing image intensity data (which may be gray scale), and 3D elevation/depth range images which are combined into a stereoscopic 3D image containing image intensity data. This stereoscopic 3D image is viewable as a 3D image rendered on a 2D computer monitor or screen, or viewable in stereoscopic 3D with suitable 3D glasses. With appropriate formatting as may be necessary, the 3D image may also be viewed in a virtual 3D environment, using a commercially available stereoscopic virtual reality viewer, for example. Such a virtual 3D viewing environment may render pavement distress features in the stereoscopic 3D image to be more readily noticeable, in comparison to a flattened rendering of a 3D image on a 2D computer monitor or screen. Once such a feature is identified, the viewing angle of the 3D image may also be manipulated to allow the pavement surface to be viewed from different points of view) [Annovi: col. 8, line 43-61].
Regarding claims 3 and 17, Annovi meets the claim limitations as set forth in claims 2 and 16. Annovi further meets the claim limitations as follow.
wherein the determining (The condition of the pavement can be determined) [Annovi: col. 1, line 27-28] is performed by a first machine learning model (the one or more distress detection modules further comprise a machine learning module) [Annovi: col. 3, line 34-35] separating and categorizing the features present in the image using semantic segmentation (In an embodiment, part of the distress detection algorithm is based on the mask region-based CNN (Mask R-CNN) architecture. This architecture simultaneously performs object detection and instance segmentation, making it useful for a range of automated inspection tasks) [Annovi: col. 12, line 11-16].
Regarding claims 4 and 18, Annovi meets the claim limitations as set forth in claims 2 and 16. Annovi further meets the claim limitations as follow.
identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] a road lane on the determined road regions (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52] by considering at least one of lane lines, curb features, and half of a full pavement width ((At each pulse, the individual cameras of a stereoscopic pair capture a line of pavement surface illuminated by the illumination source. The captured lines are then digitized into a line of grayscale intensities using the frame grabber card. The frame grabber captures a fixed number of such lines and stitches them together one line after another to form a two dimensional (2D) image) [Annovi: col. 7, line 4-10]; (produce profiles at fixed intervals along a fixed number of lines on the road) [Annovi: col. 1, line 59-60].
Regarding claims 5 and 19, Annovi meets the claim limitations as set forth in claims 2 and 16. Annovi further meets the claim limitations as follow.
wherein the identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] is performed by a second machine learning model (the one or more distress detection modules further comprise a machine learning module) [Annovi: col. 3, line 34-35] trained with lane data ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (The results of the learning process will be a trained machine learning model that can be executed on an input image set to discover a detailed descriptions of the targeted type of pavement distress. The images used in this process, depending of the type of distress, are both intensity images and depth images. The algorithm is thus learning the structure of the pavement distress. A trained machine learning model can detect and geo-reference distresses such as, but not limited to, cracks, potholes, rutting and transverse profiling, bleeding, and patching. This detection dataset is then used in an "online learning process" to continuously improve the accuracy of the model) [Annovi: col. 12, line 34-46]) for identifying road lanes ((At each pulse, the individual cameras of a stereoscopic pair capture a line of pavement surface illuminated by the illumination source. The captured lines are then digitized into a line of grayscale intensities using the frame grabber card. The frame grabber captures a fixed number of such lines and stitches them together one line after another to form a two dimensional (2D) image) [Annovi: col. 7, line 4-10]; (produce profiles at fixed intervals along a fixed number of lines on the road) [Annovi: col. 1, line 59-60].
Regarding claims 6 and 20, Annovi meets the claim limitations as set forth in claims 2 and 16. Annovi further meets the claim limitations as follow.
wherein the identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] the region of interest is performed (obtain one 3D range map for the entire region of interest) [Annovi: col. 8, line 41-42] further based on historical imagery data of the region of interest (Once the pavement has been classified through one or both techniques, the information is stored to a database at 728 for retrieval and further analysis as may be required, whether on board the vehicle, or transmitted by use of a wireless communication module (e.g. wireless module 530 of FIG. 3) to a remote location.) [Annovi: col. 8, line 41-42]
Regarding claim 7, Annovi meets the claim limitations as set forth in claim 1. Annovi further meets the claim limitations as follow.
