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 .
This is a Non-Final rejection on the merits of this application. Claims 1-22 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/23/2026 has been entered.
Response to Amendment and/or Arguments
Applicant’s arguments with respect to claim(s) 1, 9 and 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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.
Claim(s) 1-3, 5-11, 13-15 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Pyun et al. (US 2023/0278584 A1 hereinafter Pyun) in view of Ucar et al. (US 2021/0074154 A1 hereinafter Ucar).
Regarding Claim 1, Pyun teaches A vehicle system (see at least Fig. 1) comprising:
one or more processors;(see at least Fig. 1) and
memory storing instructions that, when executed by at least one of the one or more processors (see at least Fig. 1), causes the vehicle system to:
determine whether a first neighboring vehicle is exhibiting anomalous driving behavior, wherein the driving data associated with the first neighboring vehicle influences the vehicle system; (see at least Fig. 1-6 [0034-0130]: The processor 130 may monitor one or more surrounding vehicles around the driving vehicle using the sensing device 230 . The processor 130 may determine whether there is a target vehicle (e.g., a vehicle of interest) which abnormally travels (e.g., exhibits an abnormal and/or unsafe driving pattern) among the monitored surrounding vehicles. The processor 130 may select a vehicle that potentially may threaten the safety of the driving vehicle and/or a driving vehicle user as a target vehicle.) and
in response to a determination that the first neighboring vehicle is exhibiting anomalous driving behavior: selectively enabling driver assistance operations of the vehicle system; (see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route). The processor 130 may control the driving vehicle to accelerate and/or decelerate or change a driving lane without changing the driving route. By deploying the primary response logic, distance between the driving vehicle and the target vehicle increases via the primary response logic, the driving vehicle may ensure a safe space. When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route.) and
Pyun further teaches a server that manages driving of one or more of the vehicle in an autonomous driving system and may receive and process data necessary for driving of the vehicle and providing the processed data to the plurality of vehicles in the environment (see at least Fig. 1-6 [0034-0130])
Pyun does not explicitly teach initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles.
Ucar is directed to system and method for managing an anomaly and anomaly-affected entities, Ucar teaches initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles. (see at least Fig. 1-4 [0048-0125]: The anomaly manager 206 (the anomaly managing system 141 may be installed in a vehicle (e.g., a leader vehicle of a vehicle platoon, a leader vehicle of a vehicular micro cloud) receives anomaly data describes an occurrence of anomaly in a roadway environment. The anomaly manager 206 generates a first control strategy for first anomaly-affected entities (i.e. other surrounding vehicles) present within a first sub-region. The first control strategy instructs the first anomaly-affected entities in the first sub-region to change their lanes immediately. The anomaly manager 206 generates a second control strategy for second anomaly-affected entities present within a second sub-region. The second sub-region is following the first sub-region and associated with a second anomaly severity index having an intermediate impact-severity level. The second control strategy provides speed advisory to the second anomaly-affected entities in the second sub-region (e.g., advising the second anomaly-affected entities to change their speeds). The anomaly manager 206 generates a third control strategy for third anomaly-affected entities present within a third sub-region. The third sub-region is following the second sub-region and associated with a third anomaly severity index having a moderate impact-severity level. The third control strategy may reroute the third anomaly-affected entities. For each sub-region from the set of sub-regions, the anomaly manager 206 instructs the one or more anomaly-affected entities in the sub-region to execute the corresponding control strategy. For example, the anomaly manager 206 may send strategy data describing the corresponding control strategy to the one or more anomaly-affected entities via a V2X communication. A receipt of the strategy data at the one or more anomaly-affected entities may cause the one or more anomaly-affected entities to carry out the corresponding control strategy. For example, a receipt of the strategy data at the vehicle 110 can modify an operation of an Advanced Driver Assistance System (ADAS system) of the vehicle 110 so that the ADAS system operates the vehicle 110 based on the control strategy.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Pyun’s autonomous vehicle control apparatus and method to incorporate the technique of initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles as taught by Ucar with reasonable expectation of success to effectively manage an anomaly and anomaly-affected entities in a driving environment by dynamically controlling the platoon of anomaly-affected entities in order to avoid a potential hazardous situation and mitigate a risk accordingly.
