DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Statement
Paragraph numbers for the Pöchmüller (EP 1303768 B1) reference are taken from the US equivalent (US 2004/0046866 A1).
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.
Claims 11-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Satat (US 2019/0361099 A1) in view of Pöchmüller (EP 1303768 B1).
Regarding Claim 11, Satat discloses a method for determining a maximum range of a LiDAR sensor ([0156]), the method comprising the following steps:
providing a LiDAR point cloud using the LiDAR sensor, which images an environment of the LiDAR sensor at a specific point in time ([0045]: The measurements are time-resolved.) within a predefined field of view of the LiDAR sensor in a three-dimensional manner ([0008]; [0041]; [0154]; The emitter is a LiDAR which emits pulsed light and scans a scene; [0156]: “The raster scan may be performed to create a full 3D map of the environment.”);
identifying at least two different point sets within the LiDAR point cloud, each of the at least two different point sets imaging an area in the environment that was identified as belonging to a predefined environment object ([0045]: “computationally separates the background reflection (from the fog itself) and the reflection from the target;” The predefined environment objects are the fog and the target. Each corresponds to a different set of points in the scene.);
calculating the area imaged by each of the point sets, and dividing a number of LiDAR points imaging the areas by imaged corresponding areas to obtain at least two different point densities (Abstract, [0120], Satat describes using Gamma distributions for arrival times of photons reflecting from fog, Gaussian distributions for others. These distributions count the number of LiDAR points.; e.g., [0120], describes calculating distributions from an area of pixels, which therefore correspond to an area in the scene; These yield a point or photon density which is equivalent to luminous intensity.)
calculating a quotient from the at least two point densities ([0265]: Discloses using an intensity of light from a foggy scene to calculate a ratio between signal photons and background photons in a scene. This is equivalent to a contrast.);
Satat suggests but does not explicitly teach and Pöchmüller does teach using the quotient to ascertain the maximum range of the LiDAR sensor ([0034]-[0035]: The visual range delta is calculated using a quotient between contrasts, see equation in text.), for which a previously stored regression curve is used ([0042] teaches making multiple contrast measurements, fitting them to a curve, then using that to predict delta.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Pöchmüller into the method of Satat. Pöchmüller notes in [0042] that the method of calculating a regression curve to calculate delta can result in a very high accuracy value for the visual range. The accuracy level of this value is critical for safe driving in foggy or otherwise obscured conditions, and a high level of accuracy is therefore highly advantageous.
Regarding Claim 12, which depends from rejected Claim 11, Satat further teaches wherein both an existence of the predefined environment object and its position in the field of view are determined by a classification algorithm applied to the LiDAR point cloud ([0043]: “The probabilistic algorithm may leverage the fact that times of arrival of photons reflected from fog itself have a distribution (Gamma) that is different than the distribution (Gaussian) of times of arrival of photons reflected from objects occluded by fog.” The algorithm can distinguish between fog and objects in the scene; [0045]: “ Process the signal to calculate: (a) reflectance of the target; and (b) a depth map of the target (Step 204).” Performed after correcting for the background, yields the position.)
Regarding Claim 13, which depends from rejected Claim 12, Satat does not teach and Pöchmüller does teach wherein the classification algorithm is an algorithm by which a surface of the ground is detectable as a predefined environment object within the environment of the field of view of the LiDAR sensor ([0031]: “The corner of a lane marking having a pronounced bright-dark contrast is selected here as an object 1 for a contrast measurement. The object is detected and measured in the image of a camera at time t.sub.1.” Lane marking are a type of surface on the ground.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention classify and use a region of the ground such as a lane marking. Such markings offer high contrast, yielding measurements over a longer range. This is useful for vehicular applications as it allows for more accurate retrievals of the visibility via a greater number of measurements of the observed contrast over a greater distance.
Regarding Claim 14, which depends from rejected Claim 13, Satat does not teach and Pöchmüller does teach wherein the at least two different point sets image areas that represent at least part of the surface of the ground and/or the base on or above which the LiDAR sensor was situated at that time ([0031]: “The corner of a lane marking having a pronounced bright-dark contrast is selected here as an object 1 for a contrast measurement.” Contrast measurements in this context necessarily require measurements from two different areas in the field of view. In this case the regions are lane marking (bright) and road surface (dark).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use two different point sets in the measurements of Satat in view of Pöchmüller. Measurements of this type are required in order to calculate contrast as is done in Pöchmüller. In [0010], Pöchmüller notes the advantages of making multiple measurements of contrast, such as qualitatively converting the change in contrast values into a visual range, which necessitate the use of two different point areas for retrieving the contrast.
Regarding Claim 15, which depends from rejected Claim 11, Satat does not teach and Pöchmüller does teach wherein within a framework of use of the quotient, the quotient is compared to at least one regression curve previously stored for the areas imaged by the at least two different point sets or to areas that are comparable to the areas ([0042]: “An example of one possibility is to measure not only two contrast values for the selected object, but rather several contrast values, so that the curve shown in FIG. 2 may be fitted by the method of least squares,” The method of least squares necessarily requires comparison of a stored regression curve to a quotient, as it iteratively minimizes the variance between the quotient and a curve stored in memory.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to compare a quotient to a regression curve according to the teaching of Pöchmüller. The method of least squares is a robust and well-known mechanism of refining regression curves, and a skilled worker in the art would be able to implement one with predictable results.
