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
In response to the amendment filed 6/4/2026; claims 1, 3 – 11 and 13 - 20 are pending; claims 2 and 12 have been cancelled.
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.
Claims 1,5-7,9,11,15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley et al. (US 2017/0123422 A1) in view of Sun et al. (US 2019/0129436 A1).
Re claims 1, 11:
Kentley teaches 1. A system configured to determine driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine, wherein the simulated driving is based on real-world operation of real-world vehicles (Kentley, Abstract, “one or more probabilistic models associated with the one or more courses of action may also be determined”; [0081], “Simulator 740 is configured to simulate operation of one or more autonomous vehicles”), the system comprising:
electronic storage configured to electronically store information (Kentley, [0053], “storage medium”); and
one or more processors configured via machine-readable instructions (Kentley, [0134]) to:
obtain output signals from at least two different sensors carried by the real-world vehicles during the real-world operation of the real-world vehicles (Kentley, fig. 3A, “SENSOR(S)”);
determine dynamic vehicle parameters of the vehicles from the real-world vehicles, wherein the dynamic vehicle parameters are determined based on the output signals, wherein the dynamic vehicle parameters are determined multiple times in an ongoing manner during the real-world operation of real-world vehicles, wherein the dynamic vehicle parameters include vehicle speed and direction of travel of multiple real-world vehicles, and wherein real-world vehicles include a first vehicle and a second vehicle (Kentley, [0067], “Perception engine 366 may be configured to determine locations of external objects based on sensor data and other data … Perception engine 366 may be able to detect and classify external objects as pedestrians, bicyclists, dogs, other vehicles, etc. … Examples of external objects likely to be labeled as dynamic include bicyclists, pedestrians, animals, other vehicles, etc. If the external object is labeled as dynamic, and further data about the external object may indicate a typical level of activity and velocity …”; fig. 10; [0086], “a number of trajectories generated by a planner Also displayed are other vehicles 1011 and dynamic objects 1013, such as pedestrians, that may cause sufficient confusion at the planner, thereby requiring teleoperation support. User interface 1010 also presents to teleoperator 1008 a current velocity 1022, a speed limit 1024”), and wherein the individual real-world vehicles include a first vehicle (Kentley, [0065], “other position-sensing data (e.g., wheel-related data, such as steering angles, angular velocity, etc.)”; fig. 3E, “Direction of Travel”; [0066], “data describing a local pose may include one or more of an x-coordinate, a y-coordinate, a z-coordinate (or any coordinate of any coordinate system, including polar or cylindrical coordinate systems, or the like), a yaw value, a roll value, a pitch value (e.g., an angle value), a rate (e.g., velocity), altitude, and the like”; [0072], “an actual environment in which the autonomous vehicle is positioned”; [0124], “captured over a duration of time”; [0168], “continuously captured data from the sensors”);
detect multiple discrete vehicle events that have occurred in the real world at particular times during the real-world operation of the multiple different real-world vehicles, wherein detection of the vehicle events is based on the dynamic vehicle parameters, wherein the vehicle events include a first vehicle event that has occurred around a first time during operation of the first vehicle (Kentley, [0065]; fig. 3E; [0066]; [0072], “an actual environment in which the autonomous vehicle is positioned”; [0124], “captured over a duration of time”; [0168], “continuously captured data from the sensors”; [0091], “This determination may optionally take into consideration other factors, including the time of day”);
obtain, from the electronic storage, a set of vehicle event scenarios that correspond to the vehicle events that have been detected, wherein individual vehicle events are associated with physical surroundings of individual real-world vehicles around the particular times the individual vehicle events occurred (Kentley, [0118], “creating a more realistic simulation of actual dynamic environments that exist in the real world.”; [0122]; [0061], ” An event may be a condition or situation affecting operation, or potentially affecting operation, of an autonomous vehicle… An event may include traffic conditions or congestion, as well as unexpected or unusual numbers or types of external objects (or tracks) that are perceived by a perception engine”; [0073], “generating trajectories by identifying objects of interest in a surrounding environment in which autonomous vehicle 430 is transiting”);
