DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 11 is objected to because of the following informalities: “the ascertaine normal road markings” would be better understood as “the ascertained normal road markings”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “The method according to claims 11,” should recite “The method according to claim[[s]] 11,”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation “possible elevated road markings”. However, the metes and bounds required by possible elevated road markings is unclear. It is unclear to whether the markings require to be elevated.
Claim 11 recites the limitation “elevated road markings” and “an arrangement of elevated road markings”. However, it is unclear to whether “elevated road markings” and “an arrangement of elevated road markings” includes the previously recite possibly elevated road markings.
Claim 11 recites a method for recognizing and classifying elevated road markings using a lidar system of a vehicle, wherein the lidar system including a transmitting unit, a receiving unit, at least one detector, the method comprising the following method steps, which are carried out individually or in combination: a) performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system; b) ascertaining normal road markings from at least one intensity-coded or background-light coded 3D point cloud and comparing the ascertained normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system; c) ascertaining context-sensitive information from an accompanying infrastructure or from a traffic-planning relevance of installation locations of elevated road markings, and/or d) analyzing the intensity-coded or background-light-coded 3D point cloud for regular patterns of an arrangement of elevated road markings. However, it is unclear to whether the claim requires steps a), b), c), and d); a) or b) or c) or d); or a) and b) and c) or a) and b) and d). It is unclear to whether the claim language “which are carried out individually or in combination” and the “and/or” refers to one step.
Claim 17 recites “wherein, according to method step d), a targeted look-ahead is carried out in such a way that a targeted extrapolation to further elevated road markings is undertaken from the intensity-coded or background-light coded 3D point cloud based on a few visible elevated road markings as a continuation of a recognized geometric regularity”. The term “few” in claim 17 is a relative term which renders the claim indefinite. It is unclear to which elevated road markings are being referred as claim 1 recites “possibly elevated road markings” and “elevated road markings”. The term “few” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 14 recites the limitation “wherein, according to method step d), a regular repetition of the possible elevated road markings to be recognized is undertaken in each case at the same distances from one another along the analytically describable curve or the geometric plane”. There is insufficient antecedent basis for “the same distances”.
Claim 15 recites the limitation “wherein, according to method step d), circles are drawn around the possible elevated road markings to be recognized, in such a way that intersections of adjacent circles form horizontal, vertical and mutually orthogonal straight lines”. However, it is unclear in light of the specification that circles are drawn around the possible elevated road markings because the circle 70 of Fig. 5 appears to be markings that form a circle shape (see at least specification page 13: in each case a circle 70 can be conceptually drawn around a possible elevated road marking 14 to be recognized, which circle has a sufficiently large radius so that, from the resulting intersection points of the circles 70, both horizontal and vertical lines are formed that are orthogonal to one another and run parallel to one another). Further, it is unclear to what adjacent circles refers and the specification and drawings do not appear to provide clarification.
Claim 20 recites “wherein the lidar system is used for reliable recognition and traversing classification of elevated road markings”. However, it is unclear to what traversing classification refers. The instant specification appears to recite “method in a lidar system for the reliable recognition and traversability classification of elevated road markings”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 11-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See MPEP 2106.
101 Analysis – Step 1
Claim 11 is directed to a method. Therefore, the claims are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the MPEP 2106, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim(s) 11 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claims for the remainder of the 101 rejection.
