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 .
DETAILED ACTION
1. The amendment filed on 01/29/2026 has been received and considered. Claims 1-3, 6-11, 14-18, and 23-28 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
2. Claims 1-3, 6-11, 14-18, and 23-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 1, 6, and 14, they recite the limitation “based at least in part on determining the interaction,”. The limitation “at least in part” is used as a coordinating conjunction to show an alternative between features which separates two distinct options indicating that a choice must be made between them. But there is no other alternative feature claimed for “at least in part” alternative limitation.
As per claim 25, it recites the limitation “the driving style type of the real-world vehicle” in line 6. There is insufficient antecedent basis for this limitation in the claim.
As per claim 26, it recites the limitation “the driving style type of the real-world vehicle” in line 7. There is insufficient antecedent basis for this limitation in the claim.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
3. Claims 1-3, 6-9, 11, 14-16, 18, and 23-28 are rejected under 35 U.S.C. 103 as being unpatentable over Nygaard (US 10,795,804 B1) in view of Johnson ("Driving Style Recognition Using a Smartphone as a Sensor Platform”).
As per Claim 1, Nygaard teaches a system comprising:
one or more processors; and one or more computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations (Abstract, Fig. 1 and the description) comprising:
receiving log data associated with operation of a real-world vehicle in a real-world driving environment, wherein the log data indicates a real-world agent trajectory of a real-world agent different from the real-world vehicle in the real-world driving environment (col. 10 lines 42-67, col. 11 lines 1-4 “The log data may also include “event” data identifying different types of events such as collisions or near collisions with other objects, planned trajectories describing a planned geometry and/or speed for a potential path of the vehicle 100, actual locations of the vehicles at different times, actual orientations/headings of the vehicle at different times, actual speeds, accelerations and decelerations of the vehicle at different times,”; col. 12 lines 12-27 “ In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”);
…
executing a driving simulation including controlling, in a simulated environment, a simulated vehicle associated with the real-world vehicle in the real-world driving environment and a simulated agent associated with the real-world agent in the real-world driving environment (col. 11 lines 43-59, “the server computing devices 410 may run various simulations. These simulations may be log based simulations that are generated from the information stored in the aforementioned log data of storage system 450. In this regard, the server computing devices 410 may access the storage system 450 in order to retrieve the log data and run a simulation. For instance, a portion of the log data corresponding to a minute in real time of an autonomous vehicle that generated the log data may be retrieved from the storage system.”; col. 12 lines 12-27 “In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”), wherein executing the driving simulation comprises:
controlling the simulated vehicle using an autonomous vehicle controller in a simulated environment (Col. 15 lines 29-54 “at block 910, a first simulation is run using log data collected by a vehicle operating in an autonomous driving mode. The first simulation is run using the software to control a first simulated vehicle, and the log data identifies at least one agent included in the first simulation.”);
controlling the simulated agent, during a first time period in the driving simulation, to follow a first agent trajectory corresponding to the real-world agent trajectory of the real-world agent (col. 3 lines 60-67, col. 4 lines 1-16 “no matter what the simulated vehicle does, the agents will always react according to the log data”);
determining an interaction in the driving simulation between the simulated vehicle and a simulated object in the simulated environment (col. 15 lines 29-54 “At block 920, during (or after) the running of the first simulation, a particular type of interaction between the first simulated vehicle and the at least one agent is determined to have occurred.”) and
based at least in part on determining the interaction, controlling the simulated agent, during a second time period in the driving simulation after the first time period, to follow a modified agent trajectory determined using a planning component different from the autonomous vehicle controller (col. 15 lines 29-54 “At block 930, in response to determining that the particular type of interaction between the first simulated vehicle and the at least one agent has occurred, a second simulation is run using the log data by replacing the at least one agent with a model agent that simulates a road user capable of responding to actions performed by simulated vehicles.”), wherein the modified agent trajectory causes the simulated agent to diverge from the real-world agent trajectory of the real- world agent in the real-world driving environment (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation"; col. 13, lines 24-28 "In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors"), and wherein the planning component determines the modified agent trajectory (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation": Nygaard's model agent generates the responsive modified behavior of the simulated agent after the interaction)….