create a set of simulation scenarios that are suitable for use by the simulation engine (Kentley, [0118], “a simulator configured to simulate an autonomous vehicle in a synthetic environment… a simulator 2840 that is configured to generate a simulated environment 2803. As shown, simulator 2840 is configured to use reference data … to generate simulated geometries… Simulated surfaces 2892a and 2892b may simulate walls … ”; [0061], ” An event may be a condition or situation affecting operation, or potentially affecting operation, of an autonomous vehicle… An event may include traffic conditions or congestion, as well as unexpected or unusual numbers or types of external objects ( or tracks) that are perceived by a perception engine”; [0073], “generating trajectories by identifying objects of interest in a surrounding environment in which autonomous vehicle 430 is transiting”);
establish a communication link between the vehicle operator and the simulation engine, wherein the vehicle operator is an autonomous driving algorithm that controls operations of the simulated vehicle autonomously through the communication link (Kentley, [0122], “Simulator 2840 also includes a simulator controller 2856 configured to control the simulation to adapt the functionalities of any synthetically-generated element of simulated environment … simulator evaluator 2858 may analyze simulated vehicle commands 2880 (e.g., simulated steering angles and simulated velocities) … simulator 2840 may be used to explore the space of applicable controls and resulting trajectories so as to effect learning by self-simulation”);
run the set of simulation scenarios in the simulation engine, wherein the simulated vehicle interacts with the vehicle operator and is operated by the vehicle operator based on input received from the autonomous driving algorithm (Kentley, [0118], “a simulator configured to simulate an autonomous vehicle in a synthetic environment… a simulator 2840 that is configured to generate a simulated environment 2803. As shown, simulator 2840 is configured to use reference data … to generate simulated geometries… Simulated surfaces 2892a and 2892b may simulate walls … ”; [0061], ” An event may be a condition or situation affecting operation, or potentially affecting operation, of an autonomous vehicle… An event may include traffic conditions or congestion, as well as unexpected or unusual numbers or types of external objects ( or tracks) that are perceived by a perception engine”; [0073], “generating trajectories by identifying objects of interest in a surrounding environment in which autonomous vehicle 430 is transiting”; [0122] – [0123]);
detect, by the simulation engine during the running of the set of simulation scenarios, one or more simulated vehicle events that have occurred to the simulated vehicle as the autonomous driving algorithm controlled the operations of the simulated vehicle (Kentley, [0118]; [0122] – [0123]);
determine one or more metrics that quantify a performance of the vehicle operator in running the set of simulation scenarios, wherein the determination of the one or more metrics is based on the one or more simulated vehicle events as detected (Kentley, Abstract, “confidence levels may also be determined to form a subset of the one or more courses of action”; [0062], “data representing a subset of candidate trajectories may be received from an autonomous vehicle responsive to the detection of the event”; [0149], “generate an operational efficiency metric based on the AV systems 3602 that have been deployed as part of the AV service 3660. The operational efficiency metric may be computed based on a number of factors”; [0096], “Simulator 1440 can access map data and other data necessary for performing a simulation on the set of candidate trajectories, whereby simulator 1440 need not exhaustively reiterate simulations to confirm sufficiency”); and
generate a report based on the one or more metrics and transfer the report to one or more users (Kentley, [0062], “responsive to input from a teleoperator (e.g., a teleoperator may select at least one candidate trajectory as a guided trajectory from a group of differently-ranked candidate trajectories)”; a teleporter (a user) received a report related to a group of candidate trajectories).
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Fig. 10 in Kentley shows multiple (other) individual real-world vehicles includes dynamic parameters such as trajectory (direction) and velocity (Kentley , [0067]; fig. 10; [0086]).
Kentley does not explicitly disclose [A] system configured to determine driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine, wherein the simulated driving is based on real-world operation of multiple different real-world.