Claim 11 recites:
a) performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system;
b) ascertaining normal road markings from at least one intensity-coded or background-light-coded 3D point cloud and comparing the ascertaine normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system;
c) ascertaining context-sensitive information from an accompanying infrastructure or from a traffic-planning relevance of installation locations of elevated road markings, and/or d) analyzing the intensity-coded or background-light-coded 3D point cloud for regular patterns of an arrangement of elevated road markings.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the limitation(s) in the context of this claim encompasses a person determining possible curbs and other elevated road markings from a map and comparing road markings with possible curbs and other elevated road markings; determining a pattern from a 3D point cloud.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP 2106, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the MPEP 2106, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitation” while the bolded portions continue to represent the abstract idea):
Claim 11 recites:
a) performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system;
b) ascertaining normal road markings from at least one intensity-coded or background-light-coded 3D point cloud and comparing the ascertaine normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system;
c) ascertaining context-sensitive information from an accompanying infrastructure or from a traffic-planning relevance of installation locations of elevated road markings, and/or d) analyzing the intensity-coded or background-light-coded 3D point cloud for regular patterns of an arrangement of elevated road markings.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations, the examiner submits that these limitations are additional elements that do not integrate the judicial exception into a practical application and amount to no more than mere instructions to apply the exception using generic computer components and/or insignificant extra-solution activities that merely use a computer to perform the process and mere data gathering. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. The additional limitation steps are recited at a high level of generality (i.e. as a general means of gathering data, transmitting signals), and amounts to mere data gathering and storing and transmitting do not add a meaningful limitation to the process (MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754), which are forms of insignificant extra-solution activities. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than drafting effort designed to monopolize the exception (MPEP 2106.05). The additional limitations merely describe how to generally apply the otherwise mental judgements in a generic or general purpose vehicle environment. The additional limitations are recited at a high level of generality and merely automates the steps. Accordingly additional limitation(s) do/does not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP 2106, representative independent claim does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations do not provide any indication that the additional elements are anything other than a conventional computer within a vehicle. Also, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicate that mere collection or receipt of data over a network, receiving or transmitting data over a network, and storing and retrieving information in memory are a well-understood, routine, and conventional functions when claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Dependent claims 12-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that amount to no more than mere instructions to apply the exception using generic computer components that do not integrate the judicial exception into a practical application sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and merely automates the steps. The additional limitations are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity; the additional limitations are well-understood, routine, and conventional activity because the specification does not provide any indication that the additional elements are anything other than a conventional computer components. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Further, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Furthermore, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Moreover, mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the dependent claims are not patent eligible under the same rationale as provided for in the rejection of the independent claim.
Therefore, claim(s) 11-20 is/are ineligible under 35 USC 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 11-14, 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20210350147 (Yuan).
As per claim(s) 11, Yuan discloses a method for recognizing and classifying elevated road markings using a lidar system of a vehicle, wherein the lidar system including a transmitting unit, a receiving unit, at least one detector, the method comprising the following method steps, which are carried out individually or in combination:
performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system (see at least abstract: road being captured by one or more point clouds from one or more LIDAR sensors, [0069]: localization module 301 may obtain location and route information from a location server and a map and POI (MPOI) server, [0070]: Based on the sensor data provided by sensor system 115 and localization information obtained by localization module 301, a perception of the surrounding environment is determined by perception module 302, [0071]: objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc, [0139]: server 103 includes map partition engine 126. Furthermore, algorithms/models 124 can include a number of algorithms/models to extract road markings and curbs points. Using on a map provided by map server 104 (or generated by HD map generation engine 125), engine 126 partitions the map into one or more portion based the road markings and curb points extraction);
ascertaining normal road markings from at least one intensity-coded or background-light coded 3D point cloud and comparing the ascertained normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system (see at least [0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds, [0166], [0170]: identifying curbs boundaries for the road based on the curbs features includes, for each point in the point clouds, applying a machine learning algorithm to the extracted features to identify the point as an initial curb point; and for each initial curb point, determining one or more directional characteristics of the initial curb point, generating a point cluster based on the initial curb point, determining one or more characteristics for the point cluster, and identifying the point cluster as a portion of curb boundaries if the one or more characteristics satisfy a predetermined condition);
ascertaining context-sensitive information from an accompanying infrastructure or from a traffic-planning relevance of installation locations of elevated road markings, and/or d) analyzing the intensity-coded or background-light-coded 3D point cloud for regular patterns of an arrangement of elevated road markings (see at least [0109]: features extraction process 1104 extracts features or features representations from each segment of the current frame, and loop detection process 1105 compares the extracted features to features of previous frames…Examples of features include smoothness, linearity, and continuity of points for a segment (e.g., patterns), [0167]: identifying the road includes applying an elevation threshold to the point clouds to extract a number of likely ground points, applying a fitting algorithm to fit one or more planar objects to the likely ground points as an road object, and selecting a planar object with the largest surface area having a normal direction approximating a direction of a vertical axis of the LIDAR sensors as the road object).
As per claim(s) 12, Yuan discloses wherein, according to method step a), a map comparison is performed based on a geoposition of the vehicle with the map or observations of vehicles driving ahead in a cloud-based database (see at least [0063]: real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), [0105]: Submodule 1001 can also receive poses information from IMU and GPS units equipped by the ADV).
As per claim(s) 13, Yuan discloses wherein, according to method step d), an approximation of an analytically describable curve or a geometric plane is performed in such a way that the possible elevated road markings to be recognized are suitably associated with one another and a continuous plausibility check is carried out (see at least [0071]: objects can include traffic signals, road way boundaries, [0132]: probability indicating relatedness of these objects (e.g., they are the same object from two different frames), [0142]: curb curve, [0150]: extract points from point clouds which belong to curbs (e.g., the left and right boundaries of a road), [0174]: probability the voxel is occupied by an object, [0163]: road can be partitioned based on extracted road marking points and extracted curb points/curves).