Nygaard fails to teach explicitly determining a driving style type of the real-world agent in the real-world driving environment, based on at least one of:
a driving aggression score of the real-world agent;
a driving skill score of the real-world agent;
a reaction time score of the real-world agent; or
a law abidance score of the real-world agent; and
based on the driving style type of the real-world agent in the real-world driving environment.
Johnson teaches determining a driving style type of the real-world agent in the real-world driving environment, based on at least one of:
a driving aggression score of the real-world agent (Johnson, Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems"; section III-C “Driver aggression can be determined by the number of potentially aggressive events over an arbitrary epoch of driving time.”);
a driving skill score of the real-world agent;
a reaction time score of the real-world agent; or
a law abidance score of the real-world agent; and
based on the driving style type of the real-world agent in the real-world driving environment (Johnson, Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems"; section III-C “Driver aggression can be determined by the number of potentially aggressive events over an arbitrary epoch of driving time.”). In particular, Johnson describes determining a driving style type of a road user by classifying the user's driving as typical or aggressive, where the classification rests on a driving aggression measure derived from the number of potentially-aggressive events recorded for that user.
Nygaard and Johnson are analogous art because they are both related to a method for simulating vehicle behaviors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teaching of Johnson with autonomous vehicle simulation computerized simulations of reactive agents for simulating vehicle behaviors of Nygaard’s invention to provide a system that is a completely mobile, effective and inexpensive way to detect and recognize driving events thereby increasing the awareness of potentially-aggressive actions, and further promoting driver safety (Johnson: section VI).
As per Claim 2, Nygaard teaches the operations further comprising: … controlling a second simulated agent during the driving simulation… (Nygaard, col. 13, lines 40-51 "If there is no collision or near collision with the model agent, but a collision or near collision with a second agent in the new simulation, another new simulation may be run with the second agent replaced with a model agent" Nygaard controls a second simulated agent by replacing a second logged agent with its own model agent in the simulation).
Nygaard fails to teach explicitly determining a second driving style type associated with a second real-world agent in the real-world driving environment, based on the log data, wherein the second driving style type is different from the driving style type; and … based on the second driving style type.
Johnson teaches determining a second driving style type associated with a second real-world agent in the real-world driving environment, based on the log data, wherein the second driving style type is different from the driving style type; and … based on the second driving style type (Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive"; section V “We have used the MIROAD system in three different vehicles, with three different drivers, and collected over 200 driver events in urban, rural and highway environments.”; section VI "The system actively detects and records events that characterize a driver's style, thereby increasing the awareness of potentially-aggressive actions").
As per Claim 3, Nygaard teaches …wherein determining the modified agent trajectory of the simulated agent … (col. 13, lines 15-28 "based on the details of the agent defined in the log data used to run the new simulation. For instance, a small passenger vehicle may be replaced with another small passenger vehicle, a bicyclist may be replaced with a bicyclist, and so on… In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors ").
Nygaard fails to teach explicitly wherein determining the driving style type of the real-world agent in the real-world driving environment comprises:
determining, based on the log data, a first value associated with a first instance of a driving behavior of the real-world agent, and a second value associated with a second instance of the driving behavior of the real-world agent; and
determining a distribution associated with the driving behavior of the real-world agent based on the first value and the second value,
… sampling a third value from the distribution.
