DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed on 05/15/2026 with respect to claim(s) 1-20 have been fully considered but they are not persuasive or moot in view of new ground of rejection provided below.
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.
Claim(s) 1-3, 5, 10, 11-13, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael).
Regarding Claim 1, Dolben teaches a system, comprising:
at least one processor (See at least Para [0065] “In particular embodiments, the vehicle 640 may be equipped with a processing unit (e.g., one or more CPUs and GPUs), memory, and storage…”)
at least one non-transitory storage media storing instructions that, when executed by
the at least one processor (See at least Para [0077] “Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other types of integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.”), cause the at least one processor to:
apply a plurality of safety parameters to a plurality of trajectories generated for an ego
vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories (See at least Para [0003] “…A set of trajectories can be generated based on the one or more behavior constraints…”, discloses a set of trajectories is generated based on the one or more behavior constraints which is construed as applying a plurality of safety parameters to a plurality of trajectories generated for an ego vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories, behavior constraint is considered as safety parameters comprising a predefined assumption).
determine whether the plurality of trajectories are unsafe based at least on application
of the plurality of safety parameters to the plurality of trajectories (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses unrealistic trajectories which are considered unsafe);
filter a trajectory from the plurality of trajectories based at least on determining the
trajectory is unsafe (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses filtering candidate trajectories by removing unrealistic candidate trajectories which are considered unsafe);
provide the remaining trajectories from the plurality of trajectories to a machine
learning model trained to generate a score for selection of a selected trajectory for the vehicle from the remaining trajectories (See at least Para [0047] “…The candidate trajectories can be scored, and a trajectory can be selected from the candidate trajectories based on the scores of the candidate trajectories. The scores of the candidate trajectories can be based on whether the candidate trajectories violate any constraints. Candidate trajectories that violate fewer constraints can be associated with higher scores than candidate trajectories that violate more constraints. The selected trajectory can be based on a candidate trajectory with the highest score.”, Para [0048] “…In some cases, the same or another trained machine learning model can be applied to candidate trajectories and a trajectory can be selected from the candidate trajectories based on the trained machine learning model. ..”); and
controlling operation of the vehicle based on the selected trajectory (See at least Para
[0049] “In FIG. 3, the drive controller 318 can be configured to generate a behavior of an agent based on a trajectory.”).
Although, Dolben discloses set of trajectories being generated based on the one or more behavior constraints (See at least Para [0003]) and filtering candidate trajectories by removing unrealistic candidate trajectories (See at least Para [0047]), he does not explicitly used the term unsafe when disclosed removing trajectories.
However, Micheal teaches filter trajectories based at least on determining the trajectory is unsafe (See at least Claim 5 “…eliminating unsafe trajectories from the selected set of motion primitives, the unsafe trajectories determined based on the sensed environmental conditions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine Dolben with the teachings of Michael and include the feature of eliminating trajectories based on safety regarding sensed environmental conditions, thereby enhance performance safety (See at least Para [0010] “…The system can enable safe and efficient teleoperation in high disturbance scenarios by leveraging the robot's robust onboard control systems to enhance a human's capabilities to identify and navigate in unknown scenarios.”).
Regarding Claim 2, modified Dolben teaches all the elements of claim 1. Dolben further teaches wherein the plurality of trajectories are determined to be unsafe when the vehicle following the plurality of trajectories fails the safety check based at least on the predefined assumption (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories. The candidate trajectories can be scored, and a trajectory can be selected from the candidate trajectories based on the scores of the candidate trajectories. The scores of the candidate trajectories can be based on whether the candidate trajectories violate any constraints. Candidate trajectories that violate fewer constraints can be associated with higher scores than candidate trajectories that violate more constraints. The selected trajectory can be based on a candidate trajectory with the highest score.”, Para [0003] “…A trajectory can be selected from the set of trajectories based on determining that the trajectory satisfies one or more predetermined criteria…”, Para [0004] “In an embodiment, the selecting the trajectory from the set of trajectories is further based on one or more scores associated with each trajectory of the set of trajectories and wherein each score is determined based on whether the trajectory satisfies the one or more predetermined criteria.”).