As per claim(s) 14, Yuan discloses wherein, according to method step d), a regular repetition of the possible elevated road markings to be recognized is undertaken in each case at the same distances from one another along the analytically describable curve or the geometric plane (see at least [0109]: features extraction process 1104 extracts features or features representations from each segment of the current frame, and loop detection process 1105 compares the extracted features to features of previous frames…Examples of features include smoothness, linearity, and continuity of points for a segment (e.g., patterns), [0095]: loop, [0113]: Process 1107 applies a traditional ICP algorithm based on the point clouds of consecutive frames in the stream to perform point clouds registration for the stream of frames. Here, initially, process 1102-1105 can be iteratively applied to the frames of the stream, on a frame-to-frame basis).
As per claim(s) 16, Yuan discloses wherein, according to method step d), geometric plausibility checks are performed in order to determine a geometric regularity of an occurrence of elevated road markings, and demarcation from individual objects lying on the roadway is carried out (see at least [0095]: loop closure 701 may or may not contain other roadways within the loop closure. In other words, loop closure 701 (which road branch to take) can be identified based on a threshold of computational load that is required to align the poses for the point clouds in the loop closure. In one embodiment, a loop closure is identified based on a threshold of spatial area enclosing the loop closure, [0143]: static objects are retained for map generation, [0174]: probability the voxel is occupied by an object).
As per claim(s) 17, Yuan discloses wherein, according to method step d), a targeted look-ahead is carried out in such a way that a targeted extrapolation to further elevated road markings is undertaken from the intensity-coded or background-light coded 3D point cloud based on a few visible elevated road markings as a continuation of a recognized geometric regularity ([0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds, [0166], [0170]: identifying curbs boundaries for the road based on the curbs features includes, for each point in the point clouds, applying a machine learning algorithm to the extracted features to identify the point as an initial curb point; and for each initial curb point, determining one or more directional characteristics of the initial curb point, generating a point cluster based on the initial curb point, determining one or more characteristics for the point cluster, and identifying the point cluster as a portion of curb boundaries if the one or more characteristics satisfy a predetermined condition).
As per claim(s) 18, Yuan discloses wherein a comparison is undertaken of the intensity-coded 3D point cloud with the background-light-coded 3D point cloud (see at least [0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds).
As per claim(s) 19, Yuan discloses wherein crosstalk effects are minimized by comparing the background-light-coded point cloud with the intensity-coded point cloud in the case of signs of an infrastructure (see at least [0127]: A physical synchronization can include setting up camera sensors at a physical location to capture a same or substantially similar to a perspective view of a LIDAR sensor unit to be synced, [0131]: a number of point clouds (1701-1709) superimposed on a RGB image having a number of pixels).
As per claim(s) 20, Yuan discloses wherein the lidar system is used for reliable recognition and traversing classification of elevated road markings (see at least [0063]: Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), perception and planning system 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently, [0070], [0074]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 11-14, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of US 20220214187 (Liu).
As per claim(s) 11, Yuan discloses a method for recognizing and classifying elevated road markings using a lidar system of a vehicle, wherein the lidar system including a transmitting unit, a receiving unit, at least one detector, the method comprising the following method steps, which are carried out individually or in combination:
performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system (see at least abstract: road being captured by one or more point clouds from one or more LIDAR sensors, [0069]: localization module 301 may obtain location and route information from a location server and a map and POI (MPOI) server, [0070]: Based on the sensor data provided by sensor system 115 and localization information obtained by localization module 301, a perception of the surrounding environment is determined by perception module 302, [0071]: objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc, [0139]: server 103 includes map partition engine 126. Furthermore, algorithms/models 124 can include a number of algorithms/models to extract road markings and curbs points. Using on a map provided by map server 104 (or generated by HD map generation engine 125), engine 126 partitions the map into one or more portion based the road markings and curb points extraction);
ascertaining normal road markings from at least one intensity-coded or background-light coded 3D point cloud and comparing the ascertained normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system (see at least [0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds, [0166], [0170]: identifying curbs boundaries for the road based on the curbs features includes, for each point in the point clouds, applying a machine learning algorithm to the extracted features to identify the point as an initial curb point; and for each initial curb point, determining one or more directional characteristics of the initial curb point, generating a point cluster based on the initial curb point, determining one or more characteristics for the point cluster, and identifying the point cluster as a portion of curb boundaries if the one or more characteristics satisfy a predetermined condition);
ascertaining context-sensitive information from an accompanying infrastructure or from a traffic-planning relevance of installation locations of elevated road markings, and/or d) analyzing the intensity-coded or background-light-coded 3D point cloud for regular patterns of an arrangement of elevated road markings (see at least [0109]: features extraction process 1104 extracts features or features representations from each segment of the current frame, and loop detection process 1105 compares the extracted features to features of previous frames…Examples of features include smoothness, linearity, and continuity of points for a segment (e.g., patterns), [0167]: identifying the road includes applying an elevation threshold to the point clouds to extract a number of likely ground points, applying a fitting algorithm to fit one or more planar objects to the likely ground points as an road object, and selecting a planar object with the largest surface area having a normal direction approximating a direction of a vertical axis of the LIDAR sensors as the road object).