wherein the physical characteristics related to road distress comprises one or more of a pothole, an alligator cracks, a longitudinal crack, and a transverse crack ((The results of the learning process will be a trained machine learning model that can be executed on an input image set to discover a detailed descriptions of the targeted type of pavement distress. The images used in this process, depending of the type of distress, are both intensity images and depth images. The algorithm is thus learning the structure of the pavement distress. A trained machine learning model can detect and geo-reference distresses such as, but not limited to, cracks, potholes, rutting and transverse profiling, bleeding, and patching. This detection dataset is then used in an "online learning process" to continuously improve the accuracy of the model) [Annovi: col. 12, line 34-46]; (A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54]; (The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]).
Regarding claim 8, Annovi meets the claim limitations, as follows:
wherein features associated with physical characteristics related to road distress comprises crack width or crack length ((The 3D image can be used along with the contrast normalized intensity images containing image intensity data to improve the distress detection, especially, cracking 604. Cracks are identified both in the gradient and intensity images. Both the shape and intensity is then used to classify the features as cracks, sealed cracks or other road features) [Annovi: col. 9, line 30-35]; (Through the system, cracks could be further classified into regular, sealed, longitudinal, transverse and alligator cracks) [Annovi: col. 8, line 41-42] – Note: longitudinal cracks are classified by the crack’s length).
Regarding claim 9, Annovi meets the claim limitations as set forth in claim 1. Annovi further meets the claim limitations as follow.
wherein the detecting one or more distresses in the original image and in the corrected image are performed (The movement data from the IMU is used to augment the data captured by the image capturing devices to correct for pavement abnormalities and obtain more accurate 3D estimates) [Annovi: col. 6, line 58-61] by a third machine learning model (the one or more distress detection modules further comprise a machine learning module) [Annovi: col. 3, line 34-35] identifying features associated with physical characteristics related to road distress ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (The results of the learning process will be a trained machine learning model that can be executed on an input image set to discover a detailed descriptions of the targeted type of pavement distress. The images used in this process, depending of the type of distress, are both intensity images and depth images. The algorithm is thus learning the structure of the pavement distress. A trained machine learning model can detect and geo-reference distresses such as, but not limited to, cracks, potholes, rutting and transverse profiling, bleeding, and patching. This detection dataset is then used in an "online learning process" to continuously improve the accuracy of the model) [Annovi: col. 12, line 34-46]; (In an embodiment, part of the distress detection algorithm is based on the mask region-based CNN (Mask R-CNN) architecture. This architecture simultaneously performs object detection and instance segmentation, making it useful for a range of automated inspection tasks) [Annovi: col. 12, line 11-16]).
Regarding claim 10, Annovi meets the claim limitations as set forth in claim 1. Annovi further meets the claim limitations as follow.
further comprising identifying (The system and method uses the depth information of pavement as a feature to identify) [Annovi: col. 13, line 53-54] wheels path of a vehicle capturing the imagery of the road (such as road markings, wheel marks) [Annovi: col. 9, line 42]
Regarding claim 11, Annovi meets the claim limitations as set forth in claim 1. Annovi further meets the claim limitations as follow.
further comprising assigning a rating to the one or more identified distresses (According to the user preferences, the module may also display the detected distresses overlaid on the intensity image. The distresses displayed may be color-coded in different colors to indicate the level of severity) [Annovi: col. 9, line 65 - col. 10, line 2].
Regarding claim 13, Annovi meets the claim limitations as set forth in claim 1. Annovi further meets the claim limitations as follow.
wherein the imagery is captured at a fixed interval (At each pulse, the individual cameras of a stereoscopic pair capture a line of pavement surface illuminated by the illumination source. The captured lines are then digitized into a line of grayscale intensities using the frame grabber card. The frame grabber captures a fixed number of such lines and stitches them together one line after another to form a two dimensional (2D) image) [Annovi: col. 7, line 4-10].
