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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
Claims 1-5 and 8-11 are pending in this application.
Claims 6-7 are cancelled.
Claims 1-5 and 8-11 are presented for examination.
Claim Rejections - 35 USC § 103
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Schmitz (US Publication 2008/0021608 A1) in view of Towal et al. (US Publication 2019/0384304 A1).
Regarding claim 1, Schmitz teaches a device for detecting a lane departure of a land vehicle, comprising (Schmitz: Para. 10; a system for driver assistance e.g., for warning and/or for activating an actuator for transversely guiding the vehicle in the event of departure or imminent departure from the lane) ……. , b. estimate a path followed by the vehicle (Schmitz: Para. 12; the course of the track of the vehicle, for example, for the right and/or left wheel, is calculated from vehicle geometry variables, the current and possibly preceding variables (magnitudes) of the vehicle velocity, the steering angle or the yaw rate), c. determine, depending on the shape and on the position of each of the boundaries of the traffic lane (Schmitz: Para. 12; lane (track) data is calculated from the cited data, such as the lateral distance between the lane marking and the track of the vehicle (right side to the right edge, left side to the left edge), the curvature of the track, and/or the angle between the track and the lane marking (right lane to right edge, left lane to left edge) on the basis of tangent comparisons), and on the path followed by the vehicle, a time interval before the vehicle will reach one of the boundaries of the traffic lane (Schmitz: Para. 12; the expected time to line crossing is also possibly calculated therefrom), and d. compare the time interval to a determined threshold and, if the time interval is less than the determined threshold, determine that the vehicle is in the process of departing from the traffic lane (Schmitz: Para. 17; the time curve of the lateral distance to the edge marking exceeds a threshold value), ……. , and to deduce therefrom the shape and the position of each boundary of the traffic lane taken by the vehicle (Schmitz: Para. 20; lane recognition algorithm ascertains variables from the camera image as described above, such as the lateral distance of the vehicle to the edge markings, the angle of the vehicle to the edge markings, and the curvature of the lane); and wherein the device is further configured, if the time interval is less than the determined threshold, to implement at least one action from the following group: generate a lane departure warning for a driver of the vehicle, generate instructions to modify the path of the vehicle in order to keep the vehicle in the traffic lane taken by the vehicle, or generate instructions to slow down the vehicle (Schmitz: Para. 15; if it has been recognized that the departure from the lane is unintentional, a visual, acoustic, and/or haptic warning is given and/or a lane retention reaction, such as the activation of an actuator to influence the steering).
Schmitz doesn’t explicitly teach an optical radar installed in the vehicle, the optical radar being capable of acquiring information relating to surroundings of the vehicle that can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling, and at least one computer, device being configured, when the vehicle is traveling on a road comprising at least one traffic lane, to: a. estimate, absent an analysis of road markings and on a basis of the shape and the position of at least one boundary of the road on which the vehicle is traveling, the shape and the position of each boundary of a traffic lane taken by the vehicle, to either side of it ..….. wherein the device is further configured to estimate, from data acquired by the optical radar and absent an analysis of road markings, the shape and the position of at least one road boundary by using a clothoid curve, a width of traffic lanes and a number of traffic lanes of the road on which the vehicle is traveling.
However Towal, in the same field of endeavor, teaches an optical radar installed in the vehicle, the optical radar (Towal: Para. 34; image data generated by one or more cameras of an autonomous vehicle; the sensor data may additionally or alternatively include other types of sensor data, such as LIDAR data from one or more LIDAR sensors) being capable of acquiring information relating to surroundings of the vehicle that can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling (Towal: Para. 34; image data; generate the path geometry(ies) 106 (e.g., as defined by one or more vertices of a line or points of a polyline, represented by delta values from anchor points, or anchor lines, as described herein) and/or the path type(s) (e.g., confidence values that a geometry of a path corresponds to one or more path types, such as an ego-path, paths adjacent the ego-path, all paths in an environment or field(s) of view of the camera(s), etc.)), and at least one computer, device being configured, when the vehicle is traveling on a road comprising at least one traffic lane, to: a. estimate, absent an analysis of road markings and on a basis of the shape and the position of at least one boundary of the road on which the vehicle is traveling, the shape and the position of each boundary of a traffic lane taken by the vehicle, to either side of it (Towal: Para. 123, 129, Fig. 6A, 7; the drivable paths may be defined along regions of the drivable surface where no visible delineation is present; annotations of the right edge and the left edge are included in an area where lane markings are not present) ..….. wherein the device is further configured to estimate, from data acquired by the optical radar and absent an analysis of road markings (Towal: Para. 123, 129, Fig. 6A, 7; the drivable paths may be defined along regions of the drivable surface where no visible delineation is present; annotations of the right edge and the left edge are included in an area where lane markings are not present), the shape and the position of at least one road boundary by using a clothoid curve, a width of traffic lanes and a number of traffic lanes of the road on which the vehicle is traveling (Towal: Para. 98, 140; parameterized approach may use parameterized curves such as, without limitation, clothoids or cubic polynomials; in order to generate the anchor lines using the parameterized approach, information about path shapes may be gathered; sensor data which may be analyzed to determine common or average shapes; identify path width based on ground truth data).
