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 Arguments
Applicant's arguments filed 07/28/2026 have been fully considered but they are not persuasive.
Applicant argues the cited references, alone or in combination, do not teach the newly amended features of independent claim 1. In particular, Applicant argues the newly amended features of switching between normal and backup modes based on whether the external physical situation perception process is sufficiently performed within a preset critical time and generating a route based on precise physical situation perception in the normal mode and generating a route guaranteeing at least a minimum safety based on raw information in the backup mode are not taught. Therefore, Applicant independent claim 1 is distinguished over the cited references and is allowable.
However, Mercep (US 20200209848) teaches transitioning a vehicle between a safety mode from a non-safety mode default mode when sensor faults are determined and controlling the vehicle to avoid particular areas and follow alternative routes based on the determined sensor faults. The route of the autonomous vehicle is identified, where before degradation or faults are found, the route is determined during a normal mode of the vehicle and generated precisely based on predicted future sensor faults and where after degradation or faults are found, the route is determined during a backup mode of the vehicle and generated precisely based on determined sensor faults and predicted future sensor faults to avoid dangerous conditions, where another sensor may take the place of the faulty sensor thereby operating in a backup mode when faults are found. This guarantees safety based on sensor fault and predicted sensor fault data during a backup mode on the basis of raw physical situation information. Further, a predicted time to sensor fault is determined, during which time sensors of autonomous vehicle collect information on surroundings of vehicles which is within a critical time set before further processing, thereby a preset critical time. Pullagurla (US 20250003764) then teaches determining whether features are determined at least at predetermined threshold accuracy within sensor information. By the combination of these two references, one of ordinary skill would have arrived at normal and backup modes transitioning based on sufficient perception information being above an accuracy threshold, where the route plan is generated precisely based on precise perception information in the normal mode and generated to guarantee safety based on raw situation information in the backup mode, which is precisely what is required by the claim. One of ordinary skill would have been motivated to make sure modification for the reasons as laid out in the rejection below.
As such, this argument is unpersuasive.
Applicant argues independent claim 10 recites similar features to independent claim 1 and is allowable for the same reasons.
This argument is unpersuasive for the same reasons as given above.
Applicant argues the dependent claims are allowable by virtue of their dependency.
This argument is unpersuasive as each independent and dependent claim has been fully rejected and for the reasons as given above.
Claim Objections
Claim 10 objected to because of the following informalities: “according to th e route plan” (emphasis added) which should read “according to the route plan” (emphasis added). Appropriate correction is required.
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 1-5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mercep et al. (US 20200209848), in view of Pullagurla et al. (US 20250003764).
In regards to claim 1, Mercep teaches a method of handling an external abnormality in an autonomous driving system, performed in a device including a memory and a processor electrically connected to the memory, the method comprising: (Fig 5, [0076] processor performs operations stored in memory.)
collecting physical situation information outside an autonomous vehicle from at least one sensor installed in the autonomous vehicle through the processor; ([0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles, [0076] as commanded by processor. This collects situation information outside autonomous vehicle using sensors.)
repeatedly performing a physical situation perception operation for a preset critical time through the processor; ([0066], [0067] a predicted time to sensor fault is determined, during which time [0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles. These sensor detections are performed repeatedly where image sensors capture image frames at a frame rate, LiDAR sensors capture light information at a capture rate, radar sensors capture radio signals at a capture rate, and the like. These operations necessarily must be performed repeatedly to accurately measure the environment, navigate the vehicle, and eventually determine sensor faults.)
selecting one of a plurality of operation modes comprising a normal mode and a backup mode, with the preset critical time, and generating a route plan for the autonomous vehicle through the processor, wherein the route plan is generated as a precise route plan based on the physical situation perception when the normal mode is selected, and generated as a safe route plan for guaranteeing at least safety based on raw physical situation information when the backup mode is selected; ([0056] vehicle may enter safety mode from non-safety default mode when sensor fault is determined from sensor information. This selects at least between the standard mode and a safety mode. [0072], [0074] vehicle may be controlled to avoid particular areas and follow alternative routes based upon determined sensor fault. [0076] operations are performed through processor. [0072], [0074] route of autonomous vehicle is identified, where before degradation or faults are found, the route is determined during a normal mode of the vehicle and generated precisely based on predicted future sensor faults and where after degradation or faults are found, the route is determined during a backup mode of the vehicle and generated precisely based on determined sensor faults and predicted future sensor faults to avoid dangerous conditions, where [0032] when sensor faults are found, another sensor may take the place of the faulty sensor thereby operating in a backup mode. This guarantees safety based on sensor fault and predicted sensor fault data during a backup mode on the basis of raw physical situation information. [0066], [0067] a predicted time to sensor fault is determined, during which time [0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles. This is within a critical time set before further processing, thereby a preset critical time.) and
controlling driving of the autonomous vehicle according to the route plan through the processor. ([0013], [0028], [0074] autonomous vehicle controlled to follow route [0076] operations performed through processor.)
