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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-9 in the reply filed on August 17, 2026 is acknowledged.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement filed September 30, 2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the reference US 1392120 does not appear to be accurate, as there was no US patent found with this number that was issued in 2022, nor that named Censi et al. as the inventor, therefore it appears that the wrong patent number was submitted. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-9 recite a method/process, therefore claims 1-9 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mathematical concepts and/or mental processes (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method for checking and modifying trajectories suggested by at least one planning module for an automated operating mode of a vehicle, the method comprising the following steps:
aggregating situation-specific information;
ascertaining at least one possible non-critical behavior of the vehicle based on the situation-specific information, wherein each of the at least one ascertained non-critical behavior is described by a set of boundary conditions;
classifying the at least one suggested trajectory as critical or non-critical based on the boundary conditions of the at least one ascertained non-critical behavior;
wherein at least when only critical trajectories are suggested, at least one of the critical trajectories is modified by:
selecting at least one of the at least one ascertained non-critical behavior as a target behavior, and
modifying the critical trajectory such that it satisfies the boundary conditions of the target behavior at least in a given section.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process. That is, nothing in the claim elements preclude the steps from practically being performed as mental process. For example, " aggregating situation specific information”, “ascertaining at least one possible non-critical behavior…”, “classifying the at least one suggested behavior”, and “wherein at least when only critical trajectories are suggested… selecting at least one..." encompass mental processes as a human can perform these limitations using observations, evaluations, judgments, and/or opinions. “aggregating situation specific information” involves a human observing and/or evaluating a set of situation specific information and “ascertaining at least one possible non-critical behavior…”, “classifying the at least one suggested behavior”, and “wherein at least when only critical trajectories are suggested… selecting at least one..." involves a human making a judgment or using paper and pencil to determine a non-critical behavior, classifying the suggested trajectories, and if the trajectories are classified as critical, selecting a non-critical behavior as a target behavior. Thus, the claim recites at least a mental process.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
A computer-implemented method for checking and modifying trajectories suggested by at least one planning module for an automated operating mode of a vehicle, the method comprising the following steps:
aggregating situation-specific information;
ascertaining at least one possible non-critical behavior of the vehicle based on the situation-specific information, wherein each of the at least one ascertained non-critical behavior is described by a set of boundary conditions;
classifying the at least one suggested trajectory as critical or non-critical based on the boundary conditions of the at least one ascertained non-critical behavior;
wherein at least when only critical trajectories are suggested, at least one of the critical trajectories is modified by:
selecting at least one of the at least one ascertained non-critical behavior as a target behavior, and
modifying the critical trajectory such that it satisfies the boundary conditions of the target behavior at least in a given section.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of " A computer-implemented method for checking and modifying trajectories…” the examiner submits that this limitation characterizes the method as being associated with a planning module for checking and modifying trajectories of a vehicle, which merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). Additionally, the claim limitation “modifying the critical trajectory …” does not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting (MPEP § 2106.05(g)). The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “modifying the critical trajectory …” amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). With respect to “modifying the critical trajectory …” it was ruled within Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), which are recited within MPEP 2106.05(d)(II) that mere data collection or receiving/obtaining and transmitting of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic matter, as it is here. Additionally, " A computer-implemented method for checking and modifying trajectories…” is merely a technological environment or field of use as the limitations merely link the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Dependent claims 2-9 specify limitations that elaborate on the abstract idea of claim 1 and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to "significantly more" for similar reasons.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tebbens et al. (US 20220187837; hereinafter Tebbens).