Regarding Claim 9, Pyun teaches A vehicle system (see at least Fig. 1) comprising:
one or more processors (see at least Fig. 1); and
memory storing instructions that, when executed by at least one of the one or more processors (see at least Fig. 1), causes the vehicle system to:
determine whether a first neighboring vehicle is exhibiting anomalous driving behavior, wherein the driving data associated with the first neighboring vehicle influences the vehicle system; (see at least Fig. 1-6 [0034-0130]: The processor 130 may monitor one or more surrounding vehicles around the driving vehicle using the sensing device 230 . The processor 130 may determine whether there is a target vehicle (e.g., a vehicle of interest) which abnormally travels (e.g., exhibits an abnormal and/or unsafe driving pattern) among the monitored surrounding vehicles. The processor 130 may select a vehicle that potentially may threaten the safety of the driving vehicle and/or a driving vehicle user as a target vehicle.) and
in response to a determination that the first neighboring vehicle is exhibiting anomalous driving behavior: selectively adjusting a default operation of driver assistance operations of the vehicle system; (see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route). The processor 130 may control the driving vehicle to accelerate and/or decelerate or change a driving lane without changing the driving route. By deploying the primary response logic, distance between the driving vehicle and the target vehicle increases via the primary response logic, the driving vehicle may ensure a safe space. When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route.) and
Pyun further teaches a server that manages driving of one or more of the vehicle in an autonomous driving system and may receive and process data necessary for driving of the vehicle and providing the processed data to the plurality of vehicles in the environment (see at least Fig. 1-6 [0034-0130])
Pyun does not explicitly teach initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles.
Ucar is directed to system and method for managing an anomaly and anomaly-affected entities, Ucar teaches initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles. (see at least Fig. 1-4 [0048-0125]: The anomaly manager 206 (the anomaly managing system 141 may be installed in a vehicle (e.g., a leader vehicle of a vehicle platoon, a leader vehicle of a vehicular micro cloud) receives anomaly data describes an occurrence of anomaly in a roadway environment. The anomaly manager 206 generates a first control strategy for first anomaly-affected entities (i.e. other surrounding vehicles) present within a first sub-region. The first control strategy instructs the first anomaly-affected entities in the first sub-region to change their lanes immediately. The anomaly manager 206 generates a second control strategy for second anomaly-affected entities present within a second sub-region. The second sub-region is following the first sub-region and associated with a second anomaly severity index having an intermediate impact-severity level. The second control strategy provides speed advisory to the second anomaly-affected entities in the second sub-region (e.g., advising the second anomaly-affected entities to change their speeds). The anomaly manager 206 generates a third control strategy for third anomaly-affected entities present within a third sub-region. The third sub-region is following the second sub-region and associated with a third anomaly severity index having a moderate impact-severity level. The third control strategy may reroute the third anomaly-affected entities. For each sub-region from the set of sub-regions, the anomaly manager 206 instructs the one or more anomaly-affected entities in the sub-region to execute the corresponding control strategy. For example, the anomaly manager 206 may send strategy data describing the corresponding control strategy to the one or more anomaly-affected entities via a V2X communication. A receipt of the strategy data at the one or more anomaly-affected entities may cause the one or more anomaly-affected entities to carry out the corresponding control strategy. For example, a receipt of the strategy data at the vehicle 110 can modify an operation of an Advanced Driver Assistance System (ADAS system) of the vehicle 110 so that the ADAS system operates the vehicle 110 based on the control strategy.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Pyun’s autonomous vehicle control apparatus and method to incorporate the technique of initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles as taught by Ucar with reasonable expectation of success to effectively manage an anomaly and anomaly-affected entities in a driving environment by dynamically controlling the platoon of anomaly-affected entities in order to avoid a potential hazardous situation and mitigate a risk accordingly.
