DETAILED ACTIONS
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 .
Drawings
The 4-page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-4, 7-11, 14-18 and 20 are pending.
Response to Amendment
The amendment filed 08/21/2025 has been entered. Claims 1-4, 7-11, 14-18 and 20 remain pending in the application.
Response to Arguments
Applicant's arguments filed 08/21/2025 have been fully considered but they are not persuasive.
On pages 7-8 of the Remarks, Applicants contend that Mordechai is disqualified as a reference under AIA 35 U.S.C. 102(b)(2)(C). Applicants argue that according Section 102(b)(2)(C), as noted in the Statement of Common Ownership, at the time the instant application was filed, the instant application and Mordechai were both commonly owned by GM Global Technology Operations LLC. The Examiner respectfully disagrees with this characterization of Mordechai and submits that the reference is an eligible reference.
The reference Mordechai (US 2021/0174528 A1) was published on June 10, 2021, while the effective filing date of the instant application is January 30, 2023. Because Mordechai was published and available to the public before the effective filing date of the claimed invention, it is eligible to be used as basis for 35 U.S.C. 102(a)(1) rejection. According to MPEP, 102(a)(1) rejection has two exceptions which are, 102(b)(1)(A), “a disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) if the disclosure was made by the inventor or joint inventor or by another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor”, and 102(b)(1)(B), “a disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) the subject matter disclosed had, before such disclosure, been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor”. None of these two exceptions is applicable to Mordechai reference because it is published for more than a year prior to the effective filing date of the instant application. The Applicant argues that Mordechai is disqualified according Section 102(b)(2)(C), but Examiner disagrees because Mordechai is being rejected under 35 U.S.C. 102(a)(1) rather than 35 U.S.C. 102(a)(2). The MPEP states that “the AIA 35 U.S.C. 102(b)(2)(C) exception does not apply to a disclosure that qualifies as prior art under AIA 35 U.S.C. 102(a)(1) (disclosures made before the effective filing date of the claimed invention). Thus, if the publication date or issue date of a U.S. patent document is before the effective filing date of the claimed invention, it may be prior art under AIA 35 U.S.C. 102(a)(1), regardless of common ownership or the existence of an obligation to assign. Also, even if a U.S. patent or U.S. published application is not prior art under AIA 35 U.S.C. 102 or 103 as a result of AIA 35 U.S.C. 102(b)(2)(C), a double patenting rejection (either statutory under 35 U.S.C. 101 or non-statutory, sometimes called obviousness-type) may still be made on the basis of the claims of the U.S. patent or U.S. patent application publication”. Therefore, Mordechai reference is not disqualified according Section 102(b)(2)(C).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai et al., (US 2021/0174528 A1, published 06/10/2021), hereinafter referred to as Mordechai (previously cited in an IDS filed by the applicant).
Claim 1
Mordechai discloses a free space estimation and visualization system for a host vehicle (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface), comprising:
a camera configured to collect red-green-blue ("RGB")-polarimetric image data (Mordechai, [0047], “The digitized raw data output from the polarimetric camera 320 may be comprised of seven information channels where three channels are RGB color channels and four channels are polarization channels.”) of drive environs of the host vehicle (Mordechai, [0043], “The polarimetric camera 320 may be operative to capture an image, or a series of images of a field of view proximate to an ADAS equipped vehicle.”), including a potential driving path of the host vehicle (Mordechai, [0006], “a vehicle controller configured a perform an advanced driving assistance function and to control a vehicle movement in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”, adjusting the direction of the vehicle using the depth map means that the depth map creates a driving path for the vehicle),;
wherein the host vehicle is a motor vehicle having a vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”), the camera being connected to the vehicle body (Mordechai, [0031], “a polarimetric camera in a drive assistance system equipped vehicle”;
an electronic control unit ("ECU") (Mordechai, [0047], “The three dimensional point depth could may then be stored on the memory for use by the vehicle controller 330 as part of an ADAS function”) in communication with the camera (Mordechai, [0043], “The polarimetric camera 320 may be operative to capture an image, or a series of images of a field of view proximate to an ADAS equipped vehicle.”), the ECU being adapted to execute a path planning module for the host vehicle (Mordechai, [0006], “a vehicle controller configured a perform an advanced driving assistance function and to control a vehicle movement in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”, adjusting the direction of the vehicle using the depth map means that the depth map creates a driving path for the vehicle);