Mercep does not teach:
based on whether the physical situation perception operation is sufficiently performed to reach a reference accuracy
However, Pullagurla teaches determining whether features are determined at least at predetermined threshold accuracy ([0107]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Mercep, by incorporating the teachings of Pullagurla, such that determined features are checked to be within a sufficient level of accuracy within the time to sensor fault and this determination in the selection of the mode of the vehicle.
The motivation to do so is that, as acknowledged by Pullagurla, this allows for improved modeling of the environment around the vehicle ([0002], [0003]).
In regards to claim 2, Mercep, as modified by Pullagurla, teaches the method of claim 1, wherein the collecting physical situation information comprises collecting surrounding objects, road conditions, outside the vehicle as the physical situation information from a plurality of sensors including a camera, a radar, and a LiDAR. ([0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles including image sensors which are cameras, LiDAR sensors, and radar sensors. [0024] sensors detect objects around the own vehicle. [0056] external terrain and weather conditions are detected with sensors which together form road conditions.)
Pullagurla teaches determining error by first detecting traffic signals and other objects around the own vehicle ([0127]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Mercep, as already modified by Pullagurla, by further incorporating the teachings of Pullagurla, such that as sensor faults are determined while monitoring the environment of Mercep, detected objects including at least traffic signals are also detected.
The motivation to do so is the same as acknowledged by Pullagurla in regards to claim 1.
In regards to claim 3, Mercep, as modified by Pullagurla, teaches the method of claim 2, wherein the collecting physical situation information comprises detecting an abnormal situation regarding a malfunction or data loss of at least one of the plurality of sensors, determining a replacement sensor to replace the sensor associated with the abnormal situation among the remaining sensors, and determining whether to replace the sensor on the basis of a possibility of replacing the replacement sensor. ([0026], [0032] when sensor faults are found, for example due to debris or blockages, causing data loss, another sensor may take the place of the faulty sensor thereby operating in a backup mode, which is determined based on this being a possibility.)
In regards to claim 4, Mercep, as modified by Pullagurla, teaches the method of claim 1, wherein the repeatedly performing a physical situation perception operation comprises repeatedly performing at least one of object recognition, obstacle detection, road condition analysis, and traffic signal analysis on the basis of the physical situation information. ([0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles. These operations necessarily must be performed repeatedly to accurately measure the environment, navigate the vehicle, and eventually determine sensor faults. [0024] sensors detect objects and obstacles around the own vehicle. [0056] external terrain and weather conditions are detected with sensors which together form road conditions. [0022] this collected sensor data with object and condition analysis is used to form an environmental model which is updated each time sensor data is acquired, such that the operations are performed repeatedly on the basis of detected sensor information.)
In regards to claim 5, Mercep, as modified by Pullagurla, teaches the method of claim 1, wherein the generating a route plan comprises generating a movement route from a current vehicle position to a destination and regenerating the movement route by predicting a potential risk related to the physical situation information. ([0072], [0074] route is identified for vehicle from current position to destination and route may be adjusted by adjusting driving strategy to avoid predicted risk inducing situations that the degraded vehicle would otherwise encounter, where degraded vehicle performance is due to determined sensor faults.)
In regards to claim 10, Mercep teaches a device for handling an external abnormality in an autonomous driving system, the device comprising: (Figs 1, 3, 4, 6, 7.)
a data collection module configured to collect physical situation information outside an autonomous vehicle from at least one sensor installed in the autonomous vehicle; ([0014]-[0016] sensors of autonomous vehicle’s sensor system collect information on surroundings of vehicles. This collects situation information outside autonomous vehicle using sensors. Sensor system serves as data collection module.)
a physical situation perception module configured to repeatedly perform a physical situation perception operation for a preset critical time; ([0066], [0067] a predicted time to sensor fault is determined, during which time [0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles. These sensor detections are performed repeatedly where image sensors capture image frames at a frame rate, LiDAR sensors capture light information at a capture rate, radar sensors capture radio signals at a capture rate, and the like. These operations necessarily must be performed repeatedly to accurately measure the environment, navigate the vehicle, and eventually determine sensor faults. [0026], [0027] operations are performed by sensor fusion system and driving functionality system in combination acting as physical situation perception module.)
