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 . Claims 1-30 were previously pending. Claims 1, 5, 7-9, 12, 15-16, 22-23, 27, and 29 have been amended. No claims have been cancelled or newly added. Accordingly, claims 1-30 remain pending and have been examined in this application.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a method or an apparatus, which is one of the statutory categories of invention. (Step 1: YES). Independent claim 1 recites the following limitations (bolded text corresponds to the abstract idea):
A method of operating an autonomous vehicle (AV), comprising:
scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework; and
generating, using a neural motion planner and based at least on the UPC framework and a rule hierarchy of the UPC framework, a planned trajectory for the AV.
The limitations of (i) scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework, (ii) generating, based at least on the UPC framework and a rule hierarchy of the UPC framework, a planned trajectory for the AV, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “using a neural motion planner” for step (ii), nothing in the claim element precludes the step from practically being performed in the human mind. For example, scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework encompasses a person organizing rules into a rulebook or framework. Additionally, but for the “using a neural motion planner” language, generating based at least on the UPC framework and a rule hierarchy of the UPC framework, a planned trajectory for the AV encompasses a person using the organized rules to determine a trajectory for the AV. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. (Step2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are
not indicative of integration into a practical application include: (1) Adding the words "apply it"
(or an equivalent) with the judicial exception, or mere instructions to implement an abstract
idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP
2106.05.f), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP
2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological
environment or field of use (MPEP 2106.05.h).
In particular, the claims recite additional elements of a neural motion planner. The neural motion planner is recited at a high-level of generality (i.e., as a generic neural motion planner) such that it amounts to no more than mere instructions to apply the exception using a generic neural planner. The claim also recites the additional element of “an autonomous vehicle”. The abstract idea is merely linked to a particular technological field (AVs). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When considered separately and as an ordered combination, they do not add significantly more (also known as an "inventive concept") to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “neural motion planner” to perform step (ii) amounts to no more than mere instructions to apply the exception using a generic neural planner. Mere instructions to apply an exception using a generic neural planner cannot provide an inventive concept. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of an autonomous vehicle amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, cannot provide an inventive concept- rendering the claim patent ineligible. See MPEP 2106.05(h) for more details. Thus claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claim 2-10 further define the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the aforementioned claims are not patent-eligible.
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 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.
Claims 1-3, 10-14, 21-22, and 24-30 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng (US20200159225A1) in view of Tebbens (US20220187837).
Claim 1.
Zeng teaches the following limitations:
A method of operating an autonomous vehicle (AV), comprising: scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework; (Zeng – [0088] Access to a map can enable accurate motion planning, such as by permitting the autonomous vehicle to drive according to traffic rules (e.g., stop at a red light, follow the lane, change lanes only when allowed). Towards this goal, the backbone network can exploit high-definition maps that contain information about the semantics of the scene, such as lane location, the boundary type (e.g., solid, dashed) and the location of stop signs or other signs. In some examples, the map can be rasterized to form an M-channel tensor, where each channel represents a different map element.; [0092] A final convolution layer can be applied with a filter number T, which corresponds to planning horizon. Each filter can generate a cost volume ct for a future time step t. This allows the machine-learned motion planning model 202 to evaluate the cost of any trajectory s by simply indexing in the cost volume c.)
and generating, using a neural motion planner and based at least on the UPC framework, a planned trajectory for the AV. (Zeng – [0031] The minimization can be approximated by sampling a set of physically valid trajectories, and picking the trajectory having the minimum cost using a cost volume. The cost volume can be a learned cost volume generated by a convolutional neural network backbone. The convolutional neural network can extract features from both the LIDAR data and the map data to generate a feature map; [0064] The motion planning system 160 can be configured to continuously update the vehicle's motion plan 162 and a corresponding planned vehicle motion trajectory)
Zeng does not explicitly teach the following limitations: generating, based on a rule hierarchy of the UPC framework, a planned trajectory for the AV.