Johnson teaches wherein determining the driving style type of the real-world agent in the real-world driving environment (Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems") comprises:
determining, based on the log data, a first value associated with a first instance of a driving behavior of the real-world agent, and a second value associated with a second instance of the driving behavior of the real-world agent (section IV.A "The sum of these distances along the warping path p describes the total cost"; section III.C "Driver aggression can be determined by the number of potentially-aggressive events over an arbitrary epoch of driving time"); and
determining a distribution associated with the driving behavior of the real-world agent based on the first value and the second value (section IV.A "When trying to determine whether or not a driving event is typical (non-aggressive) or aggressive, the DTW algorithm finds the closest match between the different styles of templates"),
… sampling a third value from the distribution (section IV.A "When trying to determine whether or not a driving event is typical (non-aggressive) or aggressive, the DTW algorithm finds the closest match between the different styles of templates"). In particular, Johnson teaches that the driving style determination is built from per-event measurements, computing a value for each detected instance of a driving maneuver from the recorded motion data, characterizing the driver's behavior over a population of detected events, aggregating the per-event values into an overall characterization of the driver's style. Further, Johnson describes drawing on the aggregated population of per-event values when generating behavior, so that a further value characteristic of the driver's style distribution informs the behavior produced.
As per Claim 6 and 14, Nygaard teaches a method/one or more non-transitory computer-readable media storing instructions executable by a processor, wherein the instructions, when executed, cause the processor to perform operations (Abstract, Fig. 1 and the description) comprising:
receiving log data associated with operation of a real-world vehicle in a real-world driving environment, wherein the log data indicates a real-world agent trajectory of a real-world agent different from the real-world vehicle in the real-world driving environment (col. 10 lines 42-67, col. 11 lines 1-4 “The log data may also include “event” data identifying different types of events such as collisions or near collisions with other objects, planned trajectories describing a planned geometry and/or speed for a potential path of the vehicle 100, actual locations of the vehicles at different times, actual orientations/headings of the vehicle at different times, actual speeds, accelerations and decelerations of the vehicle at different times,”; col. 12 lines 12-27 “ In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”);
…, and
executing a simulation including controlling, in a simulated environment, a simulated vehicle based on the real-world vehicle and a simulated agent based on the real-world agent (col. 11 lines 43-59, “the server computing devices 410 may run various simulations. These simulations may be log based simulations that are generated from the information stored in the aforementioned log data of storage system 450. In this regard, the server computing devices 410 may access the storage system 450 in order to retrieve the log data and run a simulation. For instance, a portion of the log data corresponding to a minute in real time of an autonomous vehicle that generated the log data may be retrieved from the storage system.”; col. 12 lines 12-27 “In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”; Col. 15 lines 29-54 “at block 910, a first simulation is run using log data collected by a vehicle operating in an autonomous driving mode. The first simulation is run using the software to control a first simulated vehicle, and the log data identifies at least one agent included in the first simulation.”), wherein executing the simulation comprises:
controlling the simulated agent, during a first time period in the simulation, to follow a first agent trajectory corresponding to the real-world agent trajectory of the real- world agent (col. 3 lines 60-67, col. 4 lines 1-16 “no matter what the simulated vehicle does, the agents will always react according to the log data”; Col. 15 lines 29-54 “at block 910, a first simulation is run using log data collected by a vehicle operating in an autonomous driving mode. The first simulation is run using the software to control a first simulated vehicle, and the log data identifies at least one agent included in the first simulation.”);
determining an interaction in the simulation between the simulated vehicle and a simulated object in the simulated environment (col. 15 lines 29-54 “At block 920, during (or after) the running of the first simulation, a particular type of interaction between the first simulated vehicle and the at least one agent is determined to have occurred.”); and
based at least in part on determining the interaction, controlling the simulated agent, during a second time period in the simulation after the first time period, to follow a modified agent trajectory determined by a planning component (col. 15 lines 29-54 “At block 930, in response to determining that the particular type of interaction between the first simulated vehicle and the at least one agent has occurred, a second simulation is run using the log data by replacing the at least one agent with a model agent that simulates a road user capable of responding to actions performed by simulated vehicles.”), wherein the modified agent trajectory causes the simulated agent to diverge from the real-world agent trajectory of the real-world agent (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation"; col. 13, lines 24-28 "In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors"), and wherein the planning component determines the modified agent trajectory… (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation": Nygaard's model agent generates the responsive modified behavior of the simulated agent after the interaction)….