Sun teaches a system and method for real world autonomous vehicle trajectory simulation. Sun teaches 1. A system configured to determine / 11. A method for determining / driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine, wherein the simulated driving is based on real-world operation of multiple different real-world vehicles (Sun, [0014], “The real world data collection system 201 can collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions”; [0017], “trained to simulate or predict the behavior and trajectories of simulated vehicles with simulated drivers in a simulation scenario based on the processed data provided by the real world data collection system 201”), the system comprising:
electronic storage configured to electronically store information; and one or more processors configured via machine-readable instructions (Sun, [0021], “data storage device or memory”) to:
obtain output signals from at least two different sensors carried by the multiple different real-world vehicles during the real-world operation of the multiple different real-world vehicles (Sun, [0004], “system and method of various example embodiments may include various sensors to collect vehicle sensor data corresponding to driving patterns of human drivers”; [0014], “collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like”);
determine dynamic vehicle parameters of individual ones of the multiple different real-world vehicles, wherein the dynamic vehicle parameters are determined based on the output signals, wherein the dynamic vehicle parameters are determined multiple times in an ongoing manner during the real-world operation of the individual ones of the multiple different real-world vehicles, wherein the dynamic vehicle parameters include vehicle speed and direction of travel of individual ones of the multiple different real-world vehicles, and wherein individual ones of the multiple different real-world vehicles include a first vehicle and a second vehicle (Sun, [0022], “The trained prediction models 180 represent a simulated virtual world configured to be identical (as possible) to the real world where vehicles are operated by human drivers”; [0004], “system and method of various example embodiments may include various sensors to collect vehicle sensor data corresponding to driving patterns of human drivers”; [0014], “collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like”; [0017], “the actual behavior and trajectories of vehicles in a real world environment”; [0021], “driver intentions for a variety of different vehicle actions and behaviors can be stored in the memory 172”; [0022], “atypical driver behaviors, such as steering or heading control, speed or throttle control, and stopping or brake control”; [0016], “collect data corresponding to human driving behaviors”);
detect multiple discrete vehicle events that have occurred in the real world at particular times during the real-world operation of individual ones of the multiple different real-world vehicles, wherein detection of the vehicle events is based on the dynamic vehicle parameters, wherein the vehicle events include a first vehicle event that has occurred around a first time during operation of the first vehicle, and a second vehicle event that is different from the first vehicle event and has occurred around a second time during operation of the second vehicle that is different from the first time (Sun, [0014], “[0014] The real world data collection system 201 can collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like. The scenarios can be represented, for example, by an occupancy grid, a collection of vehicle states on a map, or a graphical representation, such as a top-down image of one or more areas of interest. The driver intentions can correspond to a driver's short term driving goals, such as merging to a ramp, making right turn at an intersection, making a U-turn, and the like. The driver intentions can also correspond to a set of driver or vehicle control actions to accomplish a particular short term driving goal”; [0004], “actual trajectories of vehicles driven by human drivers under a similar situation”; [0022], “the real world where vehicles are operated by human drivers”);
obtain, from the electronic storage, a set of vehicle event scenarios that correspond to the vehicle events that have been detected, wherein individual vehicle events are associated with physical surroundings of individual ones of the multiple different real-world vehicles around the particular times the individual vehicle events occurred, wherein a first vehicle event scenario is associated with a first set of real-world circumstances that is based on a first set of physical surroundings of the first vehicle around the first time, and wherein a second vehicle event scenario is associated with a second set of real-world circumstances that is based on a second set of physical surroundings of the second vehicle around the second time (Sun, [0014], “The real world data