Although, Dolben discloses set of trajectories being generated based on the one or more behavior constraints (See at least Para [0003]) and filtering candidate trajectories by removing unrealistic candidate trajectories (See at least Para [0047]), he does not explicitly used the term unsafe when disclosed removing trajectories.
However, Micheal teaches filter trajectories based at least on determining the trajectory is unsafe based on sensed environmental conditions (See at least Claim “…eliminating unsafe trajectories from the selected set of motion primitives, the unsafe trajectories determined based on the sensed environmental conditions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine Dolben with the teachings of Michael and include the feature of eliminating trajectories based on safety regarding sensed environmental conditions, thereby enhance performance safety (See at least Para [0010] “…The system can enable safe and efficient teleoperation in high disturbance scenarios by leveraging the robot's robust onboard control systems to enhance a human's capabilities to identify and navigate in unknown scenarios.”).
Regarding Claim 3, modified Dolben teaches all the elements of claim 1. Dolben further teaches wherein the safety check includes at least one of determining, based at least on the predefined assumption, whether the ego vehicle experiences a collision while the ego vehicle travels along the plurality of trajectories and determining, based at least on the predefined assumption, whether the ego vehicle maintains at least a threshold distance behind a non-ego vehicle of the non-ego vehicles while the ego vehicle travels along the plurality of trajectories (See at least Para [0045] “…As other examples, the one or more constraints can include boundary constraints associated with lanes on the road, curbs on the road, and a follow distance behind other vehicles on the road. The boundary constraints can be based on where the agent should travel in order to maintain safety…”).
Regarding Claim 5, modified Dolben teaches all the elements of claim 1. Dolben further teaches wherein the plurality of safety parameters further includes: a trajectory modifier modifying the plurality of trajectories prior to filtering the trajectory from the plurality of trajectories; and wherein the safety check is further performed based on modified plurality of trajectories (See at least Para [0009] “In an embodiment, the change in the environment is associated with an event in the environment, the one or more changed behavior constraints are based on the event, and the set of changed trajectories are based on the event.”).
Regarding Claim 10, modified Dolben teaches all the elements of claim 1. Dolben further teaches wherein the instructions, when executed by the at least one processor, further cause the at least one processor to generate the plurality of trajectories based on a scene context (See at least Para [0039] “…The environment can include contextual information describing features in the environment. The features can include static objects, semi-static objects, dynamic objects, agents, or a combination thereof. The features can also include various road types such as roads, freeways, and intersections. Based on the contextual information, one or more constraints associated with the environment can be determined…”, Para [0027] “…As a result, potential trajectories and changes in behavior of agents are more granular than conventional approaches due to taking into consideration contextual information and the degree of different contextual information that is considered. Based on the one or more constraints, candidate trajectories can be generated…”).
Regarding Claim 11, Dolben teaches a method, comprising:
applying a plurality of safety parameters to a plurality of trajectories generated for an ego
vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories (See at least Para [0003] “…A set of trajectories can be generated based on the one or more behavior constraints…”, discloses a set of trajectories is generated based on the one or more behavior constraints which is construed as applying a plurality of safety parameters to a plurality of trajectories generated for an ego vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories, behavior constraint is considered as safety parameters comprising a predefined assumption).
determining whether the plurality of trajectories are unsafe based at least on application
of the plurality of safety parameters to the plurality of trajectories (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses unrealistic trajectories which are considered unsafe);
filtering a trajectory from the plurality of trajectories based at least on determining the
trajectory is unsafe (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses filtering candidate trajectories by removing unrealistic candidate trajectories which are considered unsafe);
providing the remaining trajectories from the plurality of trajectories to a machine
learning model trained to generate a score for selection of a selected trajectory for the vehicle from the remaining trajectories (See at least Para [0047] “…The candidate trajectories can be scored, and a trajectory can be selected from the candidate trajectories based on the scores of the candidate trajectories. The scores of the candidate trajectories can be based on whether the candidate trajectories violate any constraints. Candidate trajectories that violate fewer constraints can be associated with higher scores than candidate trajectories that violate more constraints. The selected trajectory can be based on a candidate trajectory with the highest score.”, Para [0048] “…In some cases, the same or another trained machine learning model can be applied to candidate trajectories and a trajectory can be selected from the candidate trajectories based on the trained machine learning model. ..”); and
controlling operation of the vehicle based on the selected trajectory (See at least Para
[0049] “In FIG. 3, the drive controller 318 can be configured to generate a behavior of an agent based on a trajectory.”).