a route planning module configured to select one of a plurality of operation modes according to a result of the physical situation perception operation comprising a normal mode and a backup mode, within the preset critical time, and generate a route plan for the autonomous vehicle, wherein the route plan is generated as a precise route plan based on precise physical situation perception when the normal mode is selected, and generated as a safe route plan for guaranteeing at least safety based on raw physical situation information when the backup mode is selected; ([0056] vehicle may enter safety mode from non-safety default mode when sensor fault is determined from sensor information. This selects at least between the standard mode and a safety mode. [0068], [0072], [0074] vehicle may be controlled to avoid particular areas and follow alternative routes based upon determined sensor fault. Operations are performed by service adjustment unit and operational adjustment unit within service adjustment unit, which together form a route planning module that plans and alters the route of the vehicle. Route of autonomous vehicle is identified, where before degradation or faults are found, the route is determined during a normal mode of the vehicle and generated precisely based on predicted future sensor faults and where after degradation or faults are found, the route is determined during a backup mode of the vehicle and generated precisely based on determined sensor faults and predicted future sensor faults to avoid dangerous conditions, where [0032] when sensor faults are found, another sensor may take the place of the faulty sensor thereby operating in a backup mode. This guarantees safety based on sensor fault and predicted sensor fault data during a backup mode on the basis of raw physical situation information. [0066], [0067] a predicted time to sensor fault is determined, during which time [0014]-[0016] sensors of autonomous vehicle collect information on surroundings of vehicles. This is within a critical time set before further processing, thereby a preset critical time.) and
a control module configured to control driving of the autonomous vehicle according to th e route plan. ([0013], [0028], [0074] autonomous vehicle controlled to follow route [0076] operations performed through processor. [0028] vehicle control system controls vehicle and acts as control module.)
Mercep does not teach:
based on whether the physical situation perception operation is sufficiently performed to reach a reference accuracy
However, Pullagurla teaches determining whether features are determined at least at predetermined threshold accuracy ([0107]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control system of Mercep, by incorporating the teachings of Pullagurla, such that determined features are checked to be within a sufficient level of accuracy within the time to sensor fault and this determination in the selection of the mode of the vehicle.
The motivation to do so is that, as acknowledged by Pullagurla, this allows for improved modeling of the environment around the vehicle ([0002], [0003]).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Mecep, in view of Pullagurla, in further view of Non-patent Literature Bai et al. “Performance optimization of autonomous driving control under end-to-end deadlines”.
In regards to claim 8, Mercep, as modified by Pullagurla, teaches the method of claim 1.
Mercep, as modified by Pullagurla, also teaches when sensor faults are found, another sensor may take the place of the faulty sensor thereby operating in a backup mode ([0032]).
Mercep, as modified by Pullagurla, does not teach: wherein the controlling driving comprises determining the critical time by calculating a maximum execution time of a physical situation perception process of initiating a backup mode among the plurality of operation modes.
However, Bai teaches determining a maximum execution time for subtasks of an autonomous driving vehicle (Page 518).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Mercep, as already modified by Pullagurla, by incorporating the teachings of Bai, such that a maximum execution time for each task of the vehicle is determined including operating a sensor to take the place of a faulty sensor operating in a backup mode.
The motivation to do so is that as acknowledged by Bai, this allows for improved autonomous control (Page 509).
In regards to claim 9, Mercep, as modified by Pullagurla and Bai, teaches the method of claim 8.
Mercep also teaches when sensor faults are found, another sensor may take the place of the faulty sensor thereby operating in a backup mode, where sensor blockage and occlusion may be a sensor fault, which are a driving situation ([0026], [0032]).
Bai teaches determining a maximum execution time for subtasks of an autonomous driving vehicle and the execution time is weighted based upon the specifics of the driving scenario (Page 518).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Mercep, as already modified by Pullagurla and Bai, by further incorporating the teachings of Bai, such that as the driving situations around the own vehicle are determined, including faults of sensors, and a backup mode is engaged, the maximum execution time of each task of the vehicle is determined and weighted based upon the specifics of the driving scenario, including engaging a backup sensor to operate in place of a faulty sensor in the backup mode.
The motivation to do so is the same as acknowledged by Bai in regards to claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Meng et al. (US 20240116520) teaches operating a vehicle backup sensor when an error mode is determined of another vehicle sensor.
Han et al. (US 20230266759) teaches switching a vehicle to operate in a safety mode when a primary sensor and backup sensor fail.
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 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.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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