Regarding claim 14, Annovi meets the claim limitations, as follows:
A device ((systems and methods for accurate detection and assessment of pavement) [Annovi: col. 1, line 15-16]; (a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]) comprising:
a processor (a processor) [Annovi: col. 10, line 27; Figs. 3, 4A-C, 7];
a memory (data storage) [Annovi: col. 10, line 27; Figs. 3, 4A-C, 7] storing instructions (A file compression 506 technique such as GeoTIF, JPEG encoding, ZIP encoding and LZW encoding is applied to minimize the sizes of the combined stereoscopic 3D images, and save them to a data storage device 510 on board) [Annovi: col. 8, line 62-65; Figs. 3, 4A-C, 7], which when executed by the processor, causes the device to (It will be understood that any reference to a system or to a method as executed on the system may involve theses processors and modules as previously described) [Annovi: col. 10, line 30-33; Fig. 7]:
identify a region of interest from road regions detected (obtain one 3D range map for the entire region of interest) [Annovi: col. 8, line 41-42] in the imagery of the road (obtained using the disparity image which is obtained using the grayscale images) [Annovi: col. 8, line 41-42], wherein the region of interest (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27] includes possible road distresses ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]), and wherein the imagery of the road is obtained via a forward looking camera mounted on a vehicle ((First describing the system and method at a high level, FIG. 7 discloses an illustrative system and method in which left and right stereo images 712, 714 are acquired by a vehicle mounted system as described in the specification.) [Annovi: col. 10, line 6-10; Figs. 1-2, 7]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52] – Note: Annovi discloses that the camera can capture the road in the direction where the vehicle travels);
detect at least a first possible road distresses present (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] in the region of interest ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]) by identifying features associated with physical characteristics ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting and Transverse Profiling: Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) related to road distress in an original image including the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 67 – col 3, line 4]);
detect at least a second possible road distresses (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] present in the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]) by identifying features associated with physical characteristics related to road distress ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) in a corrected image obtained by applying at least one of optical distortion correction and perspective distortion correction to the original image including the region of interest (The movement data from the IMU is used to augment the data captured by the image capturing devices to correct for pavement abnormalities and obtain more accurate 3D estimates) [Annovi: col. 6, line 58-61];
combine the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed); and
identify one or more road distresses (identify the potholes on road) [Annovi: col. 13, line 54-55] from the combinination of the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed).
Annovi does not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle.
a corrected image obtained applying at least one of optical distortion correction and perspective distortion correction to the original image.
However, in the same field of endeavor Tian further discloses the deficient claim limitations as follows:
a memory storing instructions (The memory is configured to store computer programs or instructions) [Tian: para. 0144];
applying at least one of optical distortion correction and perspective distortion correction to the original image (In an embodiment, the performing perspective distortion correction on the first cropped area includes: performing optical distortion correction on the first cropped area to obtain a corrected first cropped area) [Tian: para. 0061].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi with Tian to program the system to implement of Tian’s method.
Therefore, the combination of Annovi with Tian will enable the system to improve image/video output [Tian: para. 0061].
Annovi and Tian do not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle
However, in the same field of endeavor Pawlicki further discloses the deficient claim limitations as follows:
via a forward looking camera mounted on a vehicle (an object detection system or imaging system of the present invention may comprise a forward facing lane departure warning system 110 (FIG. 1), which may include an image sensor or camera 114 and a control 116 positioned on or at vehicle 12. Lane departure warning system 110 is generally shown at the front of the vehicle 12 with camera 114 positioned and oriented to capture an image of the region generally forwardly of the vehicle) [Tian: col. 21, line 32-39; Figs. 1-4].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi and Tian with Pawlicki to program the system to implement of Pawlicki’s method.
Therefore, the combination of Annovi and Tian with Pawlicki will enable the system to capture an image of a scene occurring forwardly of the vehicle and control is operable to process image data of the captured images or frames to detect and monitor or track lane markers or road edges or the like or oncoming or approaching vehicles or objects, and to provide a warning or alert signal to a driver of the vehicle [Tian: col. 21, line 45-51; Figs. 1-4].