In regards to claim 1, Tebben discloses of a computer-implemented method for checking and modifying trajectories suggested by at least one planning module for an automated operating mode of a vehicle (“Enclosed are embodiments for scenario-based behavior specification and validation. In an embodiment, a method comprises: obtaining, using at least one processor, at least one trajectory associated with a driving scenario for an autonomous vehicle system; evaluating, using the at least one processor and at least one rulebook, the at least one trajectories to determine whether the at least one trajectory violates at least one rule in the at least one rulebook, wherein each rule in the rulebook is associated with at least one violation metric that is used to determine a degree to which the rule was satisfied or violated; determining, using the at least one processor and the at least one violation metric, a score for the at least one trajectory; and sending, using the at least one processor, the score to at least one of a software module in a software stack of the autonomous vehicle system, a simulation of the autonomous vehicle system or as a report or in a visual presented through a user interface of a cloud-based platform.” (Abstract), “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160) see also Para 0100), the method comprising the following steps:
aggregating situation-specific information (“In an embodiment, the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121. In an embodiment, the data storage unit 142 is similar to the ROM 308 or storage device 310 described below in relation to FIG. 3. In an embodiment, memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment 190 is transmitted to the AV 100 via a communications channel from a remotely located database 134.” (Para 0074), “Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.” (Para 0079));
ascertaining at least one possible non-critical behavior of the vehicle based on the situation-specific information, wherein each of the at least one ascertained non-critical behavior is described by a set of boundary conditions (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “As used herein, a “rulebook” is a data structure implementing a priority structure on a set of rules that are arranged based on their relative importance, where for any particular rule in the priority structure, the rule(s) having lower priority in the structure than the particular rule in the priority structure have lower importance than the particular rule. Possible priority structures include but are not limited to: hierarchical structures (e.g., total order or pre-order on different degrees of rule violations), non-hierarchical structures (e.g., a weighting system on the rules) or a hybrid priority structure in which subsets of rules are hierarchical but rules within each subset are non-hierarchical. Rules can include traffic laws, safety rules, ethical rules, local culture rules, passenger comfort rules and any other rules that could be used to evaluate a trajectory of a vehicle provided by any source (e.g., humans, text, regulations, websites).” (Para 0062));
classifying the at least one suggested trajectory as critical or non-critical based on the boundary conditions of the at least one ascertained non-critical behavior (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160));
wherein at least when only critical trajectories are suggested “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160)), at least one of the critical trajectories is modified by:
selecting at least one of the at least one ascertained non-critical behavior as a target behavior (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160)), and
modifying the critical trajectory such that it satisfies the boundary conditions of the target behavior at least in a given section (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160)).
In regards to claim 2, Tebben discloses of the method according to claim 1, wherein a respective distance between the at least one critical trajectory and the at least one non-critical behavior is determined using a predetermined distance metric, and the determined distances are used as a basis for selecting the at least one critical trajectory to be modified and for selecting the at least one associated target behavior (“FIG. 21 is an example screenshot of a graphical user interface (GUI) 2100 for rule evaluation, in accordance with one or more embodiments. GUI 2100 is an example of rule evaluation UI 501 shown in FIG. 5. GUI 2100 displays a bird's eye view (BEV) image 2102 of a road segment being evaluated. GUI affordance 2105 allows a user to select one or more layers to be overlaid on BEV image 2102, which, in this example, the user checked boxes for drivable area 2102a, footprint path 2102b, reference path 2102c, vehicles 2102d and check menu 2106. Check menu 2106 allows the user to select “checks” for the trajectory evaluation. In the example shown, the options for checks include no_collision, drivability, clearance and curvature_bound. The user has selected the “no_collision” check, which will cause the trajectory to be evaluated for collisions with other vehicles. GUI affordance 2107 provides a list of parameters for the selected check, which, in this example, includes distance_to_undrivability, max_curvature, max_inflection_points, curvature epsilon and min_clearance. The user has selected values for curvature epsilon and min_clearance. Accordingly, in this example, the user is testing a lane change maneuver to avoid a parked car and has specified a particular maximum curvature for the maneuver path and particular minimum lateral distance from the parked car.” (Para 0159), “A rule hierarchy 2104 is also displayed in GUI 2100 in response to the user selecting “Rulebook” option in menu 2101. The rule hierarchy 2104 shows the relative importance of the rules being applied to the trajectory. In this example, the rules are organized by importance from top to bottom where the most important rule is no_collision, followed by drivability, followed by track_clearance, followed by the least important rule of curvature_bound. The trajectory may violate the curvature_bound, track_clearance and drivability rules to ensure that the no_collision rule is enforced. This makes sense in that it is more desirable for the vehicle to drive off road and sacrifice the comfort of passengers (e.g., by making a quick avoidance maneuver or travel dangerously close to parked cars) to avoid a collision, which could result in serious injury or death.” (Para 0160), see also Para 0152).
In regards to claim 3, Tebben discloses of the method according to claim 1, wherein the at least one of the at least one critical trajectory is modified as part of an optimization process, wherein the boundary conditions of the selected non-critical behavior are used as optimization criteria (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), In other embodiments, other methods can be used to express system behavior under complex temporal requirements, including but not limited to: linear temporal logic (LTL), metric temporal logic (MTL), and time window temporal logic (TWTL). Also, other embodiments can use different scoring methods, including but not limited to: p-norms, heuristic optimization approaches (e.g., particle swarm optimization, simulated annealing, rapidly exploring random trees (RRTs)) and mixed integer linear programming (MILP).” (Para 0050)).