However, Tebbens teaches:
generating, based on a rule hierarchy of the UPC framework, a planned trajectory for the AV. (Tebbens – [0062, 0104, 0158, 0160] 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).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zeng with Tebbens in order to implement a priority structure on a set of rules based on their relative importance in order to evaluate a trajectory of a vehicle (Tebbens – [0062])
Claim 2.
Zeng does not explicitly teach the following limitations: wherein the scalably expressing includes expressing each rule of the traffic laws as a signal temporal logic (STL) formula
However, Tebbens teaches:
wherein the scalably expressing includes expressing each rule of the traffic laws as a signal temporal logic (STL) formula. (Tebbens – [0049] The reduction step associates each interval with a violation metric used to evaluate the trajectory. In an embodiment, a signal temporal logic (STL) framework is used to specify driving rules and an arithmetic-geometric mean (AGM) framework is used to score (measure the robustness of) trajectories. The STL framework uses qualitative and quantitative semantics to assess whether and how well a trajectory follows rules in a rulebook.)
See claim 1 for a statement of obviousness rationale.
Claim 3.
Zeng does not explicitly teach the following limitations: wherein the rules are organized in the form of a hierarchy.
However, Tebbens teaches:
wherein the rules are organized in the form of a hierarchy. (Tebbens – [0062] 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)
See claim 1 for a statement of obviousness rationale.
Claim 10.
Zeng teaches the following limitations:
wherein the planned trajectory is a first planned trajectory and the generating further includes generating a second planned trajectory using a classical motion planner, fusing the first and second planned trajectories, and providing a third planned trajectory based on the fusing. (Zeng – [0030] The trajectory generator can index the cost of each potential trajectory from different filters of the cost volume and sum them together to generate a trajectory score in some examples. The trajectory generator can select the trajectory with the minimum cost for final motion planning in some examples. In another example, the trajectory generator can optimize a single sampled trajectory using the cost volume. For example, the trajectory generator can include an optimizer that optimizes a sampled trajectory by minimizing the cost computed for the trajectory using the cost volume.)
Claim 11.
Zeng teaches the following limitations:
directing operation of the AV using the planned trajectory. (Zeng - [0049] The vehicle 102 can be configured to operate in a plurality of operating modes. For example, the vehicle 102 can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle 102 is controllable without user input (e.g., can drive and navigate with no input from a vehicle operator present in the vehicle 102 and/or remote from the vehicle 102)
Claim 12.
Zeng teaches the following limitations:
A method of operating a machine, comprising: representing operating laws in motion planning for the machine by scalably expressing the operating laws and other planning criteria in a universal planning criteria (UPC) framework (Zeng – [0088] Access to a map can enable accurate motion planning, such as by permitting the autonomous vehicle to drive according to traffic rules (e.g., stop at a red light, follow the lane, change lanes only when allowed). Towards this goal, the backbone network can exploit high-definition maps that contain information about the semantics of the scene, such as lane location, the boundary type (e.g., solid, dashed) and the location of stop signs or other signs. In some examples, the map can be rasterized to form an M-channel tensor, where each channel represents a different map element.; [0092] A final convolution layer can be applied with a filter number T, which corresponds to planning horizon. Each filter can generate a cost volume ct for a future time step t. This allows the machine-learned motion planning model 202 to evaluate the cost of any trajectory s by simply indexing in the cost volume c.)
and embedding the UPC framework in a neural motion planner; (Zeng – [0097] In some implementations, the backbone network can generate a feature map based on the sensor data and the map data and provide the feature map as input to one or more convolutional neural networks configured to generate the intermediate representations and the cost volume(s).)
generating planned trajectories by the neural motion planner using as least the UPC framework; and operating the machine using the planned trajectories. (Zeng – [0031] The minimization can be approximated by sampling a set of physically valid trajectories, and picking the trajectory having the minimum cost using a cost volume. The cost volume can be a learned cost volume generated by a convolutional neural network backbone. The convolutional neural network can extract features from both the LIDAR data and the map data to generate a feature map; [0064] The motion planning system 160 can be configured to continuously update the vehicle's motion plan 162 and a corresponding planned vehicle motion trajectory)
Zeng does not explicitly teach the following limitations: generating planned trajectories using rule hierarchy of the UPC framework.