Regarding Claim 2 (similarly claim 10), the combination of Pyun in view of Ucar teaches The vehicle system of claim 1 (similarly claim 9), wherein the instructions that, when executed by the one or more processors, further
Pyun further teaches cause the vehicle system to perform navigation maneuvers consistent with advanced driver assistance features. (see at least Fig. 1-6 [0034-0130]: When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route. Even while the driving vehicle is traveling along the second driving route, the processor 130 may determine whether abnormal driving pattern of the target vehicle continues. When it is determined that the abnormal driving pattern of the target vehicle continues, the processor 130 may control a navigation device to change the destination of the driving vehicle to a nearby safe location (e.g., a police station, a public parking lot, a town center, etc.).)
Regarding Claim 3 (similarly claim 11), the combination of Pyun in view of Ucar teaches The vehicle system of claim 1 (similarly claim 9), wherein the instructions that, when executed by the one or more processors, further
Pyun further teaches cause the vehicle system to determine whether the associated driving data of the first neighboring vehicle is suggestive of anomalous driving behavior. (see at least Fig. 1-6 [0034-0130]: The processor 130 may monitor one or more surrounding vehicles around the driving vehicle using the sensing device 230 . The processor 130 may determine whether there is a target vehicle (e.g., a vehicle of interest) which abnormally travels (e.g., exhibits an abnormal and/or unsafe driving pattern) among the monitored surrounding vehicles. The host vehicle may use a sensing device and may determine whether there is a target vehicle that exhibits an abnormal driving pattern and/or behavior (e.g., an unusual driving pattern and/or behavior, an unsafe driving pattern and/or behavior, an aggressive driving pattern and/or behavior, a reckless driving pattern and/or behavior, etc.) among the monitored surrounding vehicles. Determination of the abnormal driving pattern may be accomplished by comparing the driving pattern of the target vehicle with other driving patterns known to be abnormal, unusual, unsafe, aggressive, and/or reckless, such as speeding, swerving, drifting out of its driving lane, driving at an excessively low speed, etc.)
Regarding Claim 5 (similarly claim 13), the combination of Pyun in view of Ucar teaches The vehicle system of claim 3 (similarly claim 11),
Pyun further teaches determining whether the associated driving data of the first neighboring vehicle is suggestive of anomalous driving comprises comparing the associated driving data with threshold data, which when exceeded identifies that the first neighboring vehicle is exhibiting anomalous driving behavior. (see at least Fig. 1-6 [0034-0130]: The unique (e.g., unusual) driving pattern of a surrounding vehicle may be characterized by one or more conditions including driving on an identical route as the driving vehicle for an extended period of time, a number of times the surrounding vehicle drifts out (e.g., veers out, swerves out, etc.) of a driving lane (e.g., drifts into an adjacent lane), an amount of time driven by the surrounding vehicle while drifting out (e.g., veered out, swerved out, etc.) of the driving lane (e.g., drifted into an adjacent lane), a speed of the surrounding vehicle that is less than the road speed limit, or any combination thereof. For example, the unique driving pattern may include one or more patterns (e.g., at least one of first to third patterns) described in Table 2. The threatening driving pattern may be characterized by one or more conditions including at least one of a distance between the surrounding vehicle and the driving vehicle, a number of sudden (e.g., having a rate of deceleration greater than a threshold rate) stops of the surrounding vehicle in front of the driving vehicle, a number of lane changes of the surrounding vehicle, a speed of the surrounding vehicle that is greater than the road speed limit, a number of instances of rapid (e.g., exceeding a threshold rate) acceleration or rapid deceleration, or any combination thereof.)
