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 .
Status of Claims
The following is a non-final, first office action in response to the communication filed 06/20/2023. Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/20/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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 1-20 are rejected under 35 U.S.C. §103 as being unpatentable over US 9,453,941 B2 (hereinafter “Olshansky”) in view of US 11,021,159 B2 (hereinafter “Herman”).
Regarding claim 1, Olshansky discloses:
A system, comprising: (Olshansky, [0006], “ A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system”); a processor that executes computer-executable components stored in a non-transitory computer-readable memory, (Herman, Abstract, “system includes a computer comprising a processor and a memory”); wherein the computer-executable components comprise: (Herman, Abstract); a sensor component, comprising a LiDAR sensor, that obtains a reflectance value and a distance associated with a portion of a road; (Olshansky, [0012]–[0014], “distance to the object is measured based on analyzing the reflected light… reflective properties of the surface condition of the road”); a mapping component that generates a heat map of the reflectance value and the distance; (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: dense point cloud corresponds to spatial mapping of reflectance and distance); an indexing component that determines a number of returns received from the sensor component indicating a surface condition of the road; (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); and a road condition component that determines the surface condition of the road from the heat map and the number of returns received. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”).
However, Olshansky does not explicitly disclose a mapping component configured specifically as a heat map of reflectance and distance, and determining the surface condition based on both the heat map and the number of returns in combination as recited. Herman discloses (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Olshansky to include a mapping component that generates a heat map of reflectance and distance and to determine the surface condition based on both reflectivity and return signal characteristics as taught by Herman, in order to improve accuracy and robustness of road surface condition detection by combining spatial reflectivity mapping with return-based signal analysis, thereby enabling more reliable differentiation between dry, wet, icy, and snowy road conditions.
Regarding claim 2, Olshansky discloses:
The system of claim 1, wherein the sensor component emits one or more pulsed light waves that reflect off a surface of the road to be received by the sensor component (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”); to determine the distance between the surface and a vehicle. (Olshansky, [0012], “distance to the object is measured based on analyzing the reflected light”).
However, Olshansky does not explicitly disclose: the sensor component determining the distance specifically between the surface and a vehicle as recited in the claimed system context, where the system explicitly defines the relationship between the LiDAR sensor, the vehicle, and the road surface within the claimed system architecture. Herman discloses a processor-based system for determining road surface conditions using sensed data (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to interpret and implement the distance measurement of Olshansky as a distance between the vehicle-mounted LiDAR sensor and the road surface, as recited, and to incorporate such measurement into the system architecture of Herman, in order to provide a consistent and defined spatial relationship between the sensing system and the road surface, thereby enabling accurate environmental perception for vehicle-based applications.
Regarding claim 3, Olshansky discloses:
The system of claim 1, wherein the sensor component emits one or more pulsed light waves that reflect off a surface of the road to be received by the sensor component (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”); to determine the reflectance value. (Olshansky, [0015], “determinations of the reflective properties of the surface condition of the road”).
However, Olshansky does not explicitly disclose: determining the reflectance value as a distinct reflectance value parameter explicitly calculated and output by the sensor component as recited, as opposed to describing reflective properties in a general classification or detection context. Herman discloses determine a reflectivity of an area of a road surface (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Olshansky to determine and output a reflectance value as an explicit parameter derived from reflected light signals as taught by Herman, in order to enable quantitative analysis of road surface properties and improve classification of surface conditions such as wet, icy, or dry surfaces.
Regarding claim 4, Olshansky discloses:
The system of claim 1, wherein the road condition component compares the reflectance value
(Olshansky, [0015], “reflective properties of the surface condition of the road”); with one or more pre-defined distributions of reflectance values representing one or more surface conditions, (Herman, [0065], “the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R”); and compares the heat map with one or more pre-defined heat maps representing one or more surface conditions (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: spatial representation corresponds to mapped data used for comparison); to determine the surface condition. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”).
However, Olshansky does not explicitly disclose comparing the reflectance value to pre-defined distributions of reflectance values representing surface conditions, and comparing a heat map to pre-defined heat maps representing surface conditions, where both comparisons are performed together to determine the surface condition as recited. Herman discloses (the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]), which corresponds to comparison against predefined reflectivity-based criteria representing different surface conditions. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Olshansky to compare reflectance values to predefined distributions representing surface conditions as taught by Herman and to apply such comparisons in conjunction with spatial mapping data (e.g., heat maps derived from lidar point clouds) in order to improve the accuracy and reliability of road condition classification by combining threshold-based reflectivity analysis with spatially resolved surface representations.