Although, Dolben discloses set of trajectories being generated based on the one or more behavior constraints (See at least Para [0003]) and filtering candidate trajectories by removing unrealistic candidate trajectories (See at least Para [0047]), he does not explicitly used the term unsafe when disclosed removing trajectories.
However, Micheal teaches filter trajectories based at least on determining the trajectory is unsafe (See at least Claim 5 “…eliminating unsafe trajectories from the selected set of motion primitives, the unsafe trajectories determined based on the sensed environmental conditions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine Dolben with the teachings of Michael and include the feature of eliminating trajectories based on safety regarding sensed environmental conditions, thereby enhance performance safety (See at least Para [0010] “…The system can enable safe and efficient teleoperation in high disturbance scenarios by leveraging the robot's robust onboard control systems to enhance a human's capabilities to identify and navigate in unknown scenarios.”).
Regarding Claim 12, modified Dolben teaches all the elements of claim 11. Dolben further teaches wherein the plurality of trajectories are determined to be unsafe when the vehicle following the plurality of trajectories fails the safety check based at least on the predefined assumption (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories. The candidate trajectories can be scored, and a trajectory can be selected from the candidate trajectories based on the scores of the candidate trajectories. The scores of the candidate trajectories can be based on whether the candidate trajectories violate any constraints. Candidate trajectories that violate fewer constraints can be associated with higher scores than candidate trajectories that violate more constraints. The selected trajectory can be based on a candidate trajectory with the highest score.”, Para [0003] “…A trajectory can be selected from the set of trajectories based on determining that the trajectory satisfies one or more predetermined criteria…”, Para [0004] “In an embodiment, the selecting the trajectory from the set of trajectories is further based on one or more scores associated with each trajectory of the set of trajectories and wherein each score is determined based on whether the trajectory satisfies the one or more predetermined criteria.”).
Although, Dolben discloses set of trajectories being generated based on the one or more behavior constraints (See at least Para [0003]) and filtering candidate trajectories by removing unrealistic candidate trajectories (See at least Para [0047]), he does not explicitly used the term unsafe when disclosed removing trajectories.
However, Micheal teaches filter trajectories based at least on determining the trajectory is unsafe based on sensed environmental conditions (See at least Claim “…eliminating unsafe trajectories from the selected set of motion primitives, the unsafe trajectories determined based on the sensed environmental conditions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine Dolben with the teachings of Michael and include the feature of eliminating trajectories based on safety regarding sensed environmental conditions, thereby enhance performance safety (See at least Para [0010] “…The system can enable safe and efficient teleoperation in high disturbance scenarios by leveraging the robot's robust onboard control systems to enhance a human's capabilities to identify and navigate in unknown scenarios.”).
Regarding Claim 13, modified Dolben teaches all the elements of claim 11. Dolben further teaches wherein the safety check includes at least one of determining, based at least on the predefined assumption, whether the ego vehicle experiences a collision while the ego vehicle travels along the plurality of trajectories and determining, based at least on the predefined assumption, whether the ego vehicle maintains at least a threshold distance behind a non-ego vehicle of the non-ego vehicles while the ego vehicle travels along the plurality of trajectories (See at least Para [0045] “…As other examples, the one or more constraints can include boundary constraints associated with lanes on the road, curbs on the road, and a follow distance behind other vehicles on the road. The boundary constraints can be based on where the agent should travel in order to maintain safety…”).