It would have been obvious to one having ordinary skill in the art to modify the lane departure driver assist system (Schmitz: Para. 10) with LIDAR detected road features (Towal: Para. 34) with a reasonable expectation of success because generating path geometries based on anchor points and lines allows for path generation by vehicle sensor data where lane markings or road boundaries would not conventionally exist (Towal: Para. 34, 123).
Regarding claim 2, Schmitz teaches the device as claimed in claim 1, the device being configured to determine said time interval by determining a point of intersection between a boundary of the traffic lane taken by the vehicle and the path of the vehicle (Schmitz: Para. 16; lateral distance of the vehicle to the lane marking, in particular its chronological change),and by calculating the time interval required by the vehicle to reach the point of intersection (Schmitz: Para. 21; the lateral distance and/or the time to line crossing).
Regarding claim 3, Schmitz teaches the device as claimed in claim 2, in which, if the path of the vehicle has a point of intersection with each of the boundaries of the traffic lane taken by the vehicle, the device is configured to select the point of intersection closest to the vehicle (Schmitz: Para. 16-17; lateral distance of the vehicle to the lane marking, in particular its chronological change; time curve of the lateral distance to the edge marking exceeds a threshold value).
Regarding claim 4, Schmitz teaches the device as claimed in claim 2, the device being configured to calculate a distance to be traveled by the vehicle between its current position and the point of intersection (Schmitz: Para. 16; variable is the lateral distance of the vehicle to the lane marking, in particular its chronological change) to deduce therefrom the time interval required by the vehicle to reach the point of intersection (Schmitz: Para. 17, 21; the lateral distance and/or the time to line crossing; the time curve of the lateral distance to the edge marking exceeds a threshold value and/or the angle to the edge marking exceeds a threshold value), taking into account a speed and an acceleration of the vehicle (Schmitz: Para. 11; for detecting a variable which represents an acceleration intent of the driver, such as the extent of the accelerator pedal actuation by the driver, for detecting the velocity and/or the acceleration of the vehicle, and for detecting further operating variables of the vehicle which are significant in connection with the procedure described).
Regarding claim 5, Schmitz teaches the device according to claim1, the device being configured to implement steps a. to d. iteratively, and, in a first iteration, to: estimate the path followed by the vehicle (Schmitz: Para. 14; program, which is run in the microcomputer of the device shown in FIG. 1 at predetermined time intervals; the course of the lane edge marking, the course of the actual lane of the vehicle, a variable for the lateral distance between vehicle and lane edge; ) ……. .
Schmitz doesn’t explicitly teach wherein the determined threshold is a first determined threshold and, if the time interval is greater than the first determined threshold but less than a second determined threshold greater than the first determined threshold, determine a lateral movement of the vehicle in the traffic lane taken by the vehicle and update, for the next iteration, one or more lateral positions of the boundaries of the traffic lane taken by the vehicle in relation to the vehicle.
However, Schmitz is deemed to disclose an equivalent teaching. Schmitz teaches an iterative computation cycle, at predetermined time intervals, where the current location of the vehicle in the lane and the predicted future course of the vehicle versus the lane edge markings. The lateral distance between the vehicle and the lane edge are calculated. The system checks to see whether the vehicle is currently departing from the lane or a lane departure is imminent. If it is determined that a lane departure is not imminent then then the cycle starts again (Schmitz: Para. 14). A first threshold is represented by the determination of a lane departure has happened, and a second threshold where lane departure is imminent. When the lateral distance from the vehicle to the lane edge A second threshold which is bigger is that a lane departure is not imminent and thus the cycle of computation will start again.
