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 .
Response to Amendment
The amendment filed on 08/12/2025 has been entered. Claims 1-8, 11 remain pending in the application. amendment to the claims overcomes the 101 rejection set on record in the non-final rejection.
Priority
Acknowledgement is made of applicants claim for foreign priority under 35 U.S.C. 119(a)-(d) and (f). The certified copy has been filed in parent application JP2023-014244 filed on 02/01/2023.
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, 5, 6, 11 are rejected under 35 U.S.C. 103 as being unpatentable by Kuramochi (US20230227025) in view of Tao (US20190278277 from IDS) and Xiang (US20220032955).
Regarding claim 1, Kuramochi teaches an abnormality detection system applied to an autonomous driving system of a target vehicle, comprising:
one or more processors ([0028], [0068], [0174] disclosing the processor); and
one or more storage devices, wherein the one or more storage devices are configured to store ([0068], [0174] disclosing the memory):
travel plan information indicating a travel plan of the target vehicle in a first section generated by the autonomous driving system ([0084] disclosing the vehicle M travels along the center of the lane based on the central imaginary line, see fig. 1); and
reference travel information indicating a travel record of a reference vehicle different from the target vehicle in the first section ([0012], [0087] disclosing the area where the obstacle exists “section”. [0089]-[0121] disclosing the behavior of the preceding vehicles deviates from a route along the center line),
the one or more processors are configured to: calculate a deviation between the travel plan of the target vehicle and the travel record of the reference vehicle for each determination position in the first section based on the travel plan information and the reference travel information ([0012], [0087] disclosing the area where the obstacle exists. [0121] disclosing the route deviation of the preceding vehicles from the center of the lane “which is the target path of the host vehicle” exceeds a threshold); and
extract the determination position at which the deviation exceeds a threshold as an abnormal position related to an abnormality of the autonomous driving system (at least [0012], [0087] disclosing estimating an area where the obstacle exists, i.e., abnormal position. [0089]-[0121] disclosing the area where the obstacle exists is the area where the positional relationship is greater than a threshold. [0127] disclosing estimating the position of the area of the obstacle).
determine a presence or an absence of the abnormality of the autonomous driving system in real time based on vehicle information, the vehicle information including object information, vehicle state information, and vehicle position information ([0089]-[0121] disclosing the determination of the presence of the abnormality in the recognition of the autonomous vehicle, i.e., the abnormality in the autonomous vehicle based on the behavior of other vehicles including the brake lamps of other vehicles “vehicle state information”, a position change amount of the other vehicles “vehicle position information” and based on );
acquire the travel plan of the target vehicle from the autonomous driving system in real time ([0012] disclosing planning an optimal route in real time based on detected objects);
instruct the autonomous driving system to perform a fail-safe operation when the abnormal position is extracted in the first section ([0089]-[0125] disclosing the correction of the path based on the assumption that there is an object that cannot be detected by the autonomous vehicle, which is interpreted as fail safe operation to avoid an accident but based on failed detection of the object),
cause the autonomous driving system to execute autonomous driving control for controlling autonomous driving of the target vehicle based on sensor detection information detected by one or more sensors mounted on the target vehicle (Kuramochi [0064] disclosing the autonomous control of the vehicle based on sensors, [0077]-[0079] disclosing autonomous control based on camera data. [0084] disclosing based on the sensors and the imaginary line to control the vehicle to drive autonomously).
Kuramochi does not teach the object information. the fail-safe operation causing the target vehicle to decelerate and stop, or cause the target vehicle to perform evacuation traveling to a predetermined position including a road shoulder; and store log data related to the autonomous driving control of the target vehicle in a storage target section including the abnormal position in the one or more storage devices, and train an autonomous driving machine learning model using the log data.
Tao teaches the vehicle information including object information ([0059] disclosing the vehicle detects an obstacle that does not exist, thus the position of the wrongly detected object is the object information).
and store log data related to the autonomous driving control of the target vehicle in a storage target section including the abnormal position in the one or more storage devices, and train an autonomous driving machine learning model using the log data ([0035]-[0036] disclosing training a machine learning based on perception information of the surrounding to determine behavior of surrounding vehicles, the perception information includes the object information, and the perception information of other vehicles. [0059] further disclosing the abnormal position is the position of the obstacle perceived wrongly, thus it is interpreted that the stored data used to train a machine learning algorithm includes the abnormal position).
Since Kuramochi teaches the detection of an abnormal position based on abnormal perception due to obstruction of sensors to correct a path, Tao further teaches a wrong perception of objects based on object detected information to correct a path, thus it would have been obvious to combine the teaching of Tao of object information with the teaching of Kuramochi which would yield predictable results and avoids unnecessary paths that are based on wrong detection thus improving autonomous driving and performance of the autonomous driving and verification of sensor results based on vehicle behavior. Storing the data in a machine learning is obvious to enable the vehicle to predict the actions of other vehicles based on perception and changing the path of the vehicle based on the behavior of other vehicles indicating a wrong position as taught by Tao [0035]-[0036].
