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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 16 December 2025 has been entered.
Response to Arguments
Applicant's arguments filed 16 December 2025 have been fully considered. Applicant’s recent amendments to independent claims 1, 9 and 17 have required further searching and consideration by the Examiner. Upon further searching and consideration, an updated rejection is provided below.
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.
Claim(s) 1, 3-7, 9, 11-15, 17, and 19-20 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bande et al. "Banda" (US 2023/0366699 A1) in view of Gansch et al. "Gansch"(US 2022/0258765 A1) and Yang et al. "Yang" (US 2021/0004613 A1).
Regarding claim 1, Bande teaches
A system for correcting geometry in a world model see at least the abstract, comprising:
at least one processor coupled to non-transitory memory see at least FIG. 5, the at least one processor configured to:
retrieve, from a world model, expected geometric data for a road traveled by an autonomous vehicle see at least [0039] where the system 200 can obtain a road model based on an environment map 205 (e.g. HD map). The HD map containing road features (i.e. geometric data such as the expected locations of road lanes) [0022] & [0029]. Further see at least [0032] where the system 200 can be part of an autonomous driving system included in an autonomous vehicle;
receive sensor data from a plurality of sensors of the autonomous vehicle, the sensor data captured during operation of the autonomous vehicle see at least [0023] & [0038] where a plurality of sensors 204 of a vehicle are configured to output sensor data to a sensor-based map generator 206;
generate a predicted geometry for a feature of the road based on the sensor data see at least [0038] where the sensor data is used by the sensor-based map generator 206 to generate a sensor-based map 207. The sensor-based map 207 can include one or more elements defining elements or components of the environment surrounding the vehicle, such as a lane boundary, road boundary, etc.;
detect an error in the expected geometric data based on the predicted geometry of the feature see at least [0027] & [0040]-[0043] where “the map comparison engine 208 can identify one or more parameters of the environment map-based road model and the sensor map-based road model and then compare the difference in the parameters between the environment map-based road model and the sensor map-based road model. The one or more parameters can be a lateral difference or offset between map/model elements an angular difference or offset between map/model elements, and/or other parameters”;
generate a correction to the world model based on the error see at least [0026]; [0029] and [0043] where a correction may be determined for the environment map-based road model based on the difference in the parameters between the environment map-based road model and the sensor map-based road model;
modify the world model based on the correction see at least [0026], [0029] and [0053] where, for example, the correction may include shifting one or more road lane elements based on the difference;
and
navigate the autonomous vehicle based at least in part on the modified world model see at least [0052]-[0053] where the process 400 includes determining a correction for the first map based on the difference being less than a threshold difference and further comprises performing at least one navigation function using the first map (i.e., HD map) when the difference is less than the threshold difference.
Bande teaches all of the elements of the current invention as stated above except wherein the processor is configured to:
detect temporal features based on the sensor data;
incorporate the temporal features into a modified world model by:
including the temporal features in the modified world model as the autonomous vehicle is approaching the temporal features and the temporal features become proximate the autonomous vehicle; and
removing the temporal features from the modified world model as the autonomous vehicle is departing from the temporal features and the temporal features become irrelevant to navigation of the autonomous vehicle;
However, Gansch discloses that is known to provide the processor is configured to
detect temporal features based on the sensor data; and
incorporate the temporal features into a modified world model by:
including the temporal features in the modified world model as the autonomous vehicle is approaching the temporal features and the temporal features become proximate the autonomous vehicle; See at least the abstract, [0007] and [0009] where a method for modeling the surroundings of an automated vehicle is provided in which a world model is generated based on data acquired by vehicle sensors and external sensors. The world model may comprise environment conditions such as weather conditions, road conditions, light conditions and/or coordinates of surrounding objects (i.e., temporal features) which may be detected by a plurality of sensors such as camera and/or radar sensor and/or rain sensor and/or lidar sensor and/or pressure sensor and/or GPS receiver, etc. The world model may be adjusted dynamically based on the received environment information [0032].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified Bande to incorporate the teachings of Gansch and provide the processor configured to detect temporal features based on the sensor data and incorporate the temporal features into a modified world model. In doing so, this “achieves the technical advantage of it being possible to calculate a safe and stable world model, even in case of dynamically changing availabilities of information sources, or the environment information provided by them, particularly during runtime” [0019] thus making the world model more reliable and safer [0017].
Yang discloses that is known to provide the processor configured to incorporate the temporal features into a modified world model by:
removing the temporal features from the modified world model as the autonomous vehicle is departing from the temporal features and the temporal features become irrelevant to navigation of the autonomous vehicle see at least FIG. 14; steps 1405 and 1410 and [0128]-[0132] where sensor data such as camera images and/or lidar point clouds are obtained and a learning model is applied to the images and/or LIDAR point clouds in order to classify each of the detected objects into classes which may include dynamic objects (such as people, bicycles, cars, animals, etc.). The dynamic objects are considered temporal features since they are in motion and therefore are expected to exist in their respective detected locations temporarily.