Regarding claim 5, Olshansky discloses:
The system of claim 1, wherein the road condition component determines that the portion of the road is dry, moist, slushy, or wet (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”; Examiner’s note: classification of road surface conditions including different states); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from one or more pulsed light waves transmitted by the sensor component (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”).
However, Olshansky does not explicitly disclose determining that the portion of the road is dry, moist, slushy, or wet in response to receiving one or more returns with indexes of two, three, or four as recited. Herman discloses: classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to correlate characteristics of return signals obtained from pulsed light waves, such as signal intensity, scattering behavior, or number of detected returns as taught by Olshansky, with different road surface conditions, and to implement threshold-based classification of such characteristics as taught by Herman, in order to improve differentiation between surface conditions such as dry, moist, slushy, and wet surfaces.
Regarding claim 6, Olshansky discloses:
The system of claim 5, wherein the road condition component determines that the portion of the road is snowy or icy (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from the one or more pulsed light waves. (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”).
However, Olshansky does not explicitly disclose: determining that the portion of the road is snowy or icy in response to receiving one or more returns with an index of five as recited. Herman discloses classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend the correlation of return signal characteristics (e.g., number of returns, signal attenuation, and scattering behavior) to additional surface conditions such as snowy or icy surfaces as taught by Olshansky, and to implement threshold-based categorization of such characteristics as taught by Herman, including assigning higher or distinct threshold values (e.g., corresponding to an index value) to represent different surface conditions, in order to improve classification granularity and distinguish more complex surface states such as snow and ice.
Regarding claim 7, Olshansky discloses:
The system of claim 6, wherein the road condition component determines that the portion of the road includes a dry surface (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”; Examiner’s note: classification includes different surface states); in response to reflective values (Olshansky, [0015], “reflective properties of the surface condition of the road”); for distances greater than 10 meters between the portion of the road and a vehicle. (Olshansky, [0012], “distance to the object is measured based on analyzing the reflected light”). However, Olshansky does not explicitly disclose: determining that the surface is dry in response to reflective values up to 0.125, and applying such reflectance threshold in combination with a distance constraint greater than 10 meters as recited. Herman discloses: (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]), which teaches the use of reflectivity thresholds to distinguish between different road surface conditions. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply threshold values to reflectivity measurements, as taught by Herman, to classify specific surface conditions such as dry surfaces within the system of Olshansky, and to apply such thresholds in conjunction with measured distances obtained from the LiDAR system of Olshansky, in order to improve the accuracy and reliability of surface condition classification across varying sensing ranges, since reflectivity measurements can vary with distance and environmental conditions.
Regarding claim 8, Olshansky discloses:
A computer implemented method for estimating surface conditions on a road, the computer implemented method comprising: (Olshansky, [0006], An embodiment contemplates a method of determining a surface condition of a road of travel); obtaining, by a device operatively coupled to a processor, a reflectance value and a distance associated with a portion of a road; (Olshansky, [0012]–[0014], “distance to the object is measured based on analyzing the reflected light… reflective properties of the surface condition of the road”); generating, by the device, a heat map of the reflectance value and the distance; (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: dense point cloud corresponds to spatial mapping of reflectance and distance); and determining, by the device, a number of returns received indicating a surface condition of the road; (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); and determining, by the device, the surface condition of the road from the heat map and the number of returns received. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”).
However, Olshansky does not explicitly disclose: generating a heat map of reflectance and distance as recited, and determining the surface condition based on both the heat map and the number of returns in combination. Herman discloses: (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olshansky to include generating a mapped representation of reflectance values (e.g., a heat map) and to determine the surface condition based on both reflectivity and return signal characteristics as taught by Herman, in order to improve the accuracy and robustness of road surface condition estimation by combining spatial reflectivity mapping with return-based signal analysis.
Regarding claim 9, Olshansky discloses:
The computer implemented method of claim 8, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); emitting, by the device, one or more pulsed light waves that reflect off the surface of the road (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system”); to determine the distance between the surface and a vehicle. (Olshansky, [0012], “distance to the object is measured based on analyzing the reflected light”). However, Olshansky does not explicitly disclose determining the distance specifically between the surface and a vehicle within the claimed method context, where the method explicitly defines the relationship between the sensing device, the vehicle, and the road surface. Herman discloses a processor-based system for determining road surface conditions using sensed data (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to interpret and implement the distance measurement of Olshansky as a distance between a vehicle-mounted sensing device and the road surface, as recited, and to incorporate such measurement into a computer-implemented method as in Herman, in order to provide a consistent and well-defined spatial reference for road condition estimation in vehicle-based systems.