collection system 201 can collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like. The scenarios can be represented, for example, by an occupancy grid, a collection of vehicle states on a map, or a graphical representation, such as a top-down image of one or more areas of interest. The driver intentions can correspond to a driver's short term driving goals, such as merging to a ramp, making right turn at an intersection, making a U-turn, and the like. The driver intentions can also correspond to a set of driver or vehicle control actions to accomplish a particular short term driving goal”; [0004], “actual trajectories of vehicles driven by human drivers under a similar situation”; [0022], “the real world where vehicles are operated by human drivers”; Sun teaches a real world data collection system can collect multiple vehicles, multiple drivers, different scenarios (i.e., different locations, traffic patterns, environmental conditions));
create a set of simulation scenarios that are suitable for use by the simulation engine, wherein individual ones of the set of simulation scenarios correspond to individual ones of the set of vehicle event scenarios, wherein the individual ones of the set of simulation scenarios mimic real-world circumstances associated with a corresponding vehicle event scenario, such that a first simulation scenario mimics the first set of real-world circumstances associated with the first vehicle event scenario, and such that a second simulation scenario mimics the second set of real-world circumstances associated with the second vehicle event scenario, wherein the simulated vehicle in the first simulation scenario is based on the first vehicle, and wherein the simulated vehicle in the second simulation scenario is based on the second vehicle (Sun, [0016], “the autonomous vehicle trajectory simulation system 202 are illustrated. The autonomous vehicle trajectory simulation system 202 can use the machine learning training provided by the real world data collection system 201 in the training phase and the real-world vehicle/object scenarios or vicinal scenes generated by the vicinal scene data generator module 173 in a simulation phase to create corresponding simulations of vehicle or object trajectories in a prediction model”; [0015], “neural networks, or other machine learning systems, can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase. The training data provided by the real world data collection system 201 can be used to build and/or train the generative prediction model”);
establish a communication link between the vehicle operator and the simulation engine, wherein the vehicle operator is an autonomous driving algorithm that controls operations of the simulated vehicle autonomously through the communication link (Sun, [0019]; [0022], “the simulation produced by the autonomous vehicle trajectory simulation system 202 can be indirectly useful for configuring the control systems in autonomous vehicles”);
run the set of simulation scenarios in the simulation engine, wherein the simulated vehicle interacts with the vehicle operator and is operated by the vehicle operator based on input received from the autonomous driving algorithm (Sun, [0012], “the autonomous vehicle trajectory simulation system 202 can use one or more of the trained trajectory prediction models 180 to generate simulated vehicle trajectories based on driving intentions and vicinal scenarios provided to the autonomous vehicle trajectory simulation system 202 and based on the training the trajectory prediction models 180 receive from the data collection system 201”; [0016], “a simulation phase to create corresponding simulations of vehicle or object trajectories in a prediction model”; [0025], “performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation”);
detect, by the simulation engine during the running of the set of simulation scenarios, one or more simulated vehicle events that have occurred to the simulated vehicle as the autonomous driving algorithm controlled the operations of the simulated vehicle (Sun, [0019]; [0022], “the simulation produced by the autonomous vehicle trajectory simulation system 202 can be indirectly useful for configuring the control systems in autonomous vehicles”);
determine one or more metrics that quantify a performance of the vehicle operator in running the set of simulation scenarios, wherein the determination of the one or more metrics is based on the one or more simulated vehicle events as detected (Sun, [0022], “For each of the vehicle trajectories in each of the generated trajectory distributions, a confidence value, likelihood rating, or probability value can also be computed by the prediction models 180 to specify a degree of likelihood or probability that the particular simulated vehicle will actually traverse the corresponding trajectory of the distribution”; [0024], “corresponding likelihood ratings or probability values for each of a plurality of simulated vehicles”; [0024]).