In regards to claim 4, Tebben discloses of the method according to claim 1, wherein the critical trajectory includes critical and non-critical sections and the critical trajectory is modified only in critical sections (“As used herein, “trajectory” refers to a path or route to operate an AV from a first spatiotemporal location to second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as the initial or starting location and the second spatiotemporal location is referred to as the destination, final location, goal, goal position, or goal location. In some examples, a trajectory is made up of one or more segments (e.g., sections of road) and each segment is made up of one or more blocks (e.g., portions of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real world locations. For example, the spatiotemporal locations are pick up or drop-off locations to pick up or drop-off persons or goods.” (Para 0058), “FIGS. 14A-14C illustrate rule-based evaluation of an example trajectory of an ego vehicle, in accordance with one or more embodiments. FIG. 14A shows a computer graphic of a road segment for the driving scenario and results for the checks. FIG. 14B is plot of curvature over time. FIG. 14C is a plot of the speed of the ego vehicle over time. The plots collectively show that the evaluated trajectory violates the inflection_point and speed_extrema rules at several occasions (5 times and 7 times respectively) in the example trajectory. In particular, FIG. 14A shows where, geographically, in the scenario the violations occurred, and FIGS. 14B and 14C show by how much the trajectory violates the acceptable values for each rule. The filtered plots are especially relevant for road logs, where vehicle sensors are noisy and might therefore register big spikes that do not reflect the actual curvature of the AV trajectory. The filtering remove some of that noise to only evaluate the actual AV trajectory, not the sensor noise. Note that road segments xxx have rule violations and road segments xxx do not have rule violations.” (Para 0151), “FIG. 21 is an example screenshot of a graphical user interface (GUI) 2100 for rule evaluation, in accordance with one or more embodiments. GUI 2100 is an example of rule evaluation UI 501 shown in FIG. 5. GUI 2100 displays a bird's eye view (BEV) image 2102 of a road segment being evaluated. GUI affordance 2105 allows a user to select one or more layers to be overlaid on BEV image 2102, which, in this example, the user checked boxes for drivable area 2102a, footprint path 2102b, reference path 2102c, vehicles 2102d and check menu 2106. Check menu 2106 allows the user to select “checks” for the trajectory evaluation. In the example shown, the options for checks include no_collision, drivability, clearance and curvature_bound. The user has selected the “no_collision” check, which will cause the trajectory to be evaluated for collisions with other vehicles. GUI affordance 2107 provides a list of parameters for the selected check, which, in this example, includes distance_to_undrivability, max_curvature, max_inflection_points, curvature epsilon and min_clearance. The user has selected values for curvature epsilon and min_clearance. Accordingly, in this example, the user is testing a lane change maneuver to avoid a parked car and has specified a particular maximum curvature for the maneuver path and particular minimum lateral distance from the parked car. “ (Para 0159), see also Para 0154)).
In regards to claim 5, Tebben discloses of the method according to claim 1, further comprising:
generating a surroundings model based on the situation-specific information (“As used herein, “sensor(s)” includes one or more hardware components that detect information about the environment surrounding the sensor. Some of the hardware components can include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components such as analog-to-digital converters, a data storage device (such as a RAM and/or a nonvolatile storage), software or firmware components and data processing components such as an ASIC (application-specific integrated circuit), a microprocessor and/or a microcontroller.” (Para 0059), “In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring properties of state or condition of the AV 100, such as the AV's position, linear velocity and acceleration, angular velocity and acceleration, and heading (e.g., an orientation of the leading end of AV 100). Example of sensors 121 are a Global Navigation Satellite System (GNSS) receiver, inertial measurement units (IMU) that measure both vehicle linear accelerations and angular rates, wheel speed sensors for measuring or estimating wheel slip ratios, wheel brake pressure or braking torque sensors, engine torque or wheel torque sensors, and steering angle and angular rate sensors.” (Para 0072), and “In an embodiment, the sensors 121 also include sensors for sensing or measuring properties of the AV's environment. For example, monocular or stereo video cameras 122 in the visible light, infrared or thermal (or both) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, speed sensors, temperature sensors, humidity sensors, and precipitation sensors.” (Para 0073));
ascertaining possible behaviors of the vehicle based on the situation-specific information and the surroundings model, wherein each of the ascertained possible behaviors s described by a set of boundary conditions (“Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.” (Para 0079), “Autonomous vehicles use a planner in their software stacks to generate candidate trajectories for the autonomous vehicle under various scenarios. The planner uses sensor data and the vehicle's physical state (e.g., position, speed, heading) to generate possible trajectories for the vehicle to avoid collision with agents (e.g., other vehicles, pedestrians) in the vicinity of the autonomous vehicle. The planner typically takes into consideration the violation of traffic laws and possibly other driving rules (e.g., safety, ethics, local culture, passenger comfort, courtesy, performance, etc.) when determining which candidate trajectory the vehicle should take for a given driving scenario. Accordingly, it is desirable to evaluate planned trajectories under a large variety of driving scenarios that may occur in the real-world.” (Para 0002), see also Para 0158);
classifying the ascertained possible behaviors of the vehicle as non-critical or critical based on the boundary conditions in conjunction with a predefined set of rules (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “Process 2200 can begin by obtaining one or more trajectories, maps and perception outputs (2201). For example, the trajectories, maps and perception outputs can be provided by software modules or other system using an API.” (Para 0162));
classifying the suggested trajectory as critical or non-critical by checking whether the suggested trajectory satisfies the boundary conditions of the ascertained possible behaviors (“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “Process 2200 can begin by obtaining one or more trajectories, maps and perception outputs (2201). For example, the trajectories, maps and perception outputs can be provided by software modules or other system using an API.” (Para 0162)).