wherein the ECU (Mordechai, [0047], “The three dimensional point depth could may then be stored on the memory for use by the vehicle controller 330 as part of an ADAS function”) is configured to:
receive the RGB-polarimetric image data from the camera ([0014], “In accordance with another aspect of the present invention a method performed by a processor including receiving a color image of a field of view from a camera, receiving a polarimetric data of the field of view from a polarimetric camera, performing a neural network function to generate a depth map of the field of view in response to the color image and the polarimetric data”);
estimate an amount of free space in the potential driving path as estimated free space (Mordechai, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface), including processing the RGB-polarimetric image data via a run-time neural network (Mordechai, [0014], performing a neural network function to generate a depth map of the field of view in response to the color image and the polarimetric data”);
calculate a feature set using the RGB-polarimetric image data (Specification, [0007], “The feature set may have six set elements determined as a concatenation of the RGB data, angle of linear polarization ("AoLP") data, and degree of linear polarization ("DoLP") data from the camera. For instance, the six set elements could include sin(2 - AoLP), cos(2 - AoLP), 2 -DolP-1,2-R-1,2-G-1, and 2-B-1.”, Mordechai discloses this in [0039], “every 2×2 block makes up a four pixels calculation unit allowing measurement of three Stokes parameters (denoted S0, S1, S2) as well as measurement of Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). In this example, the polarization data is calculated independently for each color of the 4 colors of the CFA and the camera output is a scene image with 5 channels: RGB, AoLP, and DoLP. Thus the raw data received from each of the angled polarizers, I.sub.0, I.sub.45, I.sub.90, and I.sub.135 are used to determine the Stokes parameters S0, S1, S2 according to equation set 1.”) and communicate the feature set to the run-time neural network as an input data set (Mordechai, [0048], “In one exemplary embodiment, the encoder-decoder architecture may resemble a U-net convolutional network for image segmentation. The network input may include three RGB channels and up to four channels of polarization data”); and
execute a control action aboard the host vehicle (Mordechai, [0014], “performing a vehicle control operation to control a vehicle in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”), in response to the estimated free space (Mordechai, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface), including transmitting the estimated free space to the path planning module (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine each of the example embodiments of the prior art. All the claimed elements were known in the prior art and one skilled in the art could have combined the embodiments as claimed by known method with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Mordechai disclosed that the disclosed embodiments are merely examples and other embodiments can take various and alternative forms and the various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described (Mordechai, [0034]).
Claim 2
Mordechai discloses the system of claim 1 (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”), wherein the input data set is characterized by an absence of lidar data (Mordechai, [0048], “The network input may include three RGB channels and up to four channels of polarization data.”, Mordechai discloses training the neural network with lidar data, but the input is only the RGB-polarimetric data).
Claim 3
Mordechai discloses the system of claim 2 (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”), wherein the feature set has six set elements determined as a concatenation of RGB data, AoLP data, and DoLP data from the camera (Mordechai, [0047], “The digitized raw data output from the polarimetric camera 320 may be comprised of seven information channels where three channels are RGB color channels and four channels are polarization channels.”, it has six elements under BRI)
Claims 8-10 are rejected for similar reasons as those described in claims 1-3. The additional elements in Claims 8-10 (Mordechai) discloses includes: a method for use with a free space estimation and visualization system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface). It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine each of the example embodiments of the prior art. All the claimed elements were known in the prior art and one skilled in the art could have combined the embodiments as claimed by known method with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Mordechai disclosed that the disclosed embodiments are merely examples and other embodiments can take various and alternative forms and the various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described (Mordechai, [0034]).