Kuramochi as modified by Tao does not teach the fail-safe operation causing the target vehicle to decelerate and stop, or cause the target vehicle to perform evacuation traveling to a predetermined position including a road shoulder;
Xiang teaches the fail-safe operation causing the target vehicle to decelerate and stop, or cause the target vehicle to perform evacuation traveling to a predetermined position including a road shoulder ([0087]-[0089] disclosing when there is abnormal sensor in the detection of objects and stopping the vehicle based on the abnormality, it is interpreted that the vehicle decelerates to stop);
It would have been obvious to one of ordinary skill in the art to have modified the teaching of Kuramochi as modified by Xiang to incorporate the teaching of Xiang of the fail safe operation causing the target vehicle to decelerate and stop in order to improve driving safety and avoid driving with faulty sensors thus avoiding collisions. Kuramochi and Tao both teach abnormal detections of object or not detecting objects thus the combination of the teaching of Xiang is obvious yielding predictable results improving driving safety in case of faulty detections.
Regarding claim 3, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, wherein the travel plan of the target vehicle in the first section includes a route plan of the target vehicle in the first section, the travel record of the reference vehicle in the first section includes a route record of the reference vehicle in the first section, and the one or more processors are configured to ([0084]-[0121] disclosing a route of the other vehicle at least in a first section):
calculate a positional deviation between the route plan of the target vehicle and the route record of the reference vehicle for each determination position in the first section based on the travel plan information and the reference travel information ([0084]-[0121] disclosing the difference in positional relationship between the center lane along which the route of the host vehicle is set and the route of the preceding vehicle); and extract the determination position at which the positional deviation exceeds a first threshold as the abnormal position ([0012], [0087] disclosing determining the area and location at which the obstacle exists, wherein the obstacle exists in the area location where the difference exceeds a threshold [0084]-[0121]. [0127] disclosing extracting the position of the obstacle).
Regarding claim 5, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, wherein the reference vehicle is a peripheral vehicle recognized by a recognition sensor mounted on the target vehicle, and the reference travel information is obtained from a result of recognition by the recognition sensor (Kuramochi [0082]-[0090] disclosing detecting the peripheral vehicle by cameras and radar and detecting their routes by camera and radar on the vehicle).
Regarding claim 6, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 5, wherein the reference vehicle is a preceding vehicle traveling ahead of the target vehicle (Kuramochi [0084]-[0121] disclosing the reference vehicle is a preceding vehicle).
Claim 11 is rejected for similar reasons as claim 1, see above rejection.
Claims 2, 4 are rejected under 35 U.S.C. 103 as being unpatentable by Kuramochi (US20230227025) in view of Tao (US20190278277 from IDS) and Xiang (US20220032955) and Bartels (US9463799, from IDS).
Regarding claim 2, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, wherein the travel plan of the target vehicle in the first section includes a route plan (Kuramochi [0084] disclosing a route plan along the center of the lane throughout the route including the section in which the obstacle exists before recognizing an obstacle), and the travel record of the reference vehicle in the first section includes a route record (Kuramochi [0084]-[0121] disclosing a route of the other vehicle at least in a first section).
Kuramochi as modified by Tao and Xiang does not teach a speed plan of the target vehicle in the first section, and a speed record of the reference vehicle in the first section.
Bartels teaches a speed plan of the target vehicle in the first section, and a speed record of the reference vehicle in the first section (Kuramochi col.4 lines 1-40 disclosing the speed plan of the host vehicle and the speed record of reference vehicles in the first section which is ahead of the host vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Kuramochi as modified by Tao and Xiang to incorporate the teaching of Bartels of a speed plan of the target vehicle in the first section, and a speed record of the reference vehicle in the first section in order to activate the autonomous travelling when the speeds of the other vehicles are within a prescribed limit as taught by Bartels (col 4 lines 41-50) which improves safety by avoiding the activation of the autonomous travel when the difference in speed is great thus avoiding a collision.
Regarding claim 4, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, Kuramochi as modified by Tao and Xiang does not teach wherein the travel plan of the target vehicle in the first section includes a speed plan of the target vehicle in the first section, the travel record of the reference vehicle in the first section includes a speed record of the reference vehicle in the first section, and the one or more processors are configured to: calculate a speed deviation between the speed plan of the target vehicle and the speed record of the reference vehicle for each determination position in the first section based on the travel plan information and the reference travel information; and extract the determination position at which the speed deviation exceeds a second threshold as the abnormal position.