In regards to claim 6, Tebben discloses of the method according to claim 5, wherein the ascertained possible behaviors of the vehicle are prioritized based on the boundary conditions in conjunction with the predefined set of rules, and the prioritization is taken into account when selecting the target behavior for the at least one critical trajectory to be modified(“FIG. 20 illustrates the use of a rulebook, in accordance with one or more embodiments. The rulebook in FIG. 20 includes a pre-ordered set of rules arranged in a hierarchy of relative importance. Trajectories 2001, 2002 are evaluated using the rulebook to determine if the trajectories violate or satisfy rules A and B in the rulebook. In this example, rule A may be more important than rule B, and therefore rule A is at a higher level of the rulebook hierarchy. Rule A and rule B may be incomparable because they are not conflicting in any way. Rule A and rule B may have the same rank in the rulebook, i.e., the same level of the rulebook hierarchy. Each trajectory 2001, 2002 is evaluated by rulebook 2004 and the results (e.g., the number of rule violations and/or satisfactions) are compared 2005 to determine a difference 2006 in performance of the two trajectories 2001, 2002. In an embodiment, the comparison allows one trajectory to be selected over the other based on the number of rule violations. In an embodiment, evaluating a trajectory using the rulebook(s) results in a score for the trajectories 2001, 2002, which facilitates the comparing of the trajectories, i.e., the score is based on the number of rule violations or satisfactions. For example, if trajectory 2001 has a higher score than trajectory 2002, trajectory 2001 had less rule violations (or more rule satisfactions) than trajectory 2002. In an embodiment, the lower the score the less rule violations or satisfactions. In another embodiment, the violation of the highest rule in the priority structure violated by a trajectory, the more violation the trajectory has.” (Para 0158), “Process 2200 can begin by obtaining one or more trajectories, maps and perception outputs (2201). For example, the trajectories, maps and perception outputs can be provided by software modules or other system using an API.” (Para 0162)).
In regards to claim 7, Tebben discloses of the method according to claim 1, wherein results of a classification and/or modification of a critical trajectory are made available to the planning component that suggested the trajectory (“By virtue of the implementation of the systems and methods described herein, trajectories may be more accurately scored. As a result, systems described herein (e.g., planning systems) may select trajectories that satisfy one or more rules and/or forego selection of trajectories that do not satisfy the one or more rules when programming (e.g., training) one or more systems of an autonomous vehicle architecture. This, in turn, may result in better and more predictable operation of vehicles including such an autonomous vehicle architecture.” (Para 0053), “In an embodiment, rule evaluator 503 includes an application programming interface (API) that allows external systems to programmatically request evaluation of a trajectory directly from rule evaluator 503. For example, planning module 404 can use the API to request a planned trajectory evaluation, provide the planned trajectory and map identifiers (IDs) to rule evaluator 503 through the API, and receive a score for the trajectory from rule evaluator 503 through the API. In an embodiment, rules database 504 stores various rules in the form of a pre-ordered, hierarchical rulebook, as described more fully in reference to FIGS. 12-16. Rules can be provided from (e.g., represent) laws and regulations, derived by machine learning algorithms from human knowledge and any other source. System 500 includes an ingestion engine (not shown) that ingests the rules and stores the rules in rulebooks in rules database 504 in a manner that allows the rules to be accessed by rule evaluator 503 (e.g., using an index). In an embodiment, the rules are organized in a hierarchical object-oriented data structure that include data and operations, as described more fully in reference to FIG. 16. In an embodiment, the rule database 504 stores various rules in the form of pre-order on different degrees of rule violations. For example, having less than 0.8 violation for parked car clearance is more important than reaching the goal, which is more important than having less than 0.1 violation for parked car clearance.” (Para 0104)).