Regarding Claim 6 (similarly claim 14), the combination of Pyun in view of Ucar teaches The vehicle system of claim 5 (similarly claim 13),
Pyun further teaches wherein the associated driving data comprises one or more movement patterns of the first neighboring vehicle. (see at least Fig. 1-6 [0034-0130]: The unique (e.g., unusual) driving pattern of a surrounding vehicle may be characterized by one or more conditions including driving on an identical route as the driving vehicle for an extended period of time, a number of times the surrounding vehicle drifts out (e.g., veers out, swerves out, etc.) of a driving lane (e.g., drifts into an adjacent lane), an amount of time driven by the surrounding vehicle while drifting out (e.g., veered out, swerved out, etc.) of the driving lane (e.g., drifted into an adjacent lane), a speed of the surrounding vehicle that is less than the road speed limit, or any combination thereof. For example, the unique driving pattern may include one or more patterns (e.g., at least one of first to third patterns) described in Table 2. The threatening driving pattern may be characterized by one or more conditions including at least one of a distance between the surrounding vehicle and the driving vehicle, a number of sudden (e.g., having a rate of deceleration greater than a threshold rate) stops of the surrounding vehicle in front of the driving vehicle, a number of lane changes of the surrounding vehicle, a speed of the surrounding vehicle that is greater than the road speed limit, a number of instances of rapid (e.g., exceeding a threshold rate) acceleration or rapid deceleration, or any combination thereof.)
Regarding Claim 7, the combination of Pyun in view of Ucar teaches The vehicle system of claim 1, wherein the instructions that, when executed by the one or more processors, further causes the vehicle system to
Pyun further teaches selectively adjust a default operation of the driver assistance operations of the vehicle system. (see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route).)
Regarding Claim 8 (similarly claims 15 and 21), the combination of Pyun in view of Ucar teaches The vehicle system of claim 7 (similarly claim 9),
Pyun further teaches wherein selective adjustment of the default operation comprises generating and offsetting parameters used to effectuate a resulting operation that counters the exhibited anomalous driving behavior.(see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route). The processor 130 may control the driving vehicle to accelerate and/or decelerate or change a driving lane without changing the driving route. By deploying the primary response logic, distance between the driving vehicle and the target vehicle increases via the primary response logic, the driving vehicle may ensure a safe space. When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route.)
Claim(s) 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Pyun in view of Ucar and Fan et al. (US 2021/0027630 A1 hereinafter Fan).
Regarding claim 4 (similarly claim 12), the combination of Pyun in view of Ucar teaches The vehicle system of claim 3 (similarly claim 11),
the combination of Pyun in view of Ucar does not explicitly wherein the determining whether the associated driving data of the first neighboring vehicle s suggestive of anomalous driving is based on a machine learning model trained to perceive anomalous driving behavior based on input data comprising the first neighboring vehicle driving data.
Fan is directed to alarm system and method for assisting a driver in judging whether a vehicle neighbor is driving dangerously, Fan teaches wherein the determining whether the associated driving data of the first neighboring vehicle s suggestive of anomalous driving is based on a machine learning model trained to perceive anomalous driving behavior based on input data comprising the first neighboring vehicle driving data. (see at least Fig. 7-10 [0038-0059]: the processor can determine the dangerous level of the vehicle in front (i.e. neighboring vehicle) according to the driving trajectory matrix of the driving trajectory of the vehicle in front and neural network.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Pyun and Ucar to incorporate the technique of determining anomalous driving behavior of a neighboring vehicle using a neighboring vehicle’s driving trajectory and machine learning model as taught by Fan with reasonable expectation of success to provide a driving alarm system for assisting a driver in judging whether a vehicle in front or on one side is driven dangerously or has a dangerous driving record (Fan [0004]) and doing so would improve vehicle operation safety.
Claim(s) 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pyun in view of Gordon et al. (US 2017/0106876 A1 hereinafter Gordon) and Ucar.