Claims 15-17 are rejected for similar reasons as those described in claims 1-3. The additional elements in Claims 15-17 (Mordechai) discloses includes: A host vehicle Mordechai, [0006], “a vehicle controller configured a perform an advanced driving assistance function and to control a vehicle movement in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”, adjusting the direction of the vehicle using the depth map means that the depth map creates a driving path for the vehicle), comprising: a vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”); road wheels connected to the vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”); and a free space estimation and visualization ("FSEV") system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface) including: a camera connected to the vehicle body (Mordechai, [0031], “a polarimetric camera in a drive assistance system equipped vehicle”).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Blin et al., "A new multimodal RGB and polarimetric image dataset for road scenes analysis" (2020), hereinafter referred to as Blin.
Claim 4
Mordechai discloses the system of claim 3 (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”).
Mordechai does not explicitly disclose wherein the six set elements include sin(2*AoLP), cos(2*AoLP), 2*DolP - 1, 2*R - 1, 2*G - 1, and 2*B - 1.
However, Blin teaches wherein the six set elements (Blin, Section 1, A new dataset containing multimodal RGB and polarimetric images for road scene analysis in adverse weather conditions is then proposed.”, Section 3.2, “In order to compare polarimetric images to RGB ones, it is important to give images constituted of three channels, each one corresponding to a polarimetric information. Because pre-trained networks are used for the experiments, it is important to keep three channels for polarimetric data to achieve efficient training.”) include sin(2*AoLP) (Blin, Section 2, “The sensor measures an intensity according to each polarizer rotation angle αi, equation 2)
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, cos(2*AoLP) (Blin, Section 2, “The sensor measures an intensity according to each polarizer rotation angle αi, equation 2) , 2*DolP – 1 (Blin, Equation 10),
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, 2*R - 1, 2*G - 1, and 2*B – 1 (Fig. 4, RGB channels).
Mordechai and Blin are both considered to be analogous to the claimed invention because they are in the same field of road scene analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as taught by Mordechai to incorporate the teachings of Blin wherein the six set elements include sin(2*AoLP), cos(2*AoLP), 2*DolP - 1, 2*R - 1, 2*G - 1, and 2*B - 1. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to provide generic features for both good weather conditions and adverse weather ones (Blin, Abstract).
Claim 11 is rejected for similar reasons as those described in claims 4. The additional elements in Claim 11 (Mordechai and Blin) discloses includes: a method for use with a free space estimation and visualization system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface). The proposed combination as well as the motivation for combining the Mordechai and Blin references presented in the rejection of Claim 4, apply to Claim 11 and are incorporated herein by reference. Thus, the method recited in Claim 11 is met by Mordechai and Blin.
Claim 18 is rejected for similar reasons as those described in claims 4. The additional elements in Claim 18 (Mordechai and Blin) discloses includes: a host vehicle Mordechai, [0006], “a vehicle controller configured a perform an advanced driving assistance function and to control a vehicle movement in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”, adjusting the direction of the vehicle using the depth map means that the depth map creates a driving path for the vehicle), comprising: a vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”); road wheels connected to the vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”); and a free space estimation and visualization ("FSEV") system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface) including: a camera connected to the vehicle body (Mordechai, [0031], “a polarimetric camera in a drive assistance system equipped vehicle”). The proposed combination as well as the motivation for combining the Mordechai and Blin references presented in the rejection of Claim 4, apply to Claim 18 and are incorporated herein by reference. Thus, the host vehicle recited in Claim 18 is met by Mordechai and Blin.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Aycock et al., (US 2018/0005012 A1, published 01/04/2018), hereinafter referred to as Aycock (previously cited in an IDS by the applicant).
Claim 7
Mordechai discloses the system of claim 1 (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”).
Mordechai does not explicitly disclose wherein the ECU is in communication with a display screen and configured to display a graphical representation of the estimated free space on the display screen.