Should it be found that Yuan does not explicitly disclose performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system; ascertaining normal road markings from at least one intensity-coded or background-light coded 3D point cloud and comparing the ascertained normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system,
Liu teaches performing a comparison of a map, which contains information about a roadway with possible elevated road markings to be recognized along the roadway by the lidar system (see at least [0028]: localization prior map is encoded data that contains the three-dimensional boundaries of lane markers (e.g., painted lane markers and raised pavement markers), edges of curbs, edges of vertical objects or structure (e.g., poles), traffic directions, road surface manifolds, and/or other data required by autonomous vehicles' localization algorithms, [0043]: At a second step 704, the lane marker mapper 506b matches the point cloud lane marker boundaries, which can be retrieved from the lane marker classifier 506a, to the annotated lane marker boundaries within the retrieved localization prior map);
ascertaining normal road markings from at least one intensity-coded or background-light coded 3D point cloud and comparing the ascertained normal road markings with the possible elevated road markings to be recognized along the roadway by the lidar system (see at least [0041]: the lane marker classifier 506a is programmed to detect the portions of the lidar point cloud that lie on the boundaries between the lane marker indicators (e.g., paint or raised pavement markers) and the road surface (e.g., asphalt) according to the calibrated lidar intensity values and the geometries of the point cloud).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Liu with a reasonable expectation of success in order to allow autonomous vehicles to navigate more precisely within the environment.
As per claim(s) 12, Yuan discloses wherein, according to method step a), a map comparison is performed based on a geoposition of the vehicle with the map or observations of vehicles driving ahead in a cloud-based database (see at least [0063]: real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), [0105]: Submodule 1001 can also receive poses information from IMU and GPS units equipped by the ADV).
As per claim(s) 13, Yuan discloses wherein, according to method step d), an approximation of an analytically describable curve or a geometric plane is performed in such a way that the possible elevated road markings to be recognized are suitably associated with one another and a continuous plausibility check is carried out (see at least [0071]: objects can include traffic signals, road way boundaries, [0132]: probability indicating relatedness of these objects (e.g., they are the same object from two different frames), [0142]: curb curve, [0150]: extract points from point clouds which belong to curbs (e.g., the left and right boundaries of a road), [0174]: probability the voxel is occupied by an object, [0163]: road can be partitioned based on extracted road marking points and extracted curb points/curves).
As per claim(s) 14, Yuan discloses wherein, according to method step d), a regular repetition of the possible elevated road markings to be recognized is undertaken in each case at the same distances from one another along the analytically describable curve or the geometric plane (see at least [0109]: features extraction process 1104 extracts features or features representations from each segment of the current frame, and loop detection process 1105 compares the extracted features to features of previous frames…Examples of features include smoothness, linearity, and continuity of points for a segment (e.g., patterns), [0095]: loop, [0113]: Process 1107 applies a traditional ICP algorithm based on the point clouds of consecutive frames in the stream to perform point clouds registration for the stream of frames. Here, initially, process 1102-1105 can be iteratively applied to the frames of the stream, on a frame-to-frame basis).
As per claim(s) 16, Yuan discloses wherein, according to method step d), geometric plausibility checks are performed in order to determine a geometric regularity of an occurrence of elevated road markings, and demarcation from individual objects lying on the roadway is carried out (see at least [0095]: loop closure 701 may or may not contain other roadways within the loop closure. In other words, loop closure 701 (which road branch to take) can be identified based on a threshold of computational load that is required to align the poses for the point clouds in the loop closure. In one embodiment, a loop closure is identified based on a threshold of spatial area enclosing the loop closure, [0143]: static objects are retained for map generation, [0174]: probability the voxel is occupied by an object).