Further, see at least FIG. 14; step 1435 and [0141]-[0142] where the dynamic objects may be removed from the map in order to facilitate localization when a vehicle utilizes the map. “For example, the dynamic objects may change location or simply not be present when an autonomous vehicle is in the same place at some future time and using the map.”
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified Bande in view of Gansch to incorporate the teachings of Yang and provide the processor configured to incorporate the temporal features into a modified world model by: removing the temporal features from the modified world model as the autonomous vehicle is departing from the temporal features and the temporal features become irrelevant to navigation of the autonomous vehicle. In doing so, the method is improved by helping facilitate localization of the vehicle by removing dynamic objects. “If the dynamic objects is left in the map, any localization may be comprised and/or cause errors because the objects to help facilitate localization is not present or has moved as compared to sensor data being collected by the autonomous vehicle” [0141].
Regarding claim 3, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the feature of the road comprises one or more of a shoulder of the road, a lane of the road, or an intersection of the road see at least Bande [0027] & [0031] where the feature may include a location of road lane.
Regarding claim 4, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the expected geometric data for the road comprises one or more of a location of a shoulder of the road, a location of one or more lane lines of the road, or a number of lanes of the road see at least Bande [0031] where the vehicle 102 can determine a location of lanes on the road 106 (e.g., a location of a lane marker 108) and [0029] where a correction may include shifting one or more road lane elements by an amount determined by the difference between the one or more road lane elements of the HD map and the one or more corresponding road lane elements of the sensor-based map.
Regarding claim 5, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the plurality of sensors comprises one or more of a light detection and ranging (LiDAR) sensor, a radar sensor, a camera, or an inertial measurement unit (IMU) see at least Bande [0038] where the one or more sensors 204 may include LIDAR sensors, radar sensors, inertial sensors, etc.
Regarding claim 6, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the at least one processor is further configured to transmit the correction to at least one server to correct corresponding map information see at least Bande [0053].
Regarding claim 7, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the at least one processor is further configured to detect the error responsive to a difference between the predicted geometry for the feature and expected geometric of the feature indicated in the expected geometric data satisfying a threshold see at least Bande [0029] and [0053] where a correction for the first map (i.e., HD map) is determined if the difference between the one or more road lane elements of the HD map (i.e., expected geometric of the feature) and the one or more corresponding road lane elements of the sensor-based map (i.e., predicted geometry of the feature) is less than a threshold difference.
Claims 9 and 17 have substantially similar technical features as claim 1 and are therefore rejected under the same rationale.
Claim 11 has substantially similar technical features as claim 3 and is therefore rejected under the same rationale.
Claims 12 & 20 comprise substantially similar technical features as claim 4 and are therefore rejected under the same rationale.
Claims 13 & 19 comprise substantially similar technical features as claim 5 and are therefore rejected under the same rationale.
Claim 14 has substantially similar technical features as claim 6 and is therefore rejected under the same rationale.
Claim 15 has substantially similar technical features as claim 7 and is therefore rejected under the same rationale.
Regarding claim 22, Bande in view of Gansch and Yang teaches
The system of claim 1, wherein the at least one processor is further configured to:
detect the temporal features including weather conditions see at least Gansch [0007] and [0014] where the world model may comprise environment conditions such as weather conditions (such as rain, fog, etc.) which may be detected by a plurality of sensors such as camera and/or rain sensors.
Claim(s) 2, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bande in view of Gansch and Yang as applied to claim 1 above, and further in view of Elluswamy et al. " Elluswamy " (US 2024/0304003 A1).
Regarding claim 2, Bande in view of Gansch and Yang does not explicitly disclose
The system of claim 1, wherein the at least one processor is further configured to execute an artificial intelligence model using at least a portion of the sensor data as input to generate the predicted geometry for the feature of the road.
Rather, Bande discloses the process of inputting sensor data (from one or more sensors 204) into the sensor-based map generator 206 in order to generate a sensor-based map 207. The sensor-based map 207 including one or more elements defining a lane boundary, boundary, etc.
However, Elluswamy et al. (US 2024/0304003 A1) teaches that it is known to provide:
The system of claim 1, wherein the at least one processor is further configured to execute an artificial intelligence model using at least a portion of the sensor data as input to generate the predicted geometry for the feature of the road see at least [0015] and [0031]-[0032] where a trained machine learning model is used to predict a three-dimensional representation of one or more features for autonomous driving, such as lane lines.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have modified Bande in view of Gansch and Yang to incorporate the teachings of Elluswamy and provide the system of claim 1, wherein the at least one processor is further configured to execute an artificial intelligence model using at least a portion of the sensor data as input to generate the predicted geometry for the feature of the road. In doing so, this greatly improves the accuracy of lane line detection and the detection of corresponding lanes and identified drivable paths [0015].
Claims 10 and 18 comprise substantially similar technical features as claim 2 and are therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ostafew et al. (US 2021/0148726 A1) discloses an apparatus for safety-assured remote driving for autonomous vehicles. The invention includes incorporating dynamic and static objects into a world model and planning a trajectory for an autonomous vehicle according to the world model.
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/B.R.P./02/06/2025 Examiner, Art Unit 3665
/Erin D Bishop/ Supervisory Patent Examiner, Art Unit 3665