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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) were/was submitted on 03/03/2026. The information disclosure statement(s) have/has been considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Federal Republic of Germany on 11/25/2020.
Status of Application
Claims 1, 3-9, and 11-14, are pending.
Claims 1, 3, and 9, are amended.
No claims are withdrawn from consideration.
Claims 2 and 10 are cancelled.
No claims are added.
Claims 1, and 9, are independent claims.
Claims 1, 3-9, and 11-14, will be examined.
This Final Office action is in response to the “Amended Claims,” and “Applicant Arguments/Remarks,” dated 03/25/2026.
Response to Arguments
Applicant’s Remarks/Arguments and amended claims, filed 03/25/2026, with respect to claims 1, 3-9, and 11-14,, have been fully considered and Applicant' s remarks will be addressed in sequential order as they were presented.
Regarding Rejections under 35 U.S.C. 103, and the remarks, “addition to the limitations of claim 2, the amendment made to claim 1 also requires the generation of a population comprising the first observed state vector and two or more first sampling state vectors. The amendment further requires the computation of the predicted state vector for the second measurement instance to depend on the at least one motion parameter and the population comprising the first observed state vector and the two or more first sampling state vectors. None of these additional limitations are disclosed in Moskowitz in view of Shirato, Gummadi, Mueter, and Otto, whether considered separately or in combination,” the Office respectfully disagrees. The prior art Otto discloses/teaches “the generation of a population comprising the first observed state vector and two or more first sampling state vectors” within paragraph(s) (¶¶ [0006-0012], [0038-0050], and also FIG. 2). The limitation, “generation of a population,” within the context of the claim is referring to the simple grouping of data from the “first observed state vector” and the “two or more first sampling state vectors.” The prior art Otto discloses vector data gathered over time, see paragraphs [0042], “motion state data… translation based on the elapsed time and the current velocity in the measured direction,” and [0044], “the predicted sate vector and the previous state vector are expressed for a given iteration (time) k… each discrete time increment, a linear operator is applied to the previous state to generate the new state with an uncertainty.” The vectors data grouping over time (i.e. generation of a population) includes “the first observed state vector and two or more first sampling state vectors.”
Regarding applicant's remarks “a person of ordinary skill in the art would have no motivation to supply the missing elements without the benefit of Applicant's own disclosure as a guide,” is an argument equivalent to hindsight reasoning being improper. Regarding an argument of hindsight reasoning, using the invention as a roadmap to find its prior art components, it must be recognized that any judgement on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. In re McLaughlin, 443 F.2d 1392; 170 USPQ209 (CCPA 1971).
It remains the Offices stance that the cited prior art anticipates or renders obvious this claimed subject matter.
Applicant further argues that the other independent claims which recite similar features are allowable and the dependent claims are also allowable since they depend on allowable subject and the Office respectfully disagrees. It is the Office's stance that all of the claimed subject matter has been properly rejected; therefore the Office's respectfully disagrees with applicant' s arguments.
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.
Claims 1, 3-9, and 11-14, are rejected under 35 U.S.C. 103 as being unpatentable over MOSKOWITZ et al., US 20210063162, herein further known as Moskowwitz, in view of OTTO et al., US 20200216076, herein further known as Otto, further in view of SHIRATO et al., US 6,823,241, herein further known as Shirato, further in view of GUMMADI et al., US 20230382423, herein further known as Gummadi, further in view of MUETER et al., US 20170068862, herein further known as Mueter.
Regarding claim 1, Moskowwitz discloses road marking detection (¶ [0029], see also FIG. 5C, ¶¶ [0054-0060], see also FIGS. 24A-24F), wherein a first sensor dataset depicting a road marking (¶¶ [0168-0171]) at a first measurement instance (¶¶ [0320], [0324], [0332-0333], [0342] and a second sensor dataset depicting the road marking (¶¶ [0168-0171]) at a second measurement instance (¶¶ [0320], [0324], [0332-0333], [0342]) are generated by using an environmental sensor system (¶¶ [0070], [0386]) comprising a lidar system (¶¶ [0136], [0140], [0386]), the method comprising: determining at least one motion parameter, the at least one motion parameter characterizing a motion of the environmental sensor system (¶¶ [0070], [0240], [0247], [0386]) relative to the road marking (¶¶ [0168-0171]) between the first measurement instance and the second measurement instance (¶¶ [0320], [0324], [0332-0333], [0342]) and a computing unit is used (¶¶ [0310-0314]) to; generate, based on the first sensor dataset (¶¶ [0168-0171]), a first observed road marking geometrically (¶¶ [0153-0160]) at the first measurement instance (¶¶ [0320], [0324], [0332-0333], [0342]); generate, based on the second sensor dataset, a second observed road marking geometrically (¶¶ [0153-0160]) at the second measurement instance (¶¶ [0320], [0324], [0332-0333], [0342]), wherein a motor vehicle carries out physical maneuvers at least in part automatically depending on the corrected vector (¶¶ [0069-0070].