Regarding Claim 16, which depends from rejected Claim 15, Satat does not teach and Pöchmüller does teach wherein the at least one previously stored regression curve ([0042]) relates the calculated quotient to the maximum range of the LiDAR sensor ([0034]-[0035]: The visual range delta is calculated using a quotient between contrasts, see equation in text.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Pöchmüller into the method of Satat. Pöchmüller notes in [0042] that the method of calculating a regression curve to calculate delta can result in a very high accuracy value for the visual range. The accuracy level of this value is critical for safe driving in foggy or otherwise obscured conditions, and a high level of accuracy is therefore highly advantageous.
Regarding Claim 17, which depends from rejected Claim 11, Satat further teaches wherein the at least two different point sets differ from one another in at least one LiDAR point ([0048]: “A pulsed light source may emit photons into a foggy scene. A time-resolved camera may be located adjacent to the light source. Each pixel in the camera may detect time of arrival of individual photons as they arrive at the sensor, after reflecting from the fog itself or from a target in the foggy scene.”; [0049]: “In this model, a measured photon may be classified as either (a) a background photon, (b) a signal photon, or (c) a dark count photon.”; Thus the classification sorts LiDAR points into at least those due to fog and those due to a target object, and therefore differ in all points.).
Regarding Claim 18, which depends from rejected Claim 11, wherein the at least two different point sets differ from one another in all LiDAR points ([0048]: “A pulsed light source may emit photons into a foggy scene. A time-resolved camera may be located adjacent to the light source. Each pixel in the camera may detect time of arrival of individual photons as they arrive at the sensor, after reflecting from the fog itself or from a target in the foggy scene.”; [0049]: “In this model, a measured photon may be classified as either (a) a background photon, (b) a signal photon, or (c) a dark count photon.”; Thus the classification sorts LiDAR points into at least those due to fog and those due to a target object, and therefore differ in all points.).
Regarding Claim 19, which depends from rejected Claim 11, Satat further discloses wherein the predefined field of view is one of at least two predefined subfields of view of the LiDAR sensor, which jointly form a total field of view of the LiDAR sensor ([0182]: “a computational step may be performed separately for each local region of pixels, on a local region-by-local region basis.” These pixel regions correspond to areas in the scene.)
Regarding Claim 21, Satat discloses a device, comprising:
a LiDAR sensor ([0156]);
wherein the device to determine a maximum range of the LiDAR sensor ([0094]: “estimated optical thickness of fog may be employed to determine the maximum depth at which imaging through fog works well.”), the device configured to:
provide a LiDAR point cloud using the LiDAR sensor, which images an environment of the LiDAR sensor at a specific point in time ([0045]: The measurements are time-resolved.) within a predefined field of view of the LiDAR sensor in a three-dimensional manner ([0008]; [0041]; [0154]; The emitter is a LiDAR which emits pulsed light and scans a scene; [0156]: “The raster scan may be performed to create a full 3D map of the environment.”),
identify at least two different point sets within the LiDAR point cloud, each of the at least two different point sets imaging an area in the environment that was identified as belonging to a predefined environment object ([0045]: “computationally separates the background reflection (from the fog itself) and the reflection from the target;” The predefined environment objects are the fog and the target. Each corresponds to a different set of points in the scene.),
calculate the area imaged by each of the point sets, and dividing a number of LiDAR points imaging the areas by imaged corresponding areas to obtain at least two different point densities (Abstract, [0120], Satat describes using Gamma distributions for arrival times of photons reflecting from fog, Gaussian distributions for others. These distributions count the number of LiDAR points.; e.g., [0120], describes calculating distributions from an area of pixels, which therefore correspond to an area in the scene; These yield a point or photon density which is equivalent to luminous intensity.),
Satat suggests but does not explicitly teach and Pöchmüller does teach to calculate a quotient from the at least two point densities, and use the quotient to ascertain the maximum range of the LiDAR sensor ([0034]-[0035]: The visual range delta is calculated using a quotient between contrasts, see equation in text.), for which a previously stored regression curve is used ([0042] teaches making multiple contrast measurements, fitting them to a curve, then using that to predict delta.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Pöchmüller into the method of Satat. Pöchmüller notes in [0042] that the method of calculating a regression curve to calculate delta can result in a very high accuracy value for the visual range. The accuracy level of this value is critical for safe driving in foggy or otherwise obscured conditions, and a high level of accuracy is therefore highly advantageous.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Satat in view of Pöchmüller as applied to Claim 19 above, and further in view of Evans (US 4,216,498 A).
Regarding Claim 20, which depends from rejected Claim 19, Satat does not teach and Pöchmüller suggests ([0043] teaches making visual range measurements of several objects, corresponding to several regions of the scene.) but does not explicitly teach and Evans does teach wherein a predefined rule is used to ascertain a maximum range for the total field of view based on maximum ranges ascertained for the subfields of view (Column 1, Lines 13-18: “If such visibility measurements are made around the entire horizon, they may be resolved, or combined, into a single value of prevailing visibility, which is the greatest horizontal visibility equalled or surpassed throughout half of the horizon circle.” Thus visibility (e.g., maximum range) measurements are made around the horizon and combined by a predefined rule into a prevailing visibility.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Evans to combine several visibility measurements by a predefined rule into the method of Satat in view of Pöchmüller. Evans notes that “By use of targets at different ranges (at small azimuthal differences) improved accuracy in the calculation of visibility is possible.” Improved visibility accuracy is desirable to and beneficial for end users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kwon (US 7,016,045 B2) discloses a visibility measurement system that computes relative visibility by comparing images of an environment against a benchmark image.
Fischer (US 2020/0363528 A1) discloses ascertaining the maximum range of a LiDAR sensor.
Boucourt (US 2014/0061477 A1) discloses an active devices for viewing a scene through a diffusing medium.
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/B.W.C./Examiner, Art Unit 3645
/ISAM A ALSOMIRI/Supervisory Patent Examiner, Art Unit 3645
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