Nygaard fails to teach explicitly determining, based on movements of the real-world agent in the log data, a driving style type of the real-world agent; and based on the driving style type of the real-world agent.
Johnson teaches determining, based on movements of the real-world agent in the log data, a driving style type of the real-world agent (Johnson, Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems"; section III-C “Driver aggression can be determined by the number of potentially aggressive events over an arbitrary epoch of driving time.”; section III.D "In passive mode, the system records and stores all data for further analysis. The data consists of video and an archive of the raw device motion (acceleration, rotation, attitude, timestamps)");
based on the driving style type of the real-world agent in the real-world driving (Johnson, Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems"; section II "MIROAD's ability to detect the driver's style can ultimately aid DASs and increase driver safety"; section III-C “Driver aggression can be determined by the number of potentially aggressive events over an arbitrary epoch of driving time.”). In particular, Johnson describes determining a driving style type of a road user by classifying the user's driving as typical or aggressive, where the classification rests on a driving aggression measure derived from the number of potentially-aggressive events recorded for that user.
Nygaard and Johnson are analogous art because they are both related to a method for simulating vehicle behaviors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teaching of Johnson with autonomous vehicle simulation computerized simulations of reactive agents for simulating vehicle behaviors of Nygaard’s invention to provide a system that is a completely mobile, effective and inexpensive way to detect and recognize driving events thereby increasing the awareness of potentially-aggressive actions, and further promoting driver safety (Johnson: section VI).
As per Claim 7 and 15, Nygaard teaches the operations further comprising: … controlling a second simulated agent during the simulation… (Nygaard, col. 13, lines 40-51 "If there is no collision or near collision with the model agent, but a collision or near collision with a second agent in the new simulation, another new simulation may be run with the second agent replaced with a model agent" Nygaard controls a second simulated agent by replacing a second logged agent with its own model agent in the simulation).
Nygaard fails to teach explicitly determining a second driving style type associated with a second real-world agent in the real-world driving environment, based on the log data, wherein the second driving style type is different from the driving style type of the real-world agent; and… based on the second driving style type.
Johnson teaches determining a second driving style type associated with a second real-world agent in the real-world driving environment, based on the log data, wherein the second driving style type is different from the driving style type of the real-world agent; and… based on the second driving style type (Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive"; section V “We have used the MIROAD system in three different vehicles, with three different drivers, and collected over 200 driver events in urban, rural and highway environments.”; section VI "The system actively detects and records events that characterize a driver's style, thereby increasing the awareness of potentially-aggressive actions").
As per Claim 8, Nygaard teaches wherein the second simulated agent is at least one of a pedestrian or a non-motorized vehicle within the simulation ((Nygaard, col. 4 lines 17-29 "simulated agents corresponding to other road users, such as vehicles, pedestrians, bicyclists, etc. may be replaced with model agents")).
As per Claim 9 and 16, Nygaard fails to teach explicitly wherein determining the driving style type of the real-world agent comprises:
determining, based on the log data, a first value associated with a first instance of a driving behavior of the real-world agent, and a second value associated with a second instance of the driving behavior of the real-world agent; and
aggregating the first value and the second value to determine a value for the driving style type of the real-world agent.
Johnson teaches wherein determining the driving style type of the real-world agent (Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems") comprises:
determining, based on the log data, a first value associated with a first instance of a driving behavior of the real-world agent, and a second value associated with a second instance of the driving behavior of the real-world agent (section IV.A "The sum of these distances along the warping path p describes the total cost"; section III.C "Driver aggression can be determined by the number of potentially-aggressive events over an arbitrary epoch of driving time"); and
aggregating the first value and the second value to determine a value for the driving style type of the real-world agent (section III.C "Driver aggression can be determined by the number of potentially-aggressive events over an arbitrary epoch of driving time"; section VI "The system actively detects and records events that characterize a driver's style, thereby increasing the awareness of potentially-aggressive actions").