It would have been obvious to one of ordinary skill before the effective filing date to have create two thresholds for the iterative computation cycle that is taught in Schmitz with a reasonable expectation of success because if the lane departure has not happened and is not imminent then the system should go back to the top of the cycle and calculate all the values again representing the vehicle as it travels down the road real-time (Schmitz: Para. 14).
Schmitz doesn’t explicitly teach assuming that the vehicle is in the middle of the traffic lane taken by the vehicle.
However Towal, in the same field of endeavor, teaches assuming that the vehicle is in the middle of the traffic lane taken by the vehicle (Towal: Para. 96; assume that the first confidence value in the path type corresponds to a left ego-path, the middle value corresponds to the ego-path, and the right value corresponds to the right ego-path).
It would have been obvious to one having ordinary skill in the art to modify the lane departure driver assist system (Schmitz: Para. 10) with LIDAR detected road features (Towal: Para. 34) with a reasonable expectation of success because generating path geometries based on anchor points and lines allows for path generation by vehicle sensor data where lane markings or road boundaries would not conventionally exist (Towal: Para. 34, 123).
Regarding claim 8, Schmitz teaches a method for detecting a lane departure of a land vehicle, implemented by the device according to claim 1, the method comprising: (Schmitz: Para. 10; a system for driver assistance e.g., for warning and/or for activating an actuator for transversely guiding the vehicle in the event of departure or imminent departure from the lane) estimating a shape and a position of each boundary of a traffic lane taken by the vehicle, on a basis of the shape and the position of at least one boundary of the road containing the traffic lane taken by the vehicle (Schmitz: Para. 12; lane data is ascertained on the basis of the image data of the scene in front of the vehicle supplied by the image sensor system, which represents the course and the size of the lane; the lane edge markings (left and/or right lane edge) are detected and the course of the particular lane edge), estimating a path followed by the vehicle (Schmitz: Para. 12; the course of the track of the vehicle, for example, for the right and/or left wheel, is calculated from vehicle geometry variables, the current and possibly preceding variables (magnitudes) of the vehicle velocity, the steering angle or the yaw rate), determining, depending on the shape and the position of the boundaries of the traffic lane taken by the vehicle (Schmitz: Para. 12; lane (track) data is calculated from the cited data, such as the lateral distance between the lane marking and the track of the vehicle (right side to the right edge, left side to the left edge), the curvature of the track, and/or the angle between the track and the lane marking (right lane to right edge, left lane to left edge) on the basis of tangent comparisons), and the path followed by the vehicle, a time interval before the vehicle reaches a boundary of the traffic lane taken by the vehicle (Schmitz: Para. 12; the expected time to line crossing is also possibly calculated therefrom), and comparing the time interval to a determined threshold and, if the time interval is less than the determined threshold, determining that the vehicle is in a process of departing from or is going to depart from the traffic lane taken by the vehicle (Schmitz: Para. 17; the time curve of the lateral distance to the edge marking exceeds a threshold value), the estimation of the shape and the position of at least one road boundary, by using a clothoid curve, a width of traffic lanes and a number of traffic lanes of the road containing the traffic lane taken by the vehicle (Schmitz: Para. 20; lane recognition algorithm ascertains variables from the camera image as described above, such as the lateral distance of the vehicle to the edge markings, the angle of the vehicle to the edge markings, and the curvature of the lane), to deduce therefrom the shape and the position of each boundary of the traffic lane taken by the vehicle (Schmitz: Para. 20; lane recognition algorithm ascertains variables from the camera image as described above, such as the lateral distance of the vehicle to the edge markings, the angle of the vehicle to the edge markings, and the curvature of the lane).
Schmitz doesn’t explicitly teach being made from data acquired by the optical radar and absent an analysis of road markings.
However Towal, in the same field of endeavor, teaches being made from data acquired by the optical radar and absent an analysis of road markings (Towal: Para. 34, 123, 129, Fig. 6A, 7; image data generated by one or more cameras of an autonomous vehicle; the drivable paths may be defined along regions of the drivable surface where no visible delineation is present).