Regarding Claim 15, modified Dolben teaches all the elements of claim 11. Dolben further teaches wherein the plurality of safety parameters further includes: a trajectory modifier modifying the plurality of trajectories prior to filtering the trajectory from the plurality of trajectories; and wherein the safety check is further performed based on modified plurality of trajectories (See at least Para [0009] “In an embodiment, the change in the environment is associated with an event in the environment, the one or more changed behavior constraints are based on the event, and the set of changed trajectories are based on the event.”).
Regarding Claim 20, Dolben teaches at least one non-transitory storage media storing instructions that, when executed by at least one processor (See at least Para [0065] “In particular embodiments, the vehicle 640 may be equipped with a processing unit (e.g., one or more CPUs and GPUs), memory, and storage…”, Para [0077] “Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other types of integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.”), cause the at least one processor to: apply a plurality of safety parameters to a plurality of trajectories generated for an ego
vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories (See at least Para [0003] “…A set of trajectories can be generated based on the one or more behavior constraints…”, discloses a set of trajectories is generated based on the one or more behavior constraints which is construed as applying a plurality of safety parameters to a plurality of trajectories generated for an ego vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories, behavior constraint is considered as safety parameters comprising a predefined assumption).
determine whether the plurality of trajectories are unsafe based at least on application
of the plurality of safety parameters to the plurality of trajectories (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses unrealistic trajectories which are considered unsafe);
filter a trajectory from the plurality of trajectories based at least on determining the
trajectory is unsafe (See at least Para [0047] “…In some cases, thresholds associated with minimum acceleration, maximum acceleration, minimum jerk, maximum jerk, minimum speed, or maximum speed can be applied to filter the candidate trajectories. Filtering the candidate trajectories can remove, for example, unrealistic candidate trajectories…”, discloses filtering candidate trajectories by removing unrealistic candidate trajectories which are considered unsafe);
provide the remaining trajectories from the plurality of trajectories to a machine
learning model trained to generate a score for selection of a selected trajectory for the vehicle from the remaining trajectories (See at least Para [0047] “…The candidate trajectories can be scored, and a trajectory can be selected from the candidate trajectories based on the scores of the candidate trajectories. The scores of the candidate trajectories can be based on whether the candidate trajectories violate any constraints. Candidate trajectories that violate fewer constraints can be associated with higher scores than candidate trajectories that violate more constraints. The selected trajectory can be based on a candidate trajectory with the highest score.”, Para [0048] “…In some cases, the same or another trained machine learning model can be applied to candidate trajectories and a trajectory can be selected from the candidate trajectories based on the trained machine learning model. ..”); and
controlling operation of the vehicle based on the selected trajectory (See at least Para
[0049] “In FIG. 3, the drive controller 318 can be configured to generate a behavior of an agent based on a trajectory.”).
Although, Dolben discloses set of trajectories being generated based on the one or more behavior constraints (See at least Para [0003]) and filtering candidate trajectories by removing unrealistic candidate trajectories (See at least Para [0047]), he does not explicitly used the term unsafe when disclosed removing trajectories.
However, Micheal teaches filter trajectories based at least on determining the trajectory is unsafe (See at least Claim 5 “…eliminating unsafe trajectories from the selected set of motion primitives, the unsafe trajectories determined based on the sensed environmental conditions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine Dolben with the teachings of Michael and include the feature of eliminating trajectories based on safety regarding sensed environmental conditions, thereby enhance performance safety (See at least Para [0010] “…The system can enable safe and efficient teleoperation in high disturbance scenarios by leveraging the robot's robust onboard control systems to enhance a human's capabilities to identify and navigate in unknown scenarios.”).
Claim 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael), and further in view of Zhu et al. (US 20230043905 A1) (Hereinafter Zhu).
Regarding Claim 4, modified Dolben teaches all the elements of claim 1.
However, Dolben does not explicitly spell out wherein the predefined assumption includes at least one of an assumption that the non-ego vehicles are stationary while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles maintain a current heading and velocity while the ego vehicle travels along the plurality of trajectories, an assumption the non-ego vehicles behind the ego vehicle are excluded, and an assumption that all the non-ego vehicles except for a non- ego vehicle directly in front of the ego vehicle are excluded.