In regards to claim 8, Tebben discloses of the method according to claim 1, wherein trajectories classified as critical are stored in a log file together with results of any modification (“In an embodiment, the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121. In an embodiment, the data storage unit 142 is similar to the ROM 308 or storage device 310 described below in relation to FIG. 3. In an embodiment, memory 144 is similar to the main memory 306 described below. In an embodiment, the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions. In an embodiment, data relating to the environment 190 is transmitted to the AV 100 via a communications channel from a remotely located database 134.” (Para 0074), “In an embodiment, a rule-based trajectory evaluation system comprises: a user interface configured for receiving user input selecting at least a driving scenario; at least one processor; and a memory storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to: evaluate, using at least one rulebook, at least one trajectory in the at least one driving scenario to determine whether the at least one trajectory violates at least one rule in the at least one rulebook, wherein each rule in the rulebook is associated with at least one violation metric that is used to determine a degree to which the rule was satisfied or violated; determine, using the at least one metric, a score for the at least one trajectory; generate a report or visual related to the score; and provide data associated with the report, the data associated with the report configured to cause the user interface to present the report via the user interface.” (Para 0010)).
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.
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.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tebben in view of Golov (DE 112020000611; see attached English translation for citations).
In regards to claim 9, Tebben discloses of the method according to claim 1.
However, Tebben does not specifically disclose of characterized in that the aggregated situation-specific information is continuously cached and that this cached situation-specific information is permanently stored at least when only critical trajectories have been suggested and a critical trajectory has been modified.
Golov, in the same field of endeavor, teaches of characterized in that the aggregated situation-specific information is continuously cached and that this cached situation-specific information is permanently stored at least when only critical trajectories have been suggested and a critical trajectory has been modified (“The caching at block 303 and saving at block 309 can parallel to the receipt of the next new section at block 301 and to get the next oldest section at block 305 be performed. Thus, the new, incoming sections of the sensor data stream ( 151 ) are continuously cached in the cyclic buffer memory, while an older, delayed section of the sensor data stream ( 151 ) to save in the non-volatile memory ( 165 ) can be called up from the cyclic buffer memory.” (Page 17 Para 0009), “In one implementation, in response to the accident signal, the cyclic accident data buffer temporarily stops caching incoming new sensor data in order to preserve its content, and resumes caching data after at least an oldest portion of the content is in the cyclic accident data buffer the slot in the non-volatile memory of the data recorder was copied. Optionally, the cyclic buffer memory for accident data can resume the intermediate storage of incoming new sensor data after the storage of the existing data for the accident signal in the non-volatile memory has been completed. Optionally, the cyclic buffer memory for accident data can resume the caching of incoming new sensor data after a previously defined portion of the content of the cyclic buffer memory for accident data has been copied into a slot in the non-volatile memory and before the entire content of the cyclic buffer memory for accident data has been copied into the slot, wherein the temporary storage of incoming new sensor data does not overwrite the accident sensor data which are to be copied into the non-volatile memory of the data recorder.” (Page 5 Para 0007), “In one example, after an accident (e.g. a collision or near-collision) or a near-accident event, the sensor data can be evaluated in order to determine the cause of the accident. Such sensor data can be referred to in this document as accident sensor data. The accident sensor data can be analyzed in order, for example, to identify the cause of the accident or near-accident event and / or unsafe aspects or a safe design of the autonomous vehicle. The analysis can lead to improved control designs and / or configurations for the safe operation of the autonomous vehicle and / or similar vehicles.” (Page 3 Para 0006).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the aggregate situation specific information, as taught by Tebben, to include to be continuously cached and permanently stored when only crit5ical trajectories are suggested, as taught by Golov, with a reasonable expectation of success in order to allow the data concerning an accident or near-accident to be evaluated to improve the control of the vehicle in the future (Golov Page 3 Para 0006).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arun et al. (US 20240149892) discloses of categorizing the criticalness of an issue of a vehicle trajectory.
Wilhelm et al. (US 20160001775) discloses of permitting a vehicle to travel on a sidewalk, median, or oncoming lane in order to avoid a collision.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyle J Kingsland whose telephone number is (571)272-3268. The examiner can normally be reached Monday-Friday from 8:00-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE J KINGSLAND/ Primary Examiner, Art Unit 3663
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