As per Claim 11 and 18, Nygaard teaches further comprising: determining second log data associated with a second real-world agent in the real- world driving environment (col. 12 lines 12-27 “In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”; col. 13, lines 40-51 "If there is no collision or near collision with the model agent, but a collision or near collision with a second agent in the new simulation, another new simulation may be run with the second agent replaced with a model agent")… wherein determining the modified agent trajectory of the simulated agent… (col. 13, lines 15-28 "based on the details of the agent defined in the log data used to run the new simulation. For instance, a small passenger vehicle may be replaced with another small passenger vehicle, a bicyclist may be replaced with a bicyclist, and so on… In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors ").
Nygaard fails to teach explicitly determining a second driving style type of the second real-world agent, based on the second log data,
… based on the second driving style type of the second real-world agent.
Johnson teaches determining a second driving style type of the second real-world agent, based on the second log data (Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive"; section V "We have used the MIROAD system in three different vehicles, with three different drivers, and collected over 200 driver events in urban, rural and highway environments"),
… based on the second driving style type of the second real-world agent (section II "MIROAD's ability to detect the driver's style can ultimately aid DASs and increase driver safety"). In particular, Johnson describes determining a driving style type for each road user, which supplies the second agent's style characterization on which the modified trajectory may be based.
As per Claim 23 and 24, Nygaard teaches wherein the log data comprises log data captured by the real-world vehicle in the real-world driving environment (col. 11 lines 43-59, “the server computing devices 410 may run various simulations. These simulations may be log based simulations that are generated from the information stored in the aforementioned log data of storage system 450. In this regard, the server computing devices 410 may access the storage system 450 in order to retrieve the log data and run a simulation. For instance, a portion of the log data corresponding to a minute in real time of an autonomous vehicle that generated the log data may be retrieved from the storage system.”), and wherein the real-world agent is an agent vehicle different from the real-world vehicle in the real-world driving environment (col. 12 lines 12-27 “In example 600, a simulated vehicle 670, corresponding to vehicle 100 or vehicle 100A, is approaching an intersection 604. An agent vehicles 620, 622, 624, 626, generated from sensor data and/or event data from the log data for the simulation, are also approaching or passing through intersection 604.”).
As per Claim 25 and 26, Nygaard teaches wherein determining the modified agent trajectory for the simulated agent during the simulation (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation"; col. 13, lines 24-28 "In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors") comprises:
determining, by the planning component (col. 10 lines 42-67, col. 11, lines 1-9 "The storage system may also store model agents, or data and instructions that can be used to generate a simulated road user in order to interact with a virtual vehicle in a simulation"; col. 13, lines 24-28 "In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors"), …; and
selecting the modified agent trajectory for controlling the simulated agent (col. 13, lines 15-28 "based on the details of the agent defined in the log data used to run the new simulation. For instance, a small passenger vehicle may be replaced with another small passenger vehicle, a bicyclist may be replaced with a bicyclist, and so on… In some instances, the behavior of these model agents may be slightly altered from the log data. For instance, an agent vehicle may be replaced with a model agent that suddenly decides to drive more aggressively to ensure that the software is able to handle nearby aggressive actors "), ….
Nygaard fails to teach explicitly a second driving style type associated with the modified agent trajectory; and … based on the driving style type of the real-world vehicle matching the second driving style type associated with the modified agent trajectory.
Johnson teaches a second driving style type associated with the modified agent trajectory; and … based on the driving style type of the real-world vehicle matching the second driving style type associated with the modified agent trajectory (section IV.A "When trying to determine whether or not a driving event is typical (non-aggressive) or aggressive, the DTW algorithm finds the closest match between the different styles of templates"). In particular, Johnson describes determining a driving style type for the behavior under analysis, classifying it as one of the recognized style categories and a determination that finds the closest match between an observed driving style and the recognized style categories.
As per Claim 27, Nygaard fails to teach explicitly wherein determining the driving style type of the real-world agent is based on at least two of: a driving aggression score of the real-world agent; a driving skill score of the real-world agent; and a reaction time score of the real-world agent.