However, Tebbens teaches:
generating planned trajectories using rule hierarchy of the UPC framework. (Tebbens – [0062, 0104, 0158, 0160] 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).)
See claim 1 for a statement of obviousness rationale.
Claim 13.
Zeng teaches the following limitations:
wherein the operating laws are traffic laws and the machine is an autonomous vehicle. (Zeng – [0088] Access to a map can enable accurate motion planning, such as by permitting the autonomous vehicle to drive according to traffic rules (e.g., stop at a red light, follow the lane, change lanes only when allowed). Towards this goal, the backbone network can exploit high-definition maps that contain information about the semantics of the scene, such as lane location, the boundary type (e.g., solid, dashed) and the location of stop signs or other signs. In some examples, the map can be rasterized to form an M-channel tensor, where each channel represents a different map element.; [Abstract] Systems and methods for generating motion plans including target trajectories for autonomous vehicles are provided.)
Claim 14.
Rejected under the same rationale as claim 2.
Claim 21.
Zeng teaches the following limitations:
wherein the machine is a robot. (Zeng – [0068] Likewise, a smart phone with one or more cameras, a robot, augmented reality system, and/or another type of system can utilize aspects of the present disclosure to generate target trajectories)
Claim 22.
Zeng teaches the following limitations:
A control system for a machine, comprising: (Zeng – [0069] The motion planning system 160 then can provide the selected motion plan to a vehicle control system 138 that controls one or more vehicle controls (e.g., actuators or other devices that control gas flow, steering, braking, etc.) to execute the selected motion plan.)
one or more processing units configured to generate planned trajectories for the machine using a neural motion planner and based at least on operating laws for the machine represented by a universal planning criteria (UPC) framework; (Zeng – [0031] The minimization can be approximated by sampling a set of physically valid trajectories, and picking the trajectory having the minimum cost using a cost volume. The cost volume can be a learned cost volume generated by a convolutional neural network backbone. The convolutional neural network can extract features from both the LIDAR data and the map data to generate a feature map; [0064] The motion planning system 160 can be configured to continuously update the vehicle's motion plan 162 and a corresponding planned vehicle motion trajectory; [0127] These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware)
and a control unit configured to receive the planned trajectories and direct operation of the machine based on the planned trajectories. (Zeng - [0049] The vehicle 102 can be configured to operate in a plurality of operating modes. For example, the vehicle 102 can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle 102 is controllable without user input (e.g., can drive and navigate with no input from a vehicle operator present in the vehicle 102 and/or remote from the vehicle 102)
Zeng does not explicitly teach the following limitations: based on rule hierarchy of the UPC framework.
However, Tebbens teaches:
based on rule hierarchy of the UPC framework. (Tebbens – [0062, 0104, 0158, 0160] 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).)
See claim 1 for a statement of obviousness rationale.
Claim 24.
Zeng teaches the following limitations:
where the one or more processing units include a graphics processing unit. (Zeng - [0127] These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware)
Claim 25.
Zeng teaches the following limitations:
wherein the machine is an autonomous vehicle. (Zeng - [Abstract] Systems and methods for generating motion plans including target trajectories for autonomous vehicles are provided.)
Claim 26.
Zeng teaches the following limitations:
wherein the one or more processing units generate the planned trajectories in real time and the control unit directs operation of the machine in real time based on the planned trajectories. (Zeng – [0044] By utilizing a machine-learned motion planning model that handles uncertainty as well as multimodality, an autonomous vehicle can increase the accuracy and efficiency of motion planning in real time and thereby increase the safety and reliability of autonomous vehicles.)