Regarding Claim 16, Pyun teaches A vehicle (see at least Fig. 1) comprising:
one or more processors;(see at least Fig. 1) and
memory operatively connected to at least one of the one or more processors and including computer code, that when executed, causes at least one of the one or more processors (see at least Fig. 1), to:
monitor driving data associated with a first neighboring vehicle proximate to the vehicle; determine whether the first neighboring vehicle’s driving data is indicative of anomalous driving behavior, (see at least Fig. 1-6 [0034-0130]: The processor 130 may monitor one or more surrounding vehicles around the driving vehicle using the sensing device 230 . The processor 130 may determine whether there is a target vehicle (e.g., a vehicle of interest) which abnormally travels (e.g., exhibits an abnormal and/or unsafe driving pattern) among the monitored surrounding vehicles. The processor 130 may select a vehicle that potentially may threaten the safety of the driving vehicle and/or a driving vehicle user as a target vehicle.)
determine whether a driver assistance system of the vehicle is enabled; (see at least Fig. 1-6 [0034-0130]: In S 101 , the processor 130 may monitor surrounding vehicles around a vehicle (e.g., a driving vehicle), whose autonomous driving mode has been activated.)
when the driver assistance system is enabled, adjust operation of the driver assistance system based on the determination regarding whether driving data associated with the first neighboring vehicle is indicative of anomalous driving behavior; and (see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route). The processor 130 may control the driving vehicle to accelerate and/or decelerate or change a driving lane without changing the driving route. By deploying the primary response logic, distance between the driving vehicle and the target vehicle increases via the primary response logic, the driving vehicle may ensure a safe space. When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route.) and
Pyun further teaches a server that manages driving of one or more of the vehicle in an autonomous driving system and may receive and process data necessary for driving of the vehicle and providing the processed data to the plurality of vehicles in the environment (see at least Fig. 1-6 [0034-0130])
Pyun does not explicitly teach when the driver assistance system is not enabled, selectively enable the driver assistance based on a determination regarding whether driving data associated with the first neighboring vehicle is indicative of anomalous driving behavior; and
initiate one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles.
Gordon is directed to system and method for controlling driving modes of self-driving vehicles, Gordon teaches when the driver assistance system is not enabled, selectively enable the driver assistance based on a determination regarding whether driving data associated with the first neighboring vehicle is indicative of anomalous driving behavior; and (see at least Fig. 5-6 [0038, 0085-0086]: the behavior of the other vehicles on the road around the car (further) determines whether or not the SDV is placed into manual or autonomous mode. For example, sensors along the roadway and/or within a first SDV may detect that nearby SDVs are speeding, weaving in and out of traffic, etc. Since the SDV on-board computer is better at handling such conditions, the SDV on-board computer will automatically take over control of the SDV, thus placing the SDV in autonomous mode.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Pyun’s autonomous vehicle control apparatus and method to incorporate the technique of selectively enabling the driver assistance system based on a determination regarding whether driving data associated with the neighboring vehicle is indicative of anomalous driving behavior when the driver assistance system is not enabled as taught by Gordon with reasonable expectation of success to ensure the vehicle can properly prevent potential accidents and improve roadway safety.
The combination of Pyun in view of Gordon does not explicitly teach Pyun does not explicitly teach initiate one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles.