Bartels teaches wherein the travel plan of the target vehicle in the first section includes a speed plan of the target vehicle in the first section (Kuramochi col. 4 lines 1-40 disclosing a travel plan of the own vehicle including a target speed plan),
the travel record of the reference vehicle in the first section includes a speed record of the reference vehicle in the first section (Kuramochi col. 4 lines 1-40 disclosing the reference travel record including speed record of the other vehicles within a reference range “first section”), and
the one or more processors are configured to: calculate a speed deviation between the speed plan of the target vehicle and the speed record of the reference vehicle for each determination position in the first section based on the travel plan information and the reference travel information (Kuramochi col. 4 disclosing calculating a speed deviation between the desired speed and the speeds of the other vehicles in the range “section”);
and extract the determination position at which the speed deviation exceeds a second threshold as the abnormal position (Kuramochi col. 4 lines 1-50 disclosing the difference in speed is over a threshold, and not activating the autonomous travel at the current position, i.e., the current position where the speed deviation exceeds the threshold is interpreted as an abnormal position).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Kuramochi as modified by Tao and Xiang to incorporate the teaching of Bartels of wherein the travel plan of the target vehicle in the first section includes a speed plan of the target vehicle in the first section, the travel record of the reference vehicle in the first section includes a speed record of the reference vehicle in the first section, and the one or more processors are configured to: calculate a speed deviation between the speed plan of the target vehicle and the speed record of the reference vehicle for each determination position in the first section based on the travel plan information and the reference travel information; and extract the determination position at which the speed deviation exceeds a second threshold as the abnormal position in order to activate the autonomous travelling when the speeds of the other vehicles are within a prescribed limit as taught by Bartels (col 4 lines 41-50) which improves safety by avoiding the activation of the autonomous travel when the difference in speed is great thus avoiding a collision.
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable by Kuramochi (US20230227025) in view of Tao (US20190278277 from IDS) and Xiang (US20220032955) and Xu (US20210150892).
Regarding claim 7, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, Kuramochi as modified by Tao and Xiang does not teach wherein the reference vehicle is a vehicle recognized by an infrastructure sensor, and the reference travel information is generated based on a result of recognition by the infrastructure sensor.
Xu teaches wherein the reference vehicle is a vehicle recognized by an infrastructure sensor (at least [0068] disclosing a vehicle is recognized by an infrastructure sensor), and the reference travel information is generated based on a result of recognition by the infrastructure sensor ([0068] disclosing the projected path is towards the infrastructure based on the sensor detecting the front of the vehicle or if the sensor detects an object away from the infrastructure based on the rear of the vehicle recognized by the infrastructure sensor).
It would have been obvious to substitute the teaching of Xu with the teaching of Kuramochi as modified by Tao and Xiang, both Xu and Kuramochi are determining a position and route travel of a vehicle, one of ordinary skill in the art would realize the substitution in order to detect a position with the aid of the infrastructure when sensors of the vehicle fail which would yield to predictable results.
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable by Kuramochi (US20230227025) in view of Tao (US20190278277 from IDS) and Xiang (US20220032955) and Kinoshita (US20190276049).
Regarding claim 8, Kuramochi as modified by Tao and Xiang teaches the abnormality detection system according to claim 1, wherein the one or more processors acquire the travel plan of the target vehicle from the autonomous driving system in real time, and the one or more processors feedback a notification of abnormality detection to the autonomous driving system when the abnormal position is extracted in the first section (Kuramochi [0084]-[0121] disclosing the vehicle is controlled in real time along a trajectory based on the center of the lane).
Kuramochi as modified by Tao and Xiang does not teach and the one or more processors feedback a notification of abnormality detection to the autonomous driving system when the abnormal position is extracted in the first section. However, Kuramochi teaches controlling of the vehicle based on a feedback when the abnormal position is extracted in the first section (abstract, disclosing the control of the vehicle to travel to avoid the area of the obstacle).
Kinoshita teaches and the one or more processors feedback a notification of abnormality detection to the autonomous driving system ([0008], [0040]-[0054] disclosing based on the difference between the route of the vehicle and preceding vehicle over a threshold to notify a driver of the abnormality when the abnormal position is extracted, see figure 4 and [0053]).
It would have been obvious to one of ordinary skill in the art to have modified he teaching of Kuramochi as modified by Tao and Xiang to incorporate the teaching of Kinoshita of and the one or more processors feedback a notification of abnormality detection to the autonomous driving system in order to notify a driver to avoid an obstacle and a steering and braking of the vehicle as taught by Kinoshita [0053]-[0054]. The combination is obvious to solve the problem of avoiding obstacles by informing a driver yielding expected results.
Response to Arguments
Applicant’s arguments filed on 08/12/2025 have been fully considered but are not fully persuasive.
With respect to applicant’s arguments regarding the 101 rejection, the rejection has been withdrawn.
With respect to applicant’s arguments regarding the 102 and 103 rejection, the arguments are moot since the rejection relies on new references necessitated by the amendment. Please see full rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods.
US20210291868 disclosing detecting a speed difference with a preceding vehicle to change gap.
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/MOHAMAD O EL SAYAH/Examiner, Art Unit 3658B