Claim 27.
Zeng teaches the following limitations:
A computer program product having a series of operating instructions stored on a non-transitory computer-readable medium that directs a data processing apparatus when executed thereby to perform operations to direct operation of an intelligent machine, the operations comprising: (Zeng – [0052] For instance, the computing device(s) can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the vehicle 102 (e.g., its computing system, one or more processors, etc.) to perform operations and functions, such as those described herein for identifying travel way features.)
scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework, (Zeng – [0088] Access to a map can enable accurate motion planning, such as by permitting the autonomous vehicle to drive according to traffic rules (e.g., stop at a red light, follow the lane, change lanes only when allowed). Towards this goal, the backbone network can exploit high-definition maps that contain information about the semantics of the scene, such as lane location, the boundary type (e.g., solid, dashed) and the location of stop signs or other signs. In some examples, the map can be rasterized to form an M-channel tensor, where each channel represents a different map element.; [0092] A final convolution layer can be applied with a filter number T, which corresponds to planning horizon. Each filter can generate a cost volume ct for a future time step t. This allows the machine-learned motion planning model 202 to evaluate the cost of any trajectory s by simply indexing in the cost volume c.)
generating, using a neural motion planner and based at least on the UPC framework, planned trajectories for the intelligent machine; and directing movement of the intelligent machine using the planned trajectories. (Zeng – [0031] The minimization can be approximated by sampling a set of physically valid trajectories, and picking the trajectory having the minimum cost using a cost volume. The cost volume can be a learned cost volume generated by a convolutional neural network backbone. The convolutional neural network can extract features from both the LIDAR data and the map data to generate a feature map; [0064] The motion planning system 160 can be configured to continuously update the vehicle's motion plan 162 and a corresponding planned vehicle motion trajectory; [0127] These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware) [0049] The vehicle 102 can be configured to operate in a plurality of operating modes. For example, the vehicle 102 can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle 102 is controllable without user input (e.g., can drive and navigate with no input from a vehicle operator present in the vehicle 102 and/or remote from the vehicle 102)
Zeng does not explicitly teach the following limitations: wherein the scalably expressing includes expressing each rule of the traffic laws as a signal temporal logic (STL) formula; and generating, based on a rule hierarchy of the UPC framework, a planned trajectory for the AV.
However, Tebbens teaches:
the scalably expressing includes expressing each rule of the traffic laws as a signal temporal logic (STL) formula. (Tebbens – [0049] The reduction step associates each interval with a violation metric used to evaluate the trajectory. In an embodiment, a signal temporal logic (STL) framework is used to specify driving rules and an arithmetic-geometric mean (AGM) framework is used to score (measure the robustness of) trajectories. The STL framework uses qualitative and quantitative semantics to assess whether and how well a trajectory follows rules in a rulebook.); generating, based on a rule hierarchy of the UPC framework, a planned trajectory for the AV. (Tebbens – [0062, 0104, 0158, 0160] 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).)
See claim 1 for a statement of obviousness rationale.
Claim 28.
Zeng teaches the following limitations:
wherein the intelligent machine is an autonomous vehicle. (Zeng - [Abstract] Systems and methods for generating motion plans including target trajectories for autonomous vehicles are provided.)
Claim 29.