However, Aycock teaches wherein the ECU is in communication with a display screen (Aycock, [0083], “The display device 109 may consist of a tv, lcd screen, monitor or any electronic device that conveys image data resulting from the method 1000 or is attached to a personal digital assistant (PDA), computer tablet device, laptop, portable or non-portable computer, cellular or mobile phone, or the like. “, all computers have a control unit) and configured to display a graphical representation of the estimated free space on the display screen (Aycock, [0066], “The polarimeter 101 transmits raw image data to the signal processing unit 107, which processes the data as further discussed herein. The processed data is then displayed to the operator on display 109 or detection is annunciated on an annunciator 110, as further discussed herein.”, [0122], “FIG. 7a is a visible image of a dirt road 700 depicting exemplary obstacles 701 on a road 700. The obstacles 701 comprise wood planks in the image. FIG. 7b is a thermal image of the road 700 of FIG. 7a. In the image of FIG. 7b, the obstacles 701 are easier to discern than in the visible image of FIG. 7a. FIG. 7c is a contrast enhanced thermal image of the road 700 of FIG. 7a”, the obstacles are non-drivable area and the non-obstacle in the image is drivable which means it a free space for the vehicle).
Mordechai and Aycock are both considered to be analogous to the claimed invention because they are in the same field of road scene analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system as taught by Mordechai to incorporate the teachings of Blin wherein the ECU is in communication with a display screen and configured to display a graphical representation of the estimated free space on the display screen. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been because improved detection and recognition of obstacles will allow the operator to maneuver the vehicle (or vessel) to avoid obstacles (Aycock, [0116]).
Claim 14 is rejected for similar reasons as those described in claim 7. The additional elements in Claim 14 (Mordechai and Aycock) discloses includes: a method for use with a free space estimation and visualization system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface). The proposed combination as well as the motivation for combining the Mordechai and Aycock references presented in the rejection of Claim 7, apply to Claim 14 and are incorporated herein by reference. Thus, the method recited in Claim 14 is met by Mordechai and Aycock.
Claim 20 is rejected for similar reasons as those described in claims 7. The additional elements in Claim 20 (Mordechai and Aycock) discloses includes: a host vehicle Mordechai, [0006], “a vehicle controller configured a perform an advanced driving assistance function and to control a vehicle movement in response to the depth map”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”, adjusting the direction of the vehicle using the depth map means that the depth map creates a driving path for the vehicle), comprising: a vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”); road wheels connected to the vehicle body (Mordechai, [0005], “motor vehicles equipped with onboard control systems”, [0049], “The vehicle controller may be operative to adjust the direction of the vehicle controlling the vehicle steering via the steering controller 370 in response to a control signals generated by the processor 340.”); and a free space estimation and visualization ("FSEV") system (Mordechai, [0005], “autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems”, [0042], “The system is operative to use various sensors such as a polarimetric camera 320, and lidar 322 capable of detecting and mapping various external surfaces, objects and obstacles. Sensor fusion algorithms may be used to provide accurate detection and tracking of external objects as well as calculation of appropriate attributes such as relative velocities, accelerations, and the like. The camera 320 is operative to capture an image of a field of view (FOV) which may include static and dynamic objects proximate to the vehicle. Image processing techniques may be used to identify and locate objects within the FOV. These objects may then be bounded and identified as an undesirable driving area and stored in a memory or added to a reference map for the ADAS.”, the system detects road surface, objects , and obstacles, the objects that are identified as obstacles is considered to be undesirable driving area which means the ones that are not considered obstacles is a free space as a driving area, in the Specification, [0002], “Free space in a given image is typically estimated as a binary segmentation of the collected image, with image segmentation techniques being performed to separate the drivable surface area from the surface area of non-drivable surfaces”, Mordechai discloses segmenting the obstacles which is the non-drivable surface and the ones that are not determined as obstacles as drivable surface) including: a camera connected to the vehicle body (Mordechai, [0031], “a polarimetric camera in a drive assistance system equipped vehicle”). The proposed combination as well as the motivation for combining the Mordechai and Aycock references presented in the rejection of Claim 7, apply to Claim 20 and are incorporated herein by reference. Thus, the host vehicle recited in Claim 20 is met by Mordechai and Blin.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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/DENISE G ALFONSO/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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