As per claim(s) 17, Yuan discloses wherein, according to method step d), a targeted look-ahead is carried out in such a way that a targeted extrapolation to further elevated road markings is undertaken from the intensity-coded or background-light coded 3D point cloud based on a few visible elevated road markings as a continuation of a recognized geometric regularity ([0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds, [0166], [0170]: identifying curbs boundaries for the road based on the curbs features includes, for each point in the point clouds, applying a machine learning algorithm to the extracted features to identify the point as an initial curb point; and for each initial curb point, determining one or more directional characteristics of the initial curb point, generating a point cluster based on the initial curb point, determining one or more characteristics for the point cluster, and identifying the point cluster as a portion of curb boundaries if the one or more characteristics satisfy a predetermined condition).
As per claim(s) 18, Yuan discloses wherein a comparison is undertaken of the intensity-coded 3D point cloud with the background-light-coded 3D point cloud (see at least [0094]: the point clouds with the adjusted poses are used to generate a high definition (HD) 3D point clouds map, [0107]: Point clouds characteristics can be used for segment extraction, such as intensity, texture, or proximity of objects represented by the point clouds, [0124]: Approaches to use RGB images captured by image devices of an ADV together with point clouds add additional information (e.g., colors) to the point clouds).
As per claim(s) 19, Yuan discloses wherein crosstalk effects are minimized by comparing the background-light-coded point cloud with the intensity-coded point cloud in the case of signs of an infrastructure (see at least [0127]: A physical synchronization can include setting up camera sensors at a physical location to capture a same or substantially similar to a perspective view of a LIDAR sensor unit to be synced, [0131]: a number of point clouds (1701-1709) superimposed on a RGB image having a number of pixels).
As per claim(s) 20, Yuan discloses wherein the lidar system is used for reliable recognition and traversing classification of elevated road markings (see at least [0063]: Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), perception and planning system 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently, [0070], [0074]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of US 20190370563 (Dahal), US 20220349725 (Chreptyk), and US 20190265046 (Agarwal).
As per claim(s) 15, Yuan does not explicitly disclose wherein, according to method step d), circles are drawn around the possible elevated road markings to be recognized, in such a way that intersections of adjacent circles form horizontal, vertical and mutually orthogonal straight lines.
However, Dahal teaches wherein, according to method step d), circles
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Dahal with a reasonable expectation of success in order to identify circular shaped raised pavement markers.
However, Chreptyk teaches wherein, according to method step d), circles are drawn around the possible road markings to be recognized (see at least [0138]: a quadrilateral shape may be used to specify bounding shapes (e.g., boxes, quadrilaterals, circles, etc.) for road markings).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Chreptyk with a reasonable expectation of success in order to provide a definite start and end for a road marking (see at least [0125]).
Further, Agarwal teaches a pattern of a roundabout, road markings, and raised pavement markers (see at least [0012]: available features may include detectable edges on a ground surface including a road boundary, a shape, a gradient, a pattern, a building, a bridge, a traffic light, a delineator, and a raised pavement marker, [0033], [0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Agarwal with a reasonable expectation of success in order to better navigate the vehicle based on detected sensor data of an area in which the vehicle is located.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan and Liu, and further in view of Dahal, Chreptyk, Agarwal.
As per claim(s) 15, Yuan does not explicitly disclose wherein, according to method step d), circles are drawn around the possible elevated road markings to be recognized, in such a way that intersections of adjacent circles form horizontal, vertical and mutually orthogonal straight lines.
However, Liu teaches wherein, according to method step d), circles are drawn around the possible elevated road markings to be recognized, in such a way that intersections of adjacent circles form horizontal, vertical and mutually orthogonal straight lines (see at least Fig. 4, [0009] FIG. 4 illustrates a lane graph map).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Liu with a reasonable expectation of success in order to allow autonomous vehicles to navigate more precisely within the environment.
However, Dahal teaches wherein, according to method step d), circles
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Dahal with a reasonable expectation of success in order to identify circular shaped raised pavement markers.
However, Chreptyk teaches wherein, according to method step d), circles are drawn around the possible road markings to be recognized (see at least [0138]: a quadrilateral shape may be used to specify bounding shapes (e.g., boxes, quadrilaterals, circles, etc.) for road markings).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Chreptyk with a reasonable expectation of success in order to provide a definite start and end for a road marking (see at least [0125]).
Further, Agarwal teaches a pattern of a roundabout, road markings, and raised pavement markers (see at least [0012]: available features may include detectable edges on a ground surface including a road boundary, a shape, a gradient, a pattern, a building, a bridge, a traffic light, a delineator, and a raised pavement marker, [0033], [0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Yuan by incorporating the teachings of Agarwal with a reasonable expectation of success in order to better navigate the vehicle based on detected sensor data of an area in which the vehicle is located.
Conclusion
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Angelina Shudy
Primary Examiner
Art Unit 3668
/Angelina M Shudy/Primary Examiner, Art Unit 3668