Zhu teaches wherein the predefined assumption includes at least one of an assumption that the non-ego vehicles are stationary while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories (See at least Para [0027] “…generally assume a worst case scenario in which the ego vehicle 105 is underperforming (thus the use of the minimum braking for the ego vehicle) and the target vehicle 125 is at peak performance (thus the use of maximum acceleration—or maximum deceleration/braking where appropriate—for the target vehicle 125)…”), an assumption that the non-ego vehicles maintain a current heading and velocity while the ego vehicle travels along the plurality of trajectories, an assumption the non-ego vehicles behind the ego vehicle are excluded, and an assumption that all the non-ego vehicles except for a non- ego vehicle directly in front of the ego vehicle are excluded.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Zhu and include the feature of an assumption being that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories, thereby provide enhanced safety (See at least Para [0019] “FIG. 1 illustrates an example of a moving vehicle scenario demonstrating an autonomous vehicle operation safety model, according to an embodiment…”).
Regarding Claim 14, modified Dolben teaches all the elements of claim 11.
However, Dolben does not explicitly spell out wherein the predefined assumption includes at least one of an assumption that the non-ego vehicles are stationary while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles maintain a current heading and velocity while the ego vehicle travels along the plurality of trajectories, an assumption the non-ego vehicles behind the ego vehicle are excluded, and an assumption that all the non-ego vehicles except for a non- ego vehicle directly in front of the ego vehicle are excluded.
Zhu teaches wherein the predefined assumption includes at least one of an assumption that the non-ego vehicles are stationary while the ego vehicle travels along the plurality of trajectories, an assumption that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories (See at least Para [0027] “…generally assume a worst case scenario in which the ego vehicle 105 is underperforming (thus the use of the minimum braking for the ego vehicle) and the target vehicle 125 is at peak performance (thus the use of maximum acceleration—or maximum deceleration/braking where appropriate—for the target vehicle 125)…”), an assumption that the non-ego vehicles maintain a current heading and velocity while the ego vehicle travels along the plurality of trajectories, an assumption the non-ego vehicles behind the ego vehicle are excluded, and an assumption that all the non-ego vehicles except for a non- ego vehicle directly in front of the ego vehicle are excluded.
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Zhu and include the feature of an assumption being that the non-ego vehicles perform a hard brake while the ego vehicle travels along the plurality of trajectories, thereby provide enhanced safety (See at least Para [0019] “FIG. 1 illustrates an example of a moving vehicle scenario demonstrating an autonomous vehicle operation safety model, according to an embodiment…”).
Claim 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael), and further in view of Aine (US 20180079420 A1).
Regarding Claim 6, modified Dolben teaches all the elements of claim 5.
However, Dolben does not explicitly spell out wherein the trajectory modifier includes at least one of the ego vehicle following the plurality of trajectories for a fixed period followed by a deceleration along the plurality of trajectories, the ego vehicle following the plurality of trajectories for a predefined duration, the ego vehicle experiencing a predefined brake acceleration, and the ego vehicle experiencing a maximum jerk.
Aine teaches wherein the trajectory modifier includes at least one of the ego vehicle following the plurality of trajectories for a fixed period followed by a deceleration along the plurality of trajectories, the ego vehicle following the plurality of trajectories for a predefined duration, the ego vehicle experiencing a predefined brake acceleration, and the ego vehicle experiencing a maximum jerk (See at least Para [0030] “… For example, the tolerance of motion plan may include headway maintained and/or tolerance over comfort constraints on the motion of the ego vehicle (e.g., maximum jerk).”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Aine and include the feature of the trajectory modifier including the ego vehicle experiencing a maximum jerk, thereby enhance safety (See at least Para [0017] “… A minimum headway constraint may be a design feature of a vehicle control system added to enhance safety…”).
Regarding Claim 16, modified Dolben teaches all the elements of claim 15.
However, Dolben does not explicitly spell out wherein the trajectory modifier includes at least one of the ego vehicle following the plurality of trajectories for a fixed period followed by a deceleration along the plurality of trajectories, the ego vehicle following the plurality of trajectories for a predefined duration, the ego vehicle experiencing a predefined brake acceleration, and the ego vehicle experiencing a maximum jerk.