It would have been obvious to one having ordinary skill in the art to modify the lane departure driver assist system (Schmitz: Para. 10) with LIDAR detected road features (Towal: Para. 34) with a reasonable expectation of success because generating path geometries based on anchor points and lines allows for path generation by vehicle sensor data where lane markings or road boundaries would not conventionally exist (Towal: Para. 34, 123).
Regarding claim 9, Schmitz teaches a non-transitory computer program product comprising code instructions for implementing the method as claimed in claim 8,when the computer program is executed by a processor (Schmitz: Para. 14; outlines a corresponding program, which is run in the microcomputer of the device).
Regarding claim 10, Schmitz teaches a non-transitory computer-readable recording medium on which a program is recorded for implementing the method as claimed in claim 8, when the program is executed by a processor (Schmitz: Para. 10; control and/or analysis unit is shown, which has at least one input circuit, a microcomputer, and an output circuit).
Regarding claim 11, Schmitz teaches the device as claimed in claim 3, the device being configured to calculate a distance to be traveled by the vehicle between its current position and the selected point of intersection (Schmitz: Para. 16; variable is the lateral distance of the vehicle to the lane marking, in particular its chronological change), and to deduce therefrom the time interval required by the vehicle to reach the selected point of intersection (Schmitz: Para. 17, 21; the lateral distance and/or the time to line crossing; the time curve of the lateral distance to the edge marking exceeds a threshold value and/or the angle to the edge marking exceeds a threshold value), taking into account a speed and an acceleration of the vehicle (Schmitz: Para. 11; for detecting a variable which represents an acceleration intent of the driver, such as the extent of the accelerator pedal actuation by the driver, for detecting the velocity and/or the acceleration of the vehicle, and for detecting further operating variables of the vehicle which are significant in connection with the procedure described).
Response to Arguments
Applicant’s arguments, filed on 13 May 2026, with respect to claims 1-5 and 8-11 have been considered but are not persuasive.
The applicant’s attorney argues that one having ordinary skill in the art would have no expectation of success, reasonable or otherwise, in arriving at the above features of independent claim 1 via the proposed combination because Towal in no way describes or discloses how its LIDAR data could or would be used within its method or how a clothoid curve could or would be used with only the LIDAR data to estimate the shape and position of a road boundary, and thus, in no way enables one having ordinary skill in the art to make such a combination.
In response to applicant's argument that one having ordinary skill in the art would have no expectation of success, reasonable or otherwise, in arriving at the above features of independent claim 1 via the proposed combination of Schmitz and Towal, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
The applicant next argues that Towal in no way discloses, describes, or enables such a path determination method using only non-image sensor data.
In response to the applicant’s argument above, the applicant claims “an optical radar installed in the vehicle, the optical radar, being capable of acquiring information relating to surroundings of the vehicle that can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling.” The broadest reasonable interpretation of the quoted limitation is of a optical radar installed in the vehicle capable of acquiring information of the vehicle’s surroundings that can be used to estimate a shape and a position of a road boundary. The BRI is not limited to only using an optical radar or evening using the optical radar only that the optical radar is installed and capable. The examiner suggests amending the claim language to directly claim the optical radar acquiring the information and based solely on that information determining a road boundary which would move the BRI of the limitation in line with the applicant’s arguments.
Towal teaches a LIDAR and/or RADAR in a vehicle. Towal teaches sensor data may additionally or alternatively includes data from LIDAR(s) or RADAR(s) (Towal: Para. 34). As for how LIDAR can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling. One of ordinary skill of the art knows LIDAR as a point cloud map. This map is very good at identifying differing heights. The edge of the road that is buffered by a curb would have a ledge shown in the point cloud data. A soft shoulder where the earth is lower than the road surface would should the drop-off in the point cloud data. It would be obvious to one of ordinary skill in the art that RADAR can similarly detected a curb or the concrete wall of a tunnel.
Towal teaches a drivable path as a region of a drivable surface defined by no visible delineation present (Towal: Para. 123). The drivable surface would be seen in the point cloud data as flat, and the shape of the road can be calculated from the detected borders. It would be obvious to one of ordinary skill in the art to have any local street maps, location, known standard widths of road and lanes for that country to be integrated into any machine learning model that is using detected data to determine road shape.
The applicant next argues that Towal fails to disclose or suggest how such drivable pathway prediction could or would be possible using solely optical radar data.