Therefore, in view of Sun, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system / method described in Kentley, by providing a real world data collection system for multiple vehicles / drivers as taught by Sun, since the real world data collection system can collect actual trajectories of vehicles and corresponding ground truth data under different scenarios and different driver intentions. The different scenarios can correspond to different locations, different traffic patterns, different environmental conditions, and the like. The scenarios can be represented, for example, by an occupancy grid, a collection of vehicle states on a map, or a graphical representation, such as a top-down image of one or more areas of interest. The driver intentions can correspond to a driver's short term driving goals, such as merging to a ramp, making right turn at an intersection, making a U-turn, and the like (Sun, [0014]). The simulated vehicle and/or driver intentions can also correspond to a set of driver or vehicle control actions to accomplish a particular short term driving goal. The simulated vehicle and/or driver intentions for a variety of different vehicle actions and behaviors (Sun, [0021]). The training data provided by the real world data collection system can be used to build and/or train the generative prediction model that can predict vehicles' future trajectories given their driving intentions and vicinal scenarios (Sun, [0015])).
11. A method for determining driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine, wherein the simulated driving is based on real-world operation of multiple different real-world vehicles, the method comprising:
obtaining output signals from at least two different sensors carried by the multiple different real-world vehicles during the real-world operation of the multiple different real-world vehicles;
determining dynamic vehicle parameters of individual ones of the multiple different real-world vehicles, wherein the dynamic vehicle parameters are determined based on the output signals, wherein the dynamic vehicle parameters are determined multiple times in an ongoing manner during the real-world operation of the individual ones of the multiple different real-world vehicles, wherein the dynamic vehicle parameters include vehicle speed and direction of travel of individual ones of the multiple different real-world vehicles, and wherein individual ones of the multiple different real-world vehicles include a first vehicle and a second vehicle;
detecting multiple discrete vehicle events that have occurred in the real world at particular times during the real-world operation of individual ones of the multiple different real-world vehicles, wherein detection of the vehicle events is based on the dynamic vehicle parameters, wherein the vehicle events include a first vehicle event that has occurred around a first time during operation of the first vehicle, and a second vehicle event that is different from the first vehicle event that has occurred around a second time during operation of the second vehicle that is different from the first time;
obtaining a set of vehicle event scenarios that correspond to the vehicle events that have been detected, wherein individual vehicle events are associated with physical surroundings of individual ones of the multiple different real-world vehicles around the particular times the individual vehicle events occurred, wherein a first vehicle event scenario is associated with a first set of real-world circumstances that is based on a first set of physical surroundings of the first vehicle around the first time, and wherein a second vehicle event scenario is associated with a second set of real-world circumstances that is based on a second set of physical surroundings of the second vehicle around the second time;
creating a set of simulation scenarios that are suitable for use by the simulation engine, wherein individual ones of the set of simulation scenarios correspond to individual ones of the set of vehicle event scenarios, wherein the individual ones of the set of simulation scenarios mimic real-world circumstances associated with a corresponding vehicle event scenario, such that a first simulation scenario mimics the first set of real-world circumstances associated with the first vehicle event scenario, and such that a second simulation scenario mimics the second set of real-world circumstances associated with the second vehicle event scenario, wherein the simulated vehicle in the first simulation scenario is based on the first vehicle, and wherein the simulated vehicle in the second simulation scenario is based on the second vehicle;
establishing a communication link between the vehicle operator and the simulation engine, wherein the vehicle operator is an autonomous driving algorithm that controls operations of the simulated vehicle autonomously through the communication link;
running the set of simulation scenarios in the simulation engine, wherein the simulated vehicle interacts with the vehicle operator and is operated by the vehicle operator based on input received from the autonomous driving algorithm;
detecting, by the simulation engine during the running of the set of simulation scenarios, one or more simulated vehicle events that have occurred to the simulated vehicle as the autonomous driving algorithm controlled the operations of the simulated vehicle;
determining one or more metrics that quantify a performance of the vehicle operator in running the set of simulation scenarios, wherein the determination of the one or more metrics is based on the one or more simulated vehicle events as detected; and
generating a report based on the one or more metrics and transferring the report to one or more users (Claim 11 is rejected by Kentley and Sun; see citations above).