Aine teaches wherein the trajectory modifier includes at least one of the ego vehicle following the plurality of trajectories for a fixed period followed by a deceleration along the plurality of trajectories, the ego vehicle following the plurality of trajectories for a predefined duration, the ego vehicle experiencing a predefined brake acceleration, and the ego vehicle experiencing a maximum jerk (See at least Para [0030] “… For example, the tolerance of motion plan may include headway maintained and/or tolerance over comfort constraints on the motion of the ego vehicle (e.g., maximum jerk).”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Aine and include the feature of the trajectory modifier including the ego vehicle experiencing a maximum jerk, thereby enhance safety (See at least Para [0017] “… A minimum headway constraint may be a design feature of a vehicle control system added to enhance safety…”).
Claim 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael), and further in view of Batkovic et al. (US 20210078596 A1) (Hereinafter Batkovic).
Regarding Claim 7, modified Dolben teaches all the elements of claim 1.
However, Dolben does not explicitly spell out wherein the plurality of safety parameters further includes a predefined time horizon and/or a predefined downsampling of the time horizon.
Batkovic teaches wherein the plurality of safety parameters further includes a predefined time horizon and/or a predefined downsampling of the time horizon (See at least Para [0010] “According to a first aspect of the present disclosure, there is provided a method for trajectory planning for a vehicle. The method comprises obtaining a reference trajectory over a finite time horizon”, Para [0008] “More specifically, it is an object of the present disclosure to provide a method for trajectory planning for a vehicle which allows the vehicle to drive at higher speeds while still ensuring safety in an improved manner as compared to currently known solutions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Batkovic and include the feature of plurality of safety parameters including a predefined time horizon and/or a predefined downsampling of the time horizon, thereby provide enhanced safety Para [0008] “More specifically, it is an object of the present disclosure to provide a method for trajectory planning for a vehicle which allows the vehicle to drive at higher speeds while still ensuring safety in an improved manner as compared to currently known solutions.”, Para [0011] “In accordance with the proposed method, the back-up stop part mainly serves to ensure that the vehicle is able to reach a safe state (e.g. standstill) within the finite time horizon.”).
Regarding Claim 17, modified Dolben teaches all the elements of claim 11.
However, Dolben does not explicitly spell out wherein the plurality of safety parameters further includes a predefined time horizon and/or a predefined downsampling of the time horizon.
Batkovic teaches wherein the plurality of safety parameters further includes a predefined time horizon and/or a predefined downsampling of the time horizon (See at least Para [0010] “According to a first aspect of the present disclosure, there is provided a method for trajectory planning for a vehicle. The method comprises obtaining a reference trajectory over a finite time horizon”, Para [0008] “More specifically, it is an object of the present disclosure to provide a method for trajectory planning for a vehicle which allows the vehicle to drive at higher speeds while still ensuring safety in an improved manner as compared to currently known solutions.”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Batkovic and include the feature of plurality of safety parameters including a predefined time horizon and/or a predefined downsampling of the time horizon, thereby provide enhanced safety Para [0008] “More specifically, it is an object of the present disclosure to provide a method for trajectory planning for a vehicle which allows the vehicle to drive at higher speeds while still ensuring safety in an improved manner as compared to currently known solutions.”, Para [0011] “In accordance with the proposed method, the back-up stop part mainly serves to ensure that the vehicle is able to reach a safe state (e.g. standstill) within the finite time horizon.”).
Claim 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael), and further in view of Neo et al. (WO 2019151942 A1) (Hereinafter Neo).
Regarding Claim 8, modified Dolben teaches all the elements of claim 5. Dolben further teaches wherein the predefined assumption includes a plurality of predefined assumptions, wherein the safety check includes a plurality of safety checks (See at least Para [0003] “…A set of trajectories can be generated based on the one or more behavior constraints…”, discloses a set of trajectories is generated based on the one or more behavior constraints which is construed as applying a plurality of safety parameters to a plurality of trajectories generated for an ego vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories, behavior constraint is considered as safety parameters comprising a predefined assumption),
However, Dolben does not explicitly spell out … and wherein the trajectory modifier includes a plurality of trajectory modifiers.