Ucar is directed to system and method for managing an anomaly and anomaly-affected entities, Ucar teaches initiate one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles. (see at least Fig. 1-4 [0048-0125]: The anomaly manager 206 (the anomaly managing system 141 may be installed in a vehicle (e.g., a leader vehicle of a vehicle platoon, a leader vehicle of a vehicular micro cloud) receives anomaly data describes an occurrence of anomaly in a roadway environment. The anomaly manager 206 generates a first control strategy for first anomaly-affected entities (i.e. other surrounding vehicles) present within a first sub-region. The first control strategy instructs the first anomaly-affected entities in the first sub-region to change their lanes immediately. The anomaly manager 206 generates a second control strategy for second anomaly-affected entities present within a second sub-region. The second sub-region is following the first sub-region and associated with a second anomaly severity index having an intermediate impact-severity level. The second control strategy provides speed advisory to the second anomaly-affected entities in the second sub-region (e.g., advising the second anomaly-affected entities to change their speeds). The anomaly manager 206 generates a third control strategy for third anomaly-affected entities present within a third sub-region. The third sub-region is following the second sub-region and associated with a third anomaly severity index having a moderate impact-severity level. The third control strategy may reroute the third anomaly-affected entities. For each sub-region from the set of sub-regions, the anomaly manager 206 instructs the one or more anomaly-affected entities in the sub-region to execute the corresponding control strategy. For example, the anomaly manager 206 may send strategy data describing the corresponding control strategy to the one or more anomaly-affected entities via a V2X communication. A receipt of the strategy data at the one or more anomaly-affected entities may cause the one or more anomaly-affected entities to carry out the corresponding control strategy. For example, a receipt of the strategy data at the vehicle 110 can modify an operation of an Advanced Driver Assistance System (ADAS system) of the vehicle 110 so that the ADAS system operates the vehicle 110 based on the control strategy.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Pyun and Gordon to incorporate the technique of initiating one or more coordinated driver assistance operations at one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles as taught by Ucar with reasonable expectation of success to effectively manage an anomaly and anomaly-affected entities in a driving environment by dynamically controlling the platoon of anomaly-affected entities in order to avoid a potential hazardous situation and mitigate a risk accordingly.
Regarding claim 18, the combination of Pyun in view of Gordon and Ucar teaches The vehicle of claim 16, wherein the computer code causing at least one of the one or more processors to
Pyun further teaches determine whether the first neighboring vehicle’s driving data is indicative of anomalous driving behavior further causes at least one or more processors to compare the associated driving data with threshold data, which when exceeded identifies that the first neighboring vehicle is exhibiting anomalous driving behavior. (see at least Fig. 1-6 [0034-0130]: The unique (e.g., unusual) driving pattern of a surrounding vehicle may be characterized by one or more conditions including driving on an identical route as the driving vehicle for an extended period of time, a number of times the surrounding vehicle drifts out (e.g., veers out, swerves out, etc.) of a driving lane (e.g., drifts into an adjacent lane), an amount of time driven by the surrounding vehicle while drifting out (e.g., veered out, swerved out, etc.) of the driving lane (e.g., drifted into an adjacent lane), a speed of the surrounding vehicle that is less than the road speed limit, or any combination thereof. For example, the unique driving pattern may include one or more patterns (e.g., at least one of first to third patterns) described in Table 2. The threatening driving pattern may be characterized by one or more conditions including at least one of a distance between the surrounding vehicle and the driving vehicle, a number of sudden (e.g., having a rate of deceleration greater than a threshold rate) stops of the surrounding vehicle in front of the driving vehicle, a number of lane changes of the surrounding vehicle, a speed of the surrounding vehicle that is greater than the road speed limit, a number of instances of rapid (e.g., exceeding a threshold rate) acceleration or rapid deceleration, or any combination thereof.)
Regarding Claim 19, the combination of Pyun in view of Gordon and Ucar teaches The vehicle of claim 16,
Pyun further teaches wherein the driving data associated with the first neighboring vehicle comprises one or more movement patterns of the first neighboring vehicle. (see at least Fig. 1-6 [0034-0130]: The unique (e.g., unusual) driving pattern of a surrounding vehicle may be characterized by one or more conditions including driving on an identical route as the driving vehicle for an extended period of time, a number of times the surrounding vehicle drifts out (e.g., veers out, swerves out, etc.) of a driving lane (e.g., drifts into an adjacent lane), an amount of time driven by the surrounding vehicle while drifting out (e.g., veered out, swerved out, etc.) of the driving lane (e.g., drifted into an adjacent lane), a speed of the surrounding vehicle that is less than the road speed limit, or any combination thereof. For example, the unique driving pattern may include one or more patterns (e.g., at least one of first to third patterns) described in Table 2. The threatening driving pattern may be characterized by one or more conditions including at least one of a distance between the surrounding vehicle and the driving vehicle, a number of sudden (e.g., having a rate of deceleration greater than a threshold rate) stops of the surrounding vehicle in front of the driving vehicle, a number of lane changes of the surrounding vehicle, a speed of the surrounding vehicle that is greater than the road speed limit, a number of instances of rapid (e.g., exceeding a threshold rate) acceleration or rapid deceleration, or any combination thereof.)