However, Moskowwitz does not explicitly state generate a population comprising the first observed state vector and two or more first sampling state vectors, the two or more being for the first measurement instance depending on the first observed state vector and depending on at least one parameter describing a multidimensional distribution; the motion parameter comprising a yaw rate, sensor dataset comprising point clouds generated based on sensor signals of the lidar system, and a state vector describing the road marking; and compute a predicted state vector instance depending on the at least one motion parameter and the first observed state vector using a state transition function with a non-linearity based on the yaw rate; and generate a corrected state vector for the second measurement instance depending on the predicted state vector and the second observed state vector.
Otto teaches generate a population comprising the first observed state vector and two or more first sampling state vectors (¶¶ [0042-0044], grouping of vector data gathered over time (i.e. population) , the two or more being for the first measurement instance depending on the first observed state vector and depending on at least one parameter describing a multidimensional distribution (¶¶ [0006-0012], [0038-0050], and also FIG. 2).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz generate a population comprising the first observed state vector and two or more first sampling state vectors, the two or more being for the first measurement instance depending on the first observed state vector and depending on at least one parameter describing a multidimensional distribution as taught by Moskowwitz. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Moskowwitz.
One would be motivated to modify Moskowwitz in view of Moskowwitz for the reasons stated in Moskowwitz paragraph [0023], a more robust method wherein accuracy of locating the ego-vehicle's position with respect to the lane boundary may be improved.
Furthermore, Shirato teaches the motion parameter comprising a yaw rate, and state vector using a state transition function with a non-linearity based on the yaw rate (column 9, line 14 through column 11, line 25, see vehicle yaw angle ϕ, and non-linear function of the state variables as indicated by Equation (24)).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention, with a reasonable expectation of success, to incorporate in to Moskowwitz the motion parameter comprising a yaw rate, and state vector using a state transition function with a non-linearity based on the yaw rate as taught by Shirato.
One would be motivated to modify Moskowwitz in view of Shirato for the reasons stated in Shirato column 1, more robust methods and system to provide an improved lane recognition apparatus which is capable of accurately estimating a road shape (a lane) while stably performing the estimation against disturbances. Furthermore, the more robust methods and system improve lane follow-up properties by providing feedback of state quantities.
Furthermore, Gummadi teaches sensor dataset comprising point clouds generated based on sensor signals of the lidar system (¶ [0006]).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz the sensor dataset comprising point clouds generated based on sensor signals of the lidar system as taught by Gummadi. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Gummadi.
One would be motivated to modify Moskowwitz in view of Gummadi for the reasons stated in Gummadi paragraph [0002], more robust system and method that applies a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizes point cloud data from the navigation area with the processed camera images, and associates probabilities that the cell is occupied and object classifications.
Furthermore, Mueter teaches a state vector describing the road marking (¶¶ [0023-0024]); and compute a predicted state vector (¶¶ [0003-0004], [0023-0024]) for the second measurement instance (¶¶ [0042], [0045]) depending on the at least one motion parameter (¶¶ [0035], [0049]) and the first observed state vector (¶¶ [0003-0004], [0023-0024], [0042], [0045]); and generate a corrected state vector (¶¶ [0003], adjusted with subsequent detections, [0004], inventive method has a predictor-corrector structure) for the second measurement instance depending on the predicted state vector and the second observed state vector (¶¶ [0003-0004], [0023-0024]).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz a state vector describing the road marking; and compute a predicted state vector instance depending on the at least one motion parameter and the first observed state vector; and generate a corrected state vector for the second measurement instance depending on the predicted state vector and the second observed state vector as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Regarding claim 3, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 1 above.
Moskowwitz discloses the computing unit (¶¶ [0310-0314]), first sensor dataset to approximate the road marking at the first measurement instance, second sensor dataset (¶¶ [0168-0171]) to approximate the road marking at the second time instance (¶¶ [0153-0160]) (¶¶ [0320], [0324], [0332-0333], [0342]).
However, Moskowwitz does not explicitly state determine a first polynomial depending sensor dataset and to determine a second polynomial depending on the sensor dataset wherein the first observed state vector comprises coefficients of the first polynomial and the second observed state vector comprises coefficients of the second polynomial.