Re claims 5, 15:
5. The system of claim 1, wherein the dynamic vehicle parameters include a distance to an object in or near a current travelling lane of the individual real-world vehicles. 15. The method of claim 11, wherein the dynamic vehicle parameters include a distance to an object in or near a current travelling lane of the individual real-world vehicles (Kentley, [0089], “distances to external objects”).
Re claims 6, 16:
6. The system of claim 1, wherein the first set of physical surroundings of the first vehicle is based on traffic conditions around the particular time the first vehicle event occurred. 16. The method of claim 11, wherein the first set of physical surroundings of the first vehicle is based on traffic conditions around the particular time the first vehicle event occurred (Kentley, [0061]; [0091]).
Re claim 7, 17:
7. The system of claim 1, wherein the communication link provides the vehicle operator with control over the operations of the simulated vehicle. 17. The method of claim 11, wherein the communication link provides the vehicle operator with control over the operations of the simulated vehicle (Kentley, [0062], “responsive to input from a teleoperator (e.g., a teleoperator may select at least one candidate trajectory as a guided trajectory from a group of differently-ranked candidate trajectories)”; a teleporter (a user) received a report related to a group of candidate trajectories).
Re claims 9, 19:
9. The system of claim 1, wherein one of the one or more metrics is reduced responsive to an individual one of the set of simulation scenarios resulting in a simulated accident. 19. The method of claim 11, wherein one of the one or more metrics is reduced responsive to an individual one of the set of simulation scenarios resulting in a simulated accident (Kentley, [0091], “This determination may optionally take into consideration other factors, including the time of day … traffic or accident data
derived from a variety of sources”; Sun, [0024], “enable the generation of a distribution of vehicle trajectories with corresponding likelihood ratings or probability values for each of a plurality of simulated vehicles of the simulation”).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley and Sun as applied to claims 1 and 11 above, and further in view of Walther et al. (US 10,599,546 B1).
Re claims 10, 20:
Kentley does not explicitly disclose modifying one or more metrics to simulation scenarios. Walther teaches 10. The system of claim 1, wherein modification of the one or more metrics due to an individual one of the set of simulation scenarios that resulted in a simulated accident is varied based on a difficulty level of the individual one of the set of simulation scenarios. 20. The method of claim 11, wherein modification of the one or more metrics due to an individual one of the set of simulation scenarios that resulted in a simulated accident is varied based on a difficulty level of the individual one of the set of simulation scenarios (Walther, fig. 5, “Test Variations”; col. 6, lines 29 – 52, “The testing scenario can indicate one or more testing parameters of a test such as, for example: the type of geographic area represented in the testing environment ( e.g., intersection, highway, cul-de-sac, dead end, etc.), features of the geographic area (e.g., train tracks, obstructions, etc.); one or more objects within the testing environment (e.g., vehicles, bicycles, pedestrians, etc.); weather condition(s), and/or other parameters. An individual test can be a variation of the testing scenario that evaluates the one or more autonomous vehicle capabilities”; col. 7, lines 36 – 51, “The testing system can obtain data indicative of user input that identifies a testing scenario as corresponding to the test. Additionally or alternatively, the user interface can present an interactive element (e.g., text entry box, etc.) via which the user can define a new testing scenario. For example, the user can provide user input (e.g., via the user interface) indicating one or more testing parameters (e.g., four way intersection, two objects) and the testing system can generate a new testing scenario based at least in part on such user input”; changing the test parameters also changes difficulty and complexity and prong to accident). Therefore, in view of Walther, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system/method described in Kentley, by changing the parameters as taught by Walther, since the testing scenario can indicate testing parameters such as the type of geographic area represented in the testing environment, object(s) in the testing environment (e.g., vehicles, bicycles, pedestrians, etc.), weather condition(s), etc. These testing parameters can be determined based on real-world data (e.g., driving log data, sensor data, etc.) and the testing scenario can indicate one or more testing parameters of a test such as, for example: the type of geographic area represented in the testing environment ( e.g., intersection, highway, cul-de-sac, dead end, etc.), features of the geographic area (e.g., train tracks, obstructions, etc.); one or more objects within the testing environment (e.g., vehicles, bicycles, pedestrians, etc.); weather condition(s), and/or other parameters (Walther, col. 6, lines 29 – 52).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley and Sun as applied to claims 1 and 11 above, and further in view of Switkes et al. (US 2013/0041576 A1).