Regarding Claim 20, the combination of Pyun in view of Gordon and Ucar teaches The vehicle of claim 16, wherein the computer code causing at least one of the one or more processors to
Pyun further teaches adjust operation of the driver assistance system comprises offsetting parameters used to effectuate a resulting operation of the driver assistance system that counters exhibited anomalous driving behavior.(see at least Fig. 1-6 [0034-0130]: The processor 130 may enter a safety mode (e.g., a defensive driving mode) to execute response logic for the target vehicle and may control the driving vehicle. To avoid (e.g., keep the distance at a predetermined minimum distance or more) the selected target vehicle, the processor 130 may execute first avoidance logic (also referred to as primary response logic or first evasive logic) of controlling the driving vehicle to change a driving lane in a driving route (or a lane of the driving route). The processor 130 may control the driving vehicle to accelerate and/or decelerate or change a driving lane without changing the driving route. By deploying the primary response logic, distance between the driving vehicle and the target vehicle increases via the primary response logic, the driving vehicle may ensure a safe space. When abnormal driving of the target vehicle continues over a few minutes or several tens of minutes, after executing the primary response logic, the processor 130 may execute second avoidance logic (or secondary response logic) of resetting the driving route to a destination of the driving vehicle. The processor 130 may change the driving route to a second driving route, at least a portion of which is different from the initially set first driving route.)
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Pyun in view of Gordon, Ucar and Fan.
Regarding claim 17, the combination of Pyun in view of Gordon and Ucar teaches The vehicle of claim 16, wherein the computer code causing at least one of the one or more processors to
the combination of Pyun in view of Gordon and Ucar does not explicitly determine whether the first neighboring vehicle’s driving data is indicative of anomalous driving behavior comprises a machine learning model trained to perceive anomalous driving behavior based on input data comprising the driving data associated with the first neighboring vehicle.
Fan is directed to alarm system and method for assisting a driver in judging whether a vehicle neighbor is driving dangerously, Fan teaches determine whether the first neighboring vehicle’s driving data is indicative of anomalous driving behavior comprises a machine learning model trained to perceive anomalous driving behavior based on input data comprising the driving data associated with the first neighboring vehicle. (see at least Fig. 7-10 [0038-0059]: the processor can determine the dangerous level of the vehicle in front (i.e. neighboring vehicle) according to the driving trajectory matrix of the driving trajectory of the vehicle in front and neural network.)
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Pyun, Gordon and Ucar to incorporate the technique of determining anomalous driving behavior of a neighboring vehicle using a neighboring vehicle’s driving trajectory and machine learning model as taught by Fan with reasonable expectation of success to provide a driving alarm system for assisting a driver in judging whether a vehicle in front or on one side is driven dangerously or has a dangerous driving record (Fan [0004]) and doing so would improve vehicle operation safety.
Allowable Subject Matter
Claim 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding dependent claim 22, the combination of prior arts Pyun, Ucar and Gordon taken either individually or in combination with each other or other prior art of records fail to teach or render obvious of (in particular, the underlined limitation):
wherein the instructions to selectively enable driver assistance operations of the vehicle is based on one or more offset parameters that counteract an influence of data from one or more neighboring vehicles on a default operation and wherein the instructions to initiate one or more coordinated driver assistance operations at the one or more other neighboring vehicles to adjust driver assistance operations of respective vehicle systems of the one or more other neighboring vehicles is based on the one or more offset parameters.
Conclusion
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/DANA F ARTIMEZ/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
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