Regarding claim 15, Annovi meets the claim limitations, as follows:
A non-transitory computer readable storage medium (data storage) [Annovi: col. 10, line 27; Figs. 3, 4A-C, 7] storing instructions (A file compression 506 technique such as GeoTIF, JPEG encoding, ZIP encoding and LZW encoding is applied to minimize the sizes of the combined stereoscopic 3D images, and save them to a data storage device 510 on board) [Annovi: col. 8, line 62-65; Figs. 3, 4A-C, 7] which, when executed on at least one processor, cause the at least one processor to (It will be understood that any reference to a system or to a method as executed on the system may involve theses processors and modules as previously described) [Annovi: col. 10, line 30-33; Fig. 7]:
identify a region of interest from road regions detected (obtain one 3D range map for the entire region of interest) [Annovi: col. 8, line 41-42] in the imagery of the road (obtained using the disparity image which is obtained using the grayscale images) [Annovi: col. 8, line 41-42], wherein the region of interest (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27] includes possible road distresses ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]), and wherein the imagery of the road is obtained via a forward looking camera mounted on a vehicle ((First describing the system and method at a high level, FIG. 7 discloses an illustrative system and method in which left and right stereo images 712, 714 are acquired by a vehicle mounted system as described in the specification.) [Annovi: col. 10, line 6-10; Figs. 1-2, 7]; (They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52] – Note: Annovi discloses that the camera can capture the road in the direction where the vehicle travels);
detect at least a first possible road distresses present (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] in the region of interest ((a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]) by identifying features associated with physical characteristics ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting and Transverse Profiling: Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) related to road distress in an original image including the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 67 – col 3, line 4]);
detect at least a second possible road distresses (the computer vision algorithm to first detect and classify a pavement distress) [Annovi: col. 13, line 10-11] present in the region of interest ((They capture an entire area of the lane in which the survey vehicle is travelling in. Surface data captured with these systems are usually used for distress detection) [Annovi: col. 1, line 49-52]; (In an embodiment, the light sources 130A, 130B used to illuminate an area of interest) [Annovi: col. 5, line 26-27]; (a high speed pavement stereoscopic line scan imaging system and method capable of producing a stereoscopic 3D image of the pavement surface using a stereoscopic image capturing apparatus, or any number of such devices and lighting source(s) for accurate detection of pavement distresses, and assessment of the pavement surface quality) [Annovi: col. 2, line 65 – col 3, line 4]) by identifying features associated with physical characteristics related to road distress ((A number of illustrative use cases will now be described to provide examples of how the present system and method may be used in practice to detect pavement distresses. Cracks: The input to the machine learning algorithm is the depth image and the accuracy of the training is highly dependent on the quality of the depth image. The quality of depth image may vary depending on several external factors such as bad lighting, lose calibration or extreme weather conditions) [Annovi: col. 13, line 37-45]; (Potholes: The system and method uses the depth information of pavement as a feature to identify the potholes on road. What really separates potholes from cracks is the surface area and the depth is large compared to the cracks) [Annovi: col. 13, line 53-56]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]) in a corrected image obtained by applying at least one of optical distortion correction and perspective distortion correction to the original image including the region of interest (The movement data from the IMU is used to augment the data captured by the image capturing devices to correct for pavement abnormalities and obtain more accurate 3D estimates) [Annovi: col. 6, line 58-61];
combine the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed); and
identify one or more road distresses (identify the potholes on road) [Annovi: col. 13, line 54-55] from the combining of the at least first possible road distress from the original image and the at least second possible road distress from the corrected image ((The system and process can combine the depth value of 3D image and color value of intensity image to identify the patch. Thus, in an aspect, there is provided a mobile pavement surface scanning system for detecting pavement distress) [Annovi: col. 14, line 27-31]; (Rutting is the depression left on road in the wheel path and to measure this - the system and method uses the depth image. Preprocessing is done on depth image such as noise filtering. Rut is identified by finding the depth pixels in wheel path within a certain threshold. The minimum of 12 points or depth connected pixels are required to define a rut. Transverse profiling is the measure of unevenness on the pavement surface calculated similar to rut) [Annovi: col. 13, line 66 – col. 14, line 7]; (In the display module 603, the data produced can be displayed directly to the user on the on-board monitor. The display module may display just the intensity image or a combined intensity image and 3D elevation image. According to the user preferences, the module may also display the detected distresses overlaid on the intensity image) [Annovi: col. 9, line 63-67] – Note: Annovi discloses a method that combines images to detect the road distresses. Annovi discusses an approach for generating the corrected image by filtering and determining the road distress in the corrected image by comparing to a threshold. In addition, Annovi discloses that a combine image can be displayed).