Re claims 3, 13:
Kentley teaches 3. The system of claim 1, wherein the at least two different sensors include a lidar. 13. The method of claim 11, wherein the at least two different sensors include a lidar (Kentley, fig. 4, 472; fig. 3A). However, Kentley does not explicitly disclose a following distance vehicle parameter.
Switkes et al. (US 20130041576 A1) teaches systems and methods for facilitating participants of vehicular convoys to closely follow one another through partial automation (Switkes, Abstract). Switkes further teaches wherein the dynamic vehicle parameters include a following distance to a particular real-world vehicle that is ahead of one of the individual real-world vehicles (Switkes, [0043], “A forward-looking RADAR or LIDAR unit 1130, which senses distance and relative speed of the vehicle in front 410”; [0039], “following distance behind the front vehicle”). Therefore, in view of Switkes, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system/method described in Kentley, by providing the following distance as taught by Switkes, in order to provide systems and methods for a Semi-Autonomous Vehicular Convoying includes the ability to follow closely together in a safe, efficient, convenient manner (Switkes, [0072]).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley and Sun as applied to claims 1 and 11 above, and further in view of Rajvanshi et al. (US 2017/0341647 A1).
Re claims 4, 14:
Kentley does not explicitly disclose a distance to a lane marking.
Rajvanshi teaches the methods and system described herein may be used to assist an automated driving system of a host vehicle (Rajvanshi, Abstract). Rajvanshi further teaches 4. The system of claim 1, wherein the dynamic vehicle parameters include a distance of another real-world vehicle to a lane marking near the first vehicle (Rajvanshi, [0023]; [0024]; [0033] – [0035]; fig. 1; Rajvanshi teaches a distance of another real-world vehicle (target vehicle) to a lane marking near the first vehicle (host vehicle); see fig. 1), and wherein the first vehicle event scenario includes a second simulated
vehicle crossing the lane marking near the first vehicle (Rajvanshi, fig. 2A – 2D; [0016], “means to
initiate or at least partially begin a lane change or lane departure from the subject vehicle's current lane”; [0044]; [0057], “when the leading target vehicle is cutting out or switching lanes (e.g., FIG. 2D), the method determines whether the host vehicle's current lane is clear”). 14. The method of claim 11, wherein the dynamic vehicle parameters include a distance of another real-world vehicle to a lane marking near the first vehicle (Rajvanshi, [0023]; [0024]; [0033] – [0035]; fig. 1; Rajvanshi teaches a distance of another real-world vehicle (target vehicle) to a lane marking near the first vehicle (host vehicle); see fig. 1), and wherein the first vehicle event scenario includes a second simulated
vehicle crossing the lane marking near the first vehicle (Rajvanshi, fig. 2A – 2D; [0016], “means to
initiate or at least partially begin a lane change or lane departure from the subject vehicle's current lane”; [0044]; [0057], “when the leading target vehicle is cutting out or switching lanes (e.g., FIG. 2D), the method determines whether the host vehicle's current lane is clear”).
Therefore, in view of Rajvanshi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system/method described in Kentley, by providing the distance to lane marking of another vehicle as taught by Rajvanshi, since one or more sensors may be used to track another real-world vehicle that is in front of the first vehicle and to determine the relative position of the another real-world vehicle with respect to the first vehicle. While this relative positional information may be useful in terms of maintaining a safe following distance, it may not be enough by itself to determine whether the first vehicle, another real-world vehicle, or both vehicles are switching lanes and how to control the first vehicle in response thereto. With sufficient information to determine which vehicle is switching lanes or "cutting out", vehicle autonomous or semi-autonomous systems, such as ACC systems, may be able to operate more favorably, thereby creating a better passenger and/or operator experience (Rajvanshi, [0003]).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kentley and Sun as applied to claims 1 and 11 above, and further in view of Takahashi (US 2005/0137756 A1).