Regarding claim 10, Olshansky discloses:
The computer implemented method of claim 8, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); emitting, by the device, one or more pulsed light waves that reflect off the surface of the road (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system”); to determine the reflectance value of the surface. (Olshansky, [0015], “determinations of the reflective properties of the surface condition of the road”). However, Olshansky does not explicitly disclose determining the reflectance value as a distinct reflectance value parameter explicitly calculated and output within the claimed method, as opposed to describing reflective properties in a general detection or classification context. Herman discloses determine a reflectivity of an area of a road surface (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olshansky to determine and output a reflectance value as an explicit parameter derived from reflected light signals as taught by Herman, in order to enable quantitative evaluation of road surface properties and improve classification of surface conditions.
Regarding claim 11, Olshansky discloses:
The computer implemented method of claim 8, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); comparing, by the device, the reflectance value (Olshansky, [0015], “reflective properties of the surface condition of the road”); with one or more pre-defined distributions of reflectance values representing one or more surface conditions; (Herman, [0065], “the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R”); and comparing, by the device, the generated heat map with one or more pre-defined heat maps representing one or more surface conditions (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: spatial mapping corresponds to mapped data used for comparison); to determine the surface condition. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”).
However, Olshansky does not explicitly disclose: comparing the reflectance value to pre-defined distributions of reflectance values representing surface conditions, and comparing a generated heat map to pre-defined heat maps representing surface conditions, where both comparisons are performed together to determine the surface condition as recited. Herman discloses: (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]), which teaches comparison against predefined reflectivity-based criteria representing different surface conditions. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Olshansky to compare reflectance values to predefined distributions representing surface conditions as taught by Herman and to apply such comparisons in conjunction with spatial mapping data (e.g., heat maps derived from lidar point clouds), in order to improve the accuracy and reliability of road condition classification by combining threshold-based reflectivity analysis with spatially resolved surface representations.
Regarding claim 12, Olshansky discloses:
The computer implemented method of claim 8, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); determining, by the device, that the portion of the road is dry, moist, slushy, or wet (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”; Examiner’s note: classification of different road surface conditions); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from one or more pulsed light waves emitted towards the portion of the road. (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”).
However, Olshansky does not explicitly disclose determining that the portion of the road is dry, moist, slushy, or wet in response to receiving one or more returns with indexes of two, three, or four as recited. Herman discloses classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to correlate characteristics of return signals obtained from pulsed light waves, such as signal intensity, scattering behavior, or number of detected returns as taught by Olshansky, with different road surface conditions, and to implement threshold-based classification of such characteristics as taught by Herman, including discretizing such characteristics into ranges (e.g., corresponding to index values) representing different surface conditions, in order to improve differentiation between road surface states such as dry, moist, slushy, and wet.
Regarding claim 13, Olshansky discloses:
The computer implemented method of claim 12, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); determining, by the device, that the portion of the road is snowy or icy (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from the one or more pulsed light waves. (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”).
However, Olshansky does not explicitly disclose: determining that the portion of the road is snowy or icy in response to receiving one or more returns with an index of five as recited. Herman discloses: classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend the correlation of return signal characteristics (e.g., number of returns, signal attenuation, and scattering behavior) to additional surface conditions such as snowy or icy surfaces as taught by Olshansky, and to implement threshold-based categorization of such characteristics as taught by Herman, including assigning higher or distinct threshold values (e.g., corresponding to an index value) to represent different surface conditions, in order to improve classification granularity and distinguish more complex surface states such as snow and ice.
Regarding claim 14, Olshansky discloses:
The computer implemented method of claim 12, further comprising: (Olshansky, Claim 1, comprising the steps of: transmitting a light beam directed at a surface in the road of travel utilizing a lidar system; analyzing a response at a photodetector of the lidar system after transmitting the light beam; determining whether a form of precipitation is present on the road of travel in response to analyzing the response at the photodetector; generating a precipitation indicating signal in response to the determination that the ground surface includes a form of precipitation on the road of travel, the precipitation indicating signal provided to an output device to enable stability control functionality); determining, by the device, that the portion of the road includes a dry surface (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”; Examiner’s note: classification of different surface states); in response to reflective values (Olshansky, [0015], “reflective properties of the surface condition of the road”); for distances greater than 10 meters between the portion of the road and a vehicle. (Olshansky, [0012], “distance to the object is measured based on analyzing the reflected light”). However, Olshansky does not explicitly disclose: determining that the surface is dry in response to reflective values up to 0.125, and applying such reflectance threshold in combination with a distance constraint greater than 10 meters as recited. Herman discloses (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]), which teaches the use of reflectivity thresholds to distinguish between different road surface conditions. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the Herman claimed invention to apply threshold values to reflectivity measurements, as taught by Herman, to classify specific surface conditions such as dry surfaces within the method of Olshansky, and to apply such thresholds in conjunction with measured distances obtained from the LiDAR system of Olshansky, in order to improve the accuracy and reliability of surface condition classification across varying sensing ranges, since reflectivity measurements can vary with distance and environmental conditions.