Zeng teaches the following limitations:
A machine, comprising: one or more operational domains; (Zeng – [0049] In some implementations, the vehicle 102 can implement vehicle operating assistance technology (e.g., collision mitigation system, power assist steering, etc.) while in the manual operating mode to help assist the vehicle operator 106 of the vehicle 102.)
a motion planner having one or more neural networks configured to generate planned trajectories for the machine based at least on operating laws for the machine represented by a universal planning criteria (UPC) framework; (Zeng – [0031] The minimization can be approximated by sampling a set of physically valid trajectories, and picking the trajectory having the minimum cost using a cost volume. The cost volume can be a learned cost volume generated by a convolutional neural network backbone. The convolutional neural network can extract features from both the LIDAR data and the map data to generate a feature map; [0064] The motion planning system 160 can be configured to continuously update the vehicle's motion plan 162 and a corresponding planned vehicle motion trajectory)
and a control unit having one or more processors configured to receive the planned trajectories and direct operation of the one or more operational domains using commands based on the planned trajectories. (Zeng - [0049] The vehicle 102 can be configured to operate in a plurality of operating modes. For example, the vehicle 102 can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle 102 is controllable without user input (e.g., can drive and navigate with no input from a vehicle operator present in the vehicle 102 and/or remote from the vehicle 102)
Zeng does not explicitly teach the following limitations: based on rule hierarchy of the UPC framework.
However, Tebbens teaches:
based on rule hierarchy of the UPC framework. (Tebbens – [0062, 0104, 0158, 0160] 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).)
See claim 1 for a statement of obviousness rationale.
Claim 30.
Zeng teaches the following limitations:
wherein the one or more operational domains include at least one of a chassis domain, a powertrain domain, or a steering domain. (Zeng – [0049] In some implementations, the vehicle 102 can implement vehicle operating assistance technology (e.g., collision mitigation system, power assist steering, etc.) while in the manual operating mode to help assist the vehicle operator 106 of the vehicle 102.)
Claims 4-6, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng (US20200159225A1) in view of Tebbens (US20220187837), and further in view of Martin (US20240217549).
Claim 4.
Zeng does not explicitly teach the following limitations: wherein the scalably expressing further includes transforming the rules into a differentiable scalar reward function
However, Martin teaches:
wherein the scalably expressing further includes transforming the rules into a differentiable scalar reward function. (Martin – [0056] During operation of the process 400 of MCTS, for tree expansion 420, at the start of cach iteration, a TreePolicy function can determine a leaf node (e.g., nodes 450a, 450c, 450d) to expand. This leaf node can be chosen in such a way as to balance exploitation of previous state-value estimates (in the reinforcement sense) and exploration of new actions. Such a balance can be given by the upper confidence bound (UCB). The UCB formula can use an exploration constant c such that if c=0 the “best” child n.sub.c of a node n can be that which has the maximum average value, custom-character(n.sub.c)/ N(n.sub.c) where custom-character(n.sub.c) can be the total reward accumulated at node n. and N(n.sub.c) can be the number of visits of the same node)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zeng with Martin in order to provide a method for scaling computation up and down to match the desired complexity or accuracy of the solution. (Martin - [0058])
Claim 5.
Zeng does not explicitly teach the following limitations: wherein generating the planned trajectory includes using a post-hoc trajectory adjustment.
However, Martin teaches:
wherein generating the planned trajectory includes using a post-hoc trajectory adjustment. (Martin – [0027] Efficiently pruning the search space can be one approach that can help to reduce computation time. Such a pruning approach can require a careful and smart strategy for a explore and exploit tradeoff.)
See claim 4 for a statement of obviousness rationale.
Claim 6.
Zeng teaches the following limitations:
wherein the neural motion planner uses an imitation learning method (Zeng – [0044] Compared with traditional machine-learned model approaches, such as imitation learning approaches that directly regress steer angle from raw sensor data, a machine-learned model in accordance with the disclosed technology may provide interpretability and handle multi-modality naturally. For instance, when compared with traditional approaches which use manually designed cost functions built on top of perception and prediction systems, a motion planning model in accordance with the disclosed technology can provide the advantage of being jointly trained. Thus, learned representations that are more optimal for the end task of motion planning can be provided.)
Zeng does not explicitly teach the following limitations: with a UPC reward
However, Martin teaches:
wherein the neural motion planner uses an imitation learning method with a UPC reward (Martin – [0056] The UCB formula can use