Johnson teaches wherein determining the driving style type of the real-world agent is based on at least two of: a driving aggression score of the real-world agent; a driving skill score of the real-world agent; and a reaction time score of the real-world agent (Johnson, Abstract "Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems"; section I "Our contributions to this area of research include: a method for determining both driving style and type of driving maneuver, using sensor fusion and the Dynamic Time Warping algorithm"; section III-C “Driver aggression can be determined by the number of potentially aggressive events over an arbitrary epoch of driving time.”). In particular, Johnson describes determining the driving style type from at least two driving-quality measures derived from the agent's logged movements which is namely, a driving aggression measure based on the count of potentially-aggressive events, together with a determination of the type and quality of the agent's individual driving maneuvers.
As per Claim 28, Nygaard fails to teach explicitly wherein determining the driving style type of the real-world agent is further based on at least one of: a law abidance score of the real-world agent; or an average turn signal usage score of the real-world agent.
Johnson teaches wherein determining the driving style type of the real-world agent is further based on at least one of: a law abidance score of the real-world agent; or an average turn signal usage score of the real-world agent (Abstract "Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States"; section III.C "Driver aggression can be determined by the number of potentially-aggressive events over an arbitrary epoch of driving time"). In particular, Johnson describes determining the driving style type from measures characterizing whether the agent's logged driving conforms to safe and lawful operation, including the recognition and counting of potentially-aggressive events that endanger other road users.
4. Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nygaard (US 10,795,804 B1) in view of Johnson ("Driving Style Recognition Using a Smartphone as a Sensor Platform”), and further in view of Dolben (US 11,731,652 B2).
Nygaard as modified by Johnson teaches most all the instant invention as applied to claims 1-3, 6-9, 11, 14-16, 18, and 23-28 above.
As per Claim 10 and 17, Nygaard as modified by Johnson fails to teach explicitly further comprising:
determining, based on the log data, a destination in the real-world driving environment associated with the real-world agent,
wherein determining the modified agent trajectory of the simulated agent is further based on the destination.
Dolben et al. teaches further comprising determining, based on the log data, a destination in the real-world driving environment associated with the real-world agent (Col. 14 lines 9-34 “The objective of the agent can be to travel to a particular destination in the environment or to perform a particular maneuver”, Fig. 4B);
wherein determining the modified agent trajectory of the simulated agent is further based on the destination (Col. 14 lines 9-34 “The objective of the agent can be to travel to a particular destination in the environment or to perform a particular maneuver”, Fig. 4B).
Nygaard, Johnson, and Dolben are analogous art because they are all related to a method for simulating vehicle behaviors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teaching of Dolben with autonomous vehicle simulation computerized simulations of reactive agents for simulating vehicle behaviors of Nygaard as modified by Johnson’s invention to provide a system that is a completely mobile, effective and inexpensive way to detect and recognize driving events thereby increasing the awareness of potentially-aggressive actions, and further promoting driver safety (Johnson: section VI) and to provide an improved system that encounters a wide array of various scenarios to test the efficacy and safety of the AV systems and computer simulations of scenarios that accurately provide realistic scenarios as they would be encountered in real-world settings (Dolben: col. 3 lines 14-29).
Response to Arguments
5. Applicant's arguments filed 01/29/2026have been fully considered but they are not persuasive.
Applicant’s arguments with respect to claims 1, 6 and 14 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 – Nygaard (US 10,795,804 B1) in view of Johnson ("Driving Style Recognition Using a Smartphone as a Sensor Platform”).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kesting A, Treiber M, Helbing D. General lane-changing model MOBIL for car-following models. Transportation Research Record. 2007 Jan;1999(1):86-94.
Schwarting W, Pierson A, Alonso-Mora J, Karaman S, Rus D. Social behavior for autonomous vehicles. Proceedings of the National Academy of Sciences. 2019 Dec 10;116(50):24972-8.
7. 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.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188
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