Regarding claim 15, Herman discloses:
A computer program product for estimating surface conditions on a road, the computer program product comprising a non-transitory computer readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: (Herman, Abstract, “system includes a computer comprising a processor and a memory”; Examiner’s note: processor-executable instructions stored in memory); Olshansky discloses: obtain a reflectance value and a distance associated with a portion of a road via a LiDAR sensor; (Olshansky, [0012]–[0014], “distance to the object is measured based on analyzing the reflected light… reflective properties of the surface condition of the road”); generate a heat map of the reflectance value and the distance; (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: spatial mapping corresponds to a heat map representation); determine a number of returns received indicating a surface condition of the road; (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); and determine the surface condition of the road from the heat map and the number of returns received. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”).
However, Olshansky does not explicitly disclose: generating a heat map of reflectance and distance as recited, and determining the surface condition based on both the heat map and the number of returns in combination, and implementation of the method steps as program instructions stored in a non-transitory computer-readable memory executable by a processor. Herman discloses: processor-executable instructions stored in memory for performing road surface condition determination (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the method of Olshansky as processor-executable instructions stored in a non-transitory computer-readable memory as taught by Herman, and to include reflectivity-based classification within such implementation, in order to provide a computer-implemented solution capable of executing road surface condition estimation in real-time systems.
Regarding claim 16, Herman discloses:
The computer program product of claim 15, wherein the program instructions are further executable to cause the processor to: (Herman, Abstract, processor executing instructions stored in memory); Olshansky discloses: emit one or more pulsed light waves that reflect off the surface of the road (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system”); to determine the distance between the surface and a vehicle. (Olshansky, [0012], “distance to the object is measured based on analyzing the reflected light”).
However, Olshansky does not explicitly disclose: program instructions that cause a processor to emit one or more pulsed light waves and determine the distance between the surface and a vehicle within a computer program product context as recited, and determining the distance specifically between the surface and a vehicle within the claimed program instruction framework. Herman discloses: processor-executable instructions stored in memory for performing road surface condition determination (Herman, [0065]). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the emission of pulsed light waves and determination of distance as processor-executable instructions within a computer program product as taught by Herman, based on the sensing functionality of Olshansky, in order to enable software-controlled execution of LiDAR-based distance measurements within a computing system for vehicle-based road condition detection.
Regarding claim 17, Herman discloses:
The computer program product of claim 15, wherein the program instructions are further executable to cause the processor to: (Herman, Abstract, processor executing instructions stored in memory); Olshansky discloses: emit one or more pulsed light waves that reflect off the surface of the road (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system”); to determine the reflectance value of the surface. (Olshansky, [0015], “determinations of the reflective properties of the surface condition of the road”).
However, Olshansky does not explicitly disclose: program instructions that cause a processor to determine and output a reflectance value as a distinct reflectance value parameter within a computer program product context as recited, and determining the reflectance value as an explicit calculated parameter rather than general reflective properties. Herman discloses: determine a reflectivity of an area of a road surface (Herman, [0065]), and processor-executable instructions for performing such determinations (Herman, Abstract). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the determination of a reflectance value as a processor-executable instruction within a computer program product as taught by Herman, based on the sensing and reflective property determination of Olshansky, in order to enable quantitative evaluation of road surface properties within a software-controlled system for improved classification of surface conditions.