In response to the applicant’s argument above, as explained above. The applicant doesn’t claim solely using optical radar data to predict a drivable pathway.
Towal teaches a drivable path as a region of a drivable surface defined by no visible delineation present (Towal: Para. 123). Towal teaches sensor data may additionally or alternatively includes data from LIDAR(s) or RADAR(s) (Towal: Para. 34). As for how LIDAR can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling. One of ordinary skill of the art knows LIDAR as a point cloud map. This map is very good at identifying differing heights. The edge of the road that is buffered by a curb would have a ledge shown in the point cloud data. A soft shoulder where the earth is lower than the road surface would should the drop-off in the point cloud data. It would be obvious to one of ordinary skill in the art that RADAR can similarly detected a curb or the concrete wall of a tunnel.
The drivable surface would be seen in the point cloud data as flat, and the shape of the road can be calculated from the detected borders. It would be obvious to one of ordinary skill in the art to have any local street maps, location, known standard widths of road and lanes for that country to be integrated into any machine learning model that is using detected data to determine road shape.
The applicant next argues that Towal fails to describe or disclose in any way how such a machine learning model could or would automatically annotate the ground truth image data.
In response to the applicant’s argument above, the applicant is not claiming machine learning annotation. Towal teaches in Figure 9 receiving image data representative of an image (Towal: Figure 9). A LIDAR produces a point cloud data set. Towal teaches sensor data may additionally or alternatively includes data from LIDAR(s) or RADAR(s) (Towal: Para. 34). As for how LIDAR can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling. One of ordinary skill of the art knows LIDAR as a point cloud map. This map is very good at identifying differing heights. The edge of the road that is buffered by a curb would have a ledge shown in the point cloud data. A soft shoulder where the earth is lower than the road surface would should the drop-off in the point cloud data. It would be obvious to one of ordinary skill in the art that RADAR can similarly detected a curb or the concrete wall of a tunnel. A machine learning model could identify the curb, railing, or wall and the first points of ground, or road, on the vehicle side of the point cloud. If you connect the noted first points of road the system could display a rough line noting the drivable portion of the field of view. A machine learning model can annotate the area between the rough lines on either side of the vehicle as drivable portion. The machine learning model can annotate the area where the point cloud notes a railing, drop-off, curb, or wall away from the road as undrivable area.
The applicant next argues that Towal contains no description or disclosure of how its machine learning model could or would predict drivable pathways or identify portions of a road where no markings are present using only LIDAR or radar data.
In response to the applicant’s argument above, as explained above. The applicant doesn’t claim solely using optical radar data to predict a drivable pathway.
Towal teaches a drivable path as a region of a drivable surface defined by no visible delineation present (Towal: Para. 123). Towal teaches sensor data may additionally or alternatively includes data from LIDAR(s) or RADAR(s) (Towal: Para. 34). As for how LIDAR can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling. One of ordinary skill of the art knows LIDAR as a point cloud map. This map is very good at identifying differing heights. The edge of the road that is buffered by a curb would have a ledge shown in the point cloud data. A soft shoulder where the earth is lower than the road surface would should the drop-off in the point cloud data. It would be obvious to one of ordinary skill in the art that RADAR can similarly detected a curb or the concrete wall of a tunnel.
The drivable surface would be seen in the point cloud data as flat, and the shape of the road can be calculated from the detected borders. It would be obvious to one of ordinary skill in the art to have any local street maps, location, known standard widths of road and lanes for that country to be integrated into any machine learning model that is using detected data to determine road shape.
The applicant next argues Towal contains no description or disclosure of how its machine learning model could or would be trained using only LIDAR or radar data or how its machine learning model could or would predict drivable pathways or identify portions of a road where no road markings are present using only LIDAR or radar data.
In response to the applicant’s argument above, as explained above. The applicant doesn’t claim solely using optical radar data to predict a drivable pathway.
Towal teaches a drivable path as a region of a drivable surface defined by no visible delineation present (Towal: Para. 123). Towal teaches sensor data may additionally or alternatively includes data from LIDAR(s) or RADAR(s) (Towal: Para. 34). As for how LIDAR can be used to estimate a shape and a position of at least one boundary of a road on which the vehicle is traveling. One of ordinary skill of the art knows LIDAR as a point cloud map. This map is very good at identifying differing heights. The edge of the road that is buffered by a curb would have a ledge shown in the point cloud data. A soft shoulder where the earth is lower than the road surface would should the drop-off in the point cloud data. It would be obvious to one of ordinary skill in the art that RADAR can similarly detected a curb or the concrete wall of a tunnel.
The drivable surface would be seen in the point cloud data as flat, and the shape of the road can be calculated from the detected borders. It would be obvious to one of ordinary skill in the art to have any local street maps, location, known standard widths of road and lanes for that country to be integrated into any machine learning model that is using detected data to determine road shape.
The applicant next argues that Towal does not describe or disclose if/how such predetermined anchor points or predefined anchor lines could or would be implemented with the use of only LIDAR or radar data, only that they are overlayed on images.
In response to the applicant’s argument above, as explained above. The applicant doesn’t claim solely using optical radar data to implement predetermined anchor points or predetermined anchor lines. Towal teaches obtaining sensor data from vehicle sensors (Towal: Para. 34). Towal teaches a machine learning model trained to predict paths that extend continuously in space (Towal: Para. 123). Towal teaches a machine learning model trained to compute the locations of the vertices of the path rail (Towal: Para. 98). It is obvious to one of ordinary skill in the art to know that a lidar point cloud data will show a railing as different than the road surface if both are in the same field of view. Marking the placement at the edge of the road bordered by a curb or railing would be a way to determine anchor point or anchor lines.
The applicant next argues Towal does not disclose or suggests that an estimated shape or position of a road boundary, in the form of a clothoid curve is generated based on optical radar data.
In response to the applicant’s argument above, Towal teaches obtaining sensor data from vehicle sensors (Towal: Para. 34). Towal teaches a machine learning model trained to predict paths that extend continuously in space (Towal: Para. 123). Towal teaches a machine learning model trained to compute the locations of the vertices of the path rail (Towal: Para. 98). It is obvious to one of ordinary skill in the art to know that a lidar point cloud data will show a railing as different than the road surface if both are in the same field of view. Marking the placement at the edge of the road bordered by a curb or railing would be a way to determine the ground truth and vertices. The system can also use collected information based on the path shapes and estimate the road based on a parameterized curve, such as a clothoid (Towal: Para. 140). It is well known to one of ordinary skill in the art of autonomous vehicle navigation that a clothoid curve is where the curvature varies linearly with arc length and is ideal and routine in construction of highway or railway transition curves.
The applicant next argues that Towal provides no disclosure, teaching, or suggestion of how LIDAR or radar sensor data alone could or would be used to estimate the shape and the position of road boundaries/lanes, let alone using a clothoid curve to do so.
In response to the applicant’s argument above, Towal teaches obtaining sensor data from vehicle sensors (Towal: Para. 34). Towal teaches a machine learning model trained to predict paths that extend continuously in space (Towal: Para. 123). Towal teaches a machine learning model trained to compute the locations of the vertices of the path rail (Towal: Para. 98). It is obvious to one of ordinary skill in the art to know that a lidar point cloud data will show a railing as different than the road surface if both are in the same field of view. Marking the placement at the edge of the road bordered by a curb or railing would be a way to determine the ground truth and vertices. The system can also use collected information based on the path shapes and estimate the road based on a parameterized curve, such as a clothoid (Towal: Para. 140). It is well known to one of ordinary skill in the art of autonomous vehicle navigation that a clothoid curve is where the curvature varies linearly with arc length and is ideal and routine in construction of highway or railway transition curves.
The applicant next argues that there exists no expectation of success, reasonable or otherwise, of arriving at the combination of features of independent claim 1 based on the disclosures of Towal and Schmitz.
In response to applicant's argument that one having ordinary skill in the art would have no expectation of success, reasonable or otherwise, in arriving at the above features of independent claim 1 via the proposed combination of Schmitz and Towal, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
The applicant next argues that claims 2-5 and 8-11 dependent on independent claim 1, and are allowable at least based on their dependencies.
In response to the applicant’s argument above, claim 1 is rejected. Therefore claims 2-5 and 8-11 are rejected at least based on their dependencies.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Schmitz’s lane imminent departure warning with Towal’s optical radar data with machine learning calculations reads on applicant’s device and method for detecting a vehicle exiting its lane. The rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571)272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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