Annovi does not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle.
a corrected image obtained applying at least one of optical distortion correction and perspective distortion correction to the original image.
However, in the same field of endeavor Tian further discloses the deficient claim limitations as follows:
a non- transitory computer readable storage medium storing instructions (The memory is configured to store computer programs or instructions) [Tian: para. 0144];
applying at least one of optical distortion correction and perspective distortion correction to the original image (In an embodiment, the performing perspective distortion
correction on the first cropped area includes: performing optical distortion correction on the first cropped area to obtain a corrected first cropped area) [Tian: para. 0061].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi with Tian to program the system to implement of Tian’s method.
Therefore, the combination of Annovi with Tian will enable the system to improve image/video output [Tian: para. 0061].
Annovi and Tian do not explicitly disclose the following claim limitations (Emphasis added).
via a forward looking camera mounted on a vehicle
However, in the same field of endeavor Pawlicki further discloses the deficient claim limitations as follows:
via a forward looking camera mounted on a vehicle (an object detection system or imaging system of the present invention may comprise a forward facing lane departure warning system 110 (FIG. 1), which may include an image sensor or camera 114 and a control 116 positioned on or at vehicle 12. Lane departure warning system 110 is generally shown at the front of the vehicle 12 with camera 114 positioned and oriented to capture an image of the region generally forwardly of the vehicle) [Tian: col. 21, line 32-39; Figs. 1-4].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi and Tian with Pawlicki to program the system to implement of Pawlicki’s method.
Therefore, the combination of Annovi and Tian with Pawlicki will enable the system to capture an image of a scene occurring forwardly of the vehicle and control is operable to process image data of the captured images or frames to detect and monitor or track lane markers or road edges or the like or oncoming or approaching vehicles or objects, and to provide a warning or alert signal to a driver of the vehicle [Tian: col. 21, line 45-51; Figs. 1-4].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Annovi (US Patent 10,480,939 B2), (“Annovi”), in view of Tian et al. (US Patent Application Publication 2024/0031675 A1), (“Tian”), in view of Suwagara et al. (US Patent Application Publication 2024/0287748 A1), (“Suwagara”).
Regarding claim 8, Annovi meets the claim limitations, as follows:
wherein features associated with physical characteristics related to road distress comprises crack width or crack length ((The 3D image can be used along with the contrast normalized intensity images containing image intensity data to improve the distress detection, especially, cracking 604. Cracks are identified both in the gradient and intensity images. Both the shape and intensity is then used to classify the features as cracks, sealed cracks or other road features) [Annovi: col. 9, line 30-35]; (Through the system, cracks could be further classified into regular, sealed, longitudinal, transverse and alligator cracks) [Annovi: col. 8, line 41-42] – Note: longitudinal cracks are classified by the crack’s length).
However, in the same field of endeavor Suwagara further discloses the deficient claim limitations as follows:
comprises crack width or crack length ((calculates a crack width and a crack rate of the detected crack based on the image, and a display processing means that displays the calculated crack width and the calculated crack rate over the road surface on a map in a superimposed manner) [Suwagara: para. 0007; Figs. 5-6]; (In FIG. 17, the display processing unit 210 displays the crack width with shading of a classification related to the length of the width) [Suwagara: para. 0099; Figs. 5-6, 17]; (The display processing unit 4 superimposes the calculated crack width and crack rate on the road surface on a map) [Suwagara: Abstract; Figs. 5-6]).
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Annovi with Suwagara to program the system to implement of Suwagara’s method.
Therefore, the combination of Annovi with Suwagara will enable the system to provide more accurately predict the repair time [Suwagara: para. 0072].
Reference Notice
Additional prior arts, included in the Notice of Reference Cited, made of record and not relied upon is considered pertinent to applicant's disclosure.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip Dang whose telephone number is (408) 918-7529. The examiner can normally be reached on Monday-Thursday between 8:30 am - 5:00 pm (PST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached on 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Philip P. Dang/ Primary Examiner, Art Unit 2488