Mueter teaches determine a first polynomial (¶¶ [0022-0024], [0037-0040]) and to determine a second polynomial (¶¶ [0022-0024], [0037-0040]) wherein the first observed state vector comprises coefficients of the first polynomial and the second observed state vector comprises coefficients of the second polynomial (¶¶ [0021-0026], [0036-0040]).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz determine a first polynomial depending sensor dataset and to determine a second polynomial depending on the sensor dataset wherein the first observed state vector comprises coefficients of the first polynomial and the second observed state vector comprises coefficients of the second polynomial as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Additionally, the claimed invention is merely a combination of known elements of lane detection for a camera-based driver assistance system, images captured by the camera which are identified as recognized lane markings, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 4, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 1 above.
Moskowwitz discloses the computing unit (¶¶ [0310-0314]).
However, Moskowwitz does not explicitly state compute a predicted covariance matrix for the second measurement instance depending on the at least one motion parameter, the first observed state vector and the predicted state vector; and generate a corrected covariance matrix for the second measurement instance depending on the predicted covariance matrix and the second observed state vector.
Otto teaches compute a predicted covariance matrix for the second measurement instance depending on the at least one motion parameter, the first observed state vector and the predicted state vector (¶¶ [0011-0012], [0020], [0048]); and generate a corrected covariance matrix for the second measurement instance depending on the predicted covariance matrix and the second observed state vector (¶¶ [0011-0012], [0020], [0048]).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz compute a predicted covariance matrix for the second measurement instance depending on the at least one motion parameter, the first observed state vector and the predicted state vector; and generate a corrected covariance matrix for the second measurement instance depending on the predicted covariance matrix and the second observed state vector as taught by Moskowwitz. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Moskowwitz.
One would be motivated to modify Moskowwitz in view of Moskowwitz for the reasons stated in Moskowwitz paragraph [0023], a more robust method wherein accuracy of locating the ego-vehicle's position with respect to the lane boundary may be improved.
Regarding claim 5, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 1 above.
Moskowwitz discloses a plurality of sensor datasets (¶¶ [0168-0171]) depicting the road marking at respective consecutive measurement instances (¶ [0320], intervals sufficient to create a mapped lane mark, one point per meter, one point per every five meters) is generated by using the environmental sensor system (¶¶ [0153-0160]), (¶¶ [0168-0171]), (¶¶ [0070], [0386]), wherein the plurality of sensor datasets includes the first sensor dataset and the second sensor dataset (¶¶ [0168-0171]) (¶¶ [0153-0160]) (¶¶ [0320], [0324], [0332-0333], [0342]); for each of the measurement instances (¶ [0320]), the at least one motion parameter characterizing the motion of the environmental sensor system relative to the road marking (¶¶ [0070], [0240], [0247], [0386]) between the respective measurement instance and a respective subsequent measurement instance is determined (¶¶ [0320], [0324], [0332-0333], [0342]); for each of the measurement instances (¶ [0320]), the computing unit is used (¶¶ [0310-0314]) to generate a respective observed state describing the road marking geometrically (¶¶ [0153-0160]) at the respective measurement instance based on the respective sensor dataset (¶¶ [0168-0171]) (¶¶ [0153-0160]) (¶¶ [0320], [0324], [0332-0333], [0342]); for each of the measurement instances (¶ [0320] except a final measurement instance, and computing unit (¶¶ [0310-0314]).
However, Moskowwitz does not explicitly state compute a predictive state vector describing the road marking; and compute a predicted state vector for a respective subsequent measurement instance depending on the respective at least one motion parameter and the observed state vector of the respective measurement instance, and generate a corrected state for the respective subsequent measurement instance depending on the predicted state for the respective subsequent measurement instance and the observed state of the respective subsequent measurement instance.
Mueter teaches compute a predictive state vector describing the road marking; and compute a predicted state vector for a respective subsequent measurement instance (¶¶ [0003-0004], [0023-0024]) depending on the respective at least one motion parameter (¶¶ [0035], [0049]) and the observed state vector of the respective measurement instance, (¶¶ [0021-0026], [0036-0040]) and generate a corrected state (¶¶ [0003], adjusted with subsequent detections, [0004], inventive method has a predictor-corrector structure) for the respective subsequent measurement instance depending on the predicted state for the respective subsequent measurement instance and the observed state (¶¶ [0021-0026], [0036-0040]).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz to compute a predictive state vector describing the road marking; and compute a predicted state vector for a respective subsequent measurement instance depending on the respective at least one motion parameter and the observed state vector of the respective measurement instance, and generate a corrected state for the respective subsequent measurement instance depending on the predicted state for the respective subsequent measurement instance and the observed state of the respective subsequent measurement instance as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Additionally, the claimed invention is merely a combination of known elements of lane detection for a camera-based driver assistance system, images captured by the camera which are identified as recognized lane markings, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 6, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 1 above.