Regarding claim 18, Herman discloses:
The computer program product of claim 15, wherein the program instructions are further executable to cause the processor to: (Herman, Abstract, processor executing instructions stored in memory); Olshansky discloses: compare the reflectance value (Olshansky, [0015], “reflective properties of the surface condition of the road”); with one or more pre-defined distributions of reflectance values representing one or more surface conditions; (Herman, [0065], “the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R”); and compare the generated heat map with one or more pre-defined heat maps representing one or more surface conditions (Olshansky, [0037], “lidar scans can be combined into dense point clouds (if ego-motions sensors are available or from which ego-motion can be estimated) and the same analysis can be applied to point cloud patches, which will provide full information about the road surface surrounding the vehicle”; Examiner’s note: spatial mapping corresponds to mapped data used for comparison); to determine the surface condition. (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”). However, Olshansky does not explicitly disclose: program instructions that cause a processor to compare reflectance values to pre-defined distributions representing surface conditions, and program instructions that cause a processor to compare a generated heat map to pre-defined heat maps representing surface conditions, where both comparisons are performed together within a computer program product context as recited. Herman discloses: (“the change of reflectivity R of different materials, e.g., concrete and brass, based on the wavelength of light that hits a surface 150 formed of a respective material and based on being wet or dry. The computer 110 may be programmed to identify a wet surface 150 based on a known material of the surface 150, e.g., determined based on map data, and determining the material based on data received from the lidar sensor 130. The computer 110 may store data including a reflectivity R” Herman, [0065]), which teaches comparison against predefined reflectivity-based criteria, and processor-executable instructions for performing such comparisons (Herman, Abstract). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement, as processor-executable instructions within a computer program product as taught by Herman, the comparison of reflectance values to predefined distributions representing surface conditions and to apply such comparisons in conjunction with spatial mapping data (e.g., heat maps derived from lidar point clouds) as in Olshansky, in order to improve the accuracy and reliability of road condition classification by combining threshold-based reflectivity analysis with spatially resolved surface representations.
Regarding claim 19, Herman discloses:
The computer program product of claim 18, wherein the program instructions are further executable to cause the processor to: (Herman, Abstract, processor executing instructions stored in memory); Olshansky discloses: determine that the portion of the road is dry, moist, slushy, wet, snowy, or icy (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”; Examiner’s note: classification includes multiple road surface conditions); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from one or more pulsed light waves emitted towards the portion of the road. (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”).
However, Olshansky does not explicitly disclose: determining that the portion of the road is dry, moist, slushy, wet, snowy, or icy in response to receiving one or more returns with indexes of two, three, or four as recited. Herman discloses: classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]), and processor-executable instructions for performing such classifications (Herman, Abstract). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to correlate characteristics of return signals obtained from pulsed light waves, such as signal intensity, scattering behavior, or number of detected returns as taught by Olshansky, with multiple road surface conditions, and to implement threshold-based classification of such characteristics as taught by Herman, including discretizing such characteristics into ranges (e.g., corresponding to index values) representing different surface conditions, in order to provide flexible classification of road surfaces across varying environmental conditions.
Regarding claim 20, Herman discloses:
The computer program product of claim 19, wherein the program instructions are further executable to cause the processor to: (Herman, Abstract, processor executing instructions stored in memory); Olshansky discloses determine that the portion of the road is snowy or icy (Olshansky, [0026], “Classification labels include, but are not limited to, wet regions, icy regions, dry regions, and snow regions”); in response to receiving one or more returns (Olshansky, [0004], “Various techniques may be used to determine ice/snow/water on the surface of the surface of the road such as no response signal received, a response signal receive have an increased scattered beam as determined by analyzing the received signal using a sliding window, and detection of false objects in the return signal”); from the one or more pulsed light waves. (Olshansky, [0006], “A light beam directed at a surface in the road of travel is transmitted utilizing a lidar system. A response at a photodetector of the lidar system is analyzed after transmitting the light beam”). However, Olshansky does not explicitly disclose: determining that the portion of the road is snowy or icy in response to receiving one or more returns with an index of five as recited, and implementing such determination as program instructions executable by a processor within a computer program product context. Herman discloses: classification of road surface conditions based on reflectivity values and thresholds (Herman, [0065]), and processor-executable instructions for performing such classifications (Herman, Abstract). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement, as processor-executable instructions within a computer program product as taught by Herman, the correlation of return signal characteristics (e.g., number of returns, signal attenuation, and scattering behavior) to specific surface conditions such as snowy or icy surfaces as taught by Olshansky, and to assign higher or distinct threshold values (e.g., corresponding to an index value such as five) to represent such surface conditions, in order to improve classification granularity and enable reliable detection of complex road surface states.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dominick Cabrera whose telephone number is (571) 317-1401. The examiner can normally be reached Monday - Thursday, 8 AM - 4 PM.
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, Vladimir Magloire can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DOMINICK JACOB CABRERA/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648