Re claims 8, 18:
Kentley does not explicitly disclose the simulation engine is run at faster-than-real-time. Takahashi teaches a traffic flow simulator simulates a virtual traffic flow based on the detected traffic flow, and a simulation result evaluating device predicts an approaching traffic flow around the vehicle based on the simulation result (Takahashi, Abstract). Takahashi teaches 8. The system of claim 1, wherein the simulation engine is computer-implemented and configured by a set of machine-readable instructions, and wherein the set of simulation scenarios in the simulation engine is run at faster-than-real-time. 18. The method of claim 11, wherein the set of simulation scenarios in the simulation engine is run at faster-than-real-time (Takahashi, [0039]). Therefore, in view of Takahashi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system/method described in Kentley, by providing faster simulation time as taught by Takahashi, since the traffic flow simulation is made faster than real-time progress, in which an actual traffic flow realized in a minute can be calculated in about one-tenth to save time and analyze more simulated scenarios (Takahashi, [0039]).
Response to Arguments
Applicant's arguments filed 6/4/2026 have been fully considered but they are not persuasive.
Applicant argues that the Kentley (a) Kentley is non-analogous art with respect to the claimed invention, and (b) Kentley and Walther, alone or in combination, fail to teach or suggest the features of the amended claims.
The examiner strongly disagrees.
A reference qualifies as prior art for an obviousness determination when it is analogous to the claimed invention. Innovention Toys, LLC. v. MGA Ent., Inc., 637 F.3d 1314, 1321 (Fed. Cir. 2011).
Two separate tests define the scope of analogous art: (1) whether the art is from the same field of endeavor, regardless of the problem addressed, and (2) if the reference is not within the field of the inventor’s endeavor, whether the reference still is reasonably pertinent to the particular problem with which the inventor is involved.
In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004). The “field of endeavor” test asks if the structure and function of the prior art is such that it would be considered by a person of ordinary skill in the art because of similarity to the structure and function of the claimed invention as disclosed in the application. “A reference is reasonably pertinent . . . if it is one which, because of the matter with which it deals, logically would have commended itself to an inventor's attention in considering his problem.” In re Clay, 966 F.2d 656, 659 (Fed. Cir. 1992). “If a reference disclosure has the same purpose as the claimed invention, the reference relates to the same problem, and that fact supports use of that reference in an obviousness rejection.” Id.
Kentley teaches an autonomous vehicle and associated mechanical, electrical and electronic hardware, computer software and systems communications to provide an autonomous vehicle fleet as a service incorporates with a simulator for simulating various aspects of an autonomous vehicle (Kentley, Abstract; [0123]). Applicant’ s problem is concerned with determining driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine. In particular, simulation scenarios based on vehicle events are simulated, and the simulated vehicle is operated by the vehicle operator, which may be an autonomous driving algorithm. Kentley and Applicant’s inventions are in the same field of endeavor and pertinent to the particular problem with which the inventor is involved.
Applicant argues that the rejection under § 103 should still be withdrawn at least because the proposed combination of references fails to teach or suggest detecting multiple discrete vehicle events from multiple different real-world vehicles, obtaining vehicle event scenarios corresponding to those detected vehicle events, and creating simulation scenarios corresponding to those vehicle event scenarios and mimicking their respective real-world circumstances.
The newly cited reference: Sun et al. (US 2019/0129436 A1) teaches a real-world data collection system can collect multiple vehicles, multiple drivers, different scenarios (i.e., different locations, traffic patterns, environmental conditions));
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 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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/JACK YIP/Primary Examiner, Art Unit 3715