Neo teaches … and wherein the trajectory modifier includes a plurality of trajectory modifiers (See at least Page 1 Para 3 “…and obtaining a campaign value from one of applying the selected one of the plurality of modifiers to total route value obtained from the zone value of the at least one of the plurality of geographical zones entered into by the travel route…”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Neo and include the feature of the trajectory modifier including a plurality of trajectory modifiers, thereby provide different options for modifying trajectories.
Regarding Claim 18, modified Dolben teaches all the elements of claim 15. Dolben further teaches wherein the predefined assumption includes a plurality of predefined assumptions, wherein the safety check includes a plurality of safety checks (See at least Para [0003] “…A set of trajectories can be generated based on the one or more behavior constraints…”, discloses a set of trajectories is generated based on the one or more behavior constraints which is construed as applying a plurality of safety parameters to a plurality of trajectories generated for an ego vehicle, wherein the plurality of safety parameters comprises a predefined assumption associated with non-ego vehicles along the plurality of trajectories, behavior constraint is considered as safety parameters comprising a predefined assumption),
However, Dolben does not explicitly spell out … and wherein the trajectory modifier includes a plurality of trajectory modifiers.
Neo teaches … and wherein the trajectory modifier includes a plurality of trajectory modifiers (See at least Page 1 Para 3 “…and obtaining a campaign value from one of applying the selected one of the plurality of modifiers to total route value obtained from the zone value of the at least one of the plurality of geographical zones entered into by the travel route…”).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Neo and include the feature of the trajectory modifier including a plurality of trajectory modifiers, thereby provide different options for modifying trajectories.
Claim 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dolben et al. (US 20220185323 A1) (Hereinafter Dolben) in view of Michael et al. (US 20190271979 A1) (Hereinafter Michael), and further in view of Zhao et al (US 20210165375 A1) (Hereinafter Zhao).
Regarding Claim 9, modified Dolben teaches all the elements of claim 1.
Although Dolben teaches using machine learning to candidate trajectories and a trajectory can be selected from the candidate trajectories based on the trained machine learning model (See Para [0048]), however, Dolben does not explicitly spell out wherein the machine learning model is at least one of an Inverse Reinforcement Learning model, a propose-and-select model, and a classification-based model.
Zhao teaches wherein the machine learning model is at least one of an Inverse Reinforcement Learning model, a propose-and-select model, and a classification-based model (See at least Para [0006] “Figure (“FIG.”) 1 depicts a process for model training with inverse reinforcement learning of MPC according to embodiments of the present document.”)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Zhao and include the feature of the machine learning model being at least one of an Inverse Reinforcement Learning model, thereby upgrade ego navigation (See at least Para [0001] “The present disclosure relates generally to systems and methods for machine learning with improved performance, features, and uses.”).
Regarding Claim 19, modified Dolben teaches all the elements of claim 11.
Although Dolben teaches using machine learning to candidate trajectories and a trajectory can be selected from the candidate trajectories based on the trained machine learning model (See Para [0048]), however, Dolben does not explicitly spell out wherein the machine learning model is at least one of an Inverse Reinforcement Learning model, a propose-and-select model, and a classification-based model.
Zhao teaches wherein the machine learning model is at least one of an Inverse Reinforcement Learning model, a propose-and-select model, and a classification-based model (See at least Para [0006] “Figure (“FIG.”) 1 depicts a process for model training with inverse reinforcement learning of MPC according to embodiments of the present document.”)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Dolben with the teachings of Zhao and include the feature of the machine learning model being at least one of an Inverse Reinforcement Learning model, thereby upgrade ego navigation (See at least Para [0001] “The present disclosure relates generally to systems and methods for machine learning with improved performance, features, and uses.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Narang et al. (US 11,320,827 B2) teaches system and method for trajectory selection based on
uncertainty estimation for an autonomous agent
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/SHAHEDA HOQUE/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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