Moskowwitz discloses guiding the motor vehicle at least in part automatically (¶ [0068-0071]), road marking detection (¶ [0029], see also FIG. 5C, ¶¶ [0054-0060], see also FIGS. 24A-24F), wherein the environmental sensor system is mounted on the motor vehicle (¶ [0090], [0129--0130]); guiding the motor vehicle at least in part automatically (¶¶ [0068-0071]), wherein the physical maneuvers comprise one or more of steering, deceleration, and acceleration (¶ [0069]).
However, Moskowwitz does not explicitly state depending on the corrected state vector.
Mueter teaches depending on the corrected state vector (¶¶ [0003], adjusted with subsequent detections, [0004], inventive method has a predictor-corrector structure).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz depending on the corrected state vector as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Additionally, the claimed invention is merely a combination of known elements of lane detection for a camera-based driver assistance system, images captured by the camera which are identified as recognized lane markings, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 7, the combination of Moskowwitz, Shirato, Gummadi, and Mueter, disclose all elements of claim 6 above.
Moskowwitz discloses computing unit is used to generate electronic map data representing an environment of the motor vehicle (¶¶ [0070], [0132], and [0239]); and the motor vehicle is guided at least in part automatically depending on the electronic map data (¶ [0068-0071]) and (¶ [0070], navigating).
However, Moskowwitz does not explicitly state depending on the corrected state vector.
Mueter teaches depending on the corrected state vector (¶¶ [0003], adjusted with subsequent detections, [0004], inventive method has a predictor-corrector structure).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz depending on the corrected state vector as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Additionally, the claimed invention is merely a combination of known elements of lane detection for a camera-based driver assistance system, images captured by the camera which are identified as recognized lane markings, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 8, the combination of Moskowwitz, Shirato, Gummadi, and Mueter, disclose all elements of claim 6 above.
Moskowwitz discloses one or more control signals for guiding the motor vehicle (¶ [0122]) at least in part automatically (¶ [0068-0071]).
However, Moskowwitz does not explicitly state depending on the corrected state vector.
Mueter teaches depending on the corrected state vector (¶¶ [0003], adjusted with subsequent detections, [0004], inventive method has a predictor-corrector structure).
It would have been obvious to person of ordinary skill in the art before the effective filing date of the invention to incorporate in to Moskowwitz depending on the corrected state vector as taught by Mueter. Furthermore, a person having ordinary skill in the art would have a reasonable expectation of success in combining the teachings of Moskowwitz, and Mueter.
One would be motivated to modify Moskowwitz in view of Mueter for the reasons stated in Mueter paragraph [0005], a more robust system and method wherein a model on which tracking is based can be brought in line with reality, and avoid significantly reducing the performance of a driver assistance system which is not acceptable particularly in autonomous vehicles.
Additionally, the claimed invention is merely a combination of known elements of lane detection for a camera-based driver assistance system, images captured by the camera which are identified as recognized lane markings, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Regarding claim 9, all limitations have been examined with respect to the method/steps in claim 1. The system/apparatus taught/disclosed in claim 9 can clearly perform the method/steps of claim 1. Therefore, claim 9 is rejected under the same rationale as claim 1 above.
Regarding claim 11, all limitations have been examined with respect to the method/steps in claim 8. The system/apparatus taught/disclosed in claim 11 can clearly perform the method/steps of claim 8. Therefore, claim 11 is rejected under the same rationale as claim 8 above.
Regarding claim 12, the combination of Moskowwitz, Shirato, Gummadi, and Mueter, disclose all elements of claim 11 above.
Moskowwitz discloses further the motor vehicle (¶ [0016]), see also FIG. 2A, ¶¶ [0088-0089]) comprising the electronic vehicle guidance system (¶¶ [0068-0071]), (¶ [0122]).
Regarding claim 13, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 1 above.
Moskowwitz discloses further computer program comprising instructions (¶¶ [0012], 0408) executed by a road marking detection system (¶ [0029], see also FIG. 5C, ¶¶ [0054-0060], see also FIGS. 24A-24F).
Regarding claim 14, the combination of Moskowwitz, Otto, Shirato, Gummadi, and Mueter, disclose all elements of claim 13 above.
Moskowwitz discloses further computer readable storage medium storing a computer program (¶ [0012]).
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 Terry Buse whose telephone number is (313)446-6647. The examiner can normally be reached Monday - Friday 8-5 PM EST.
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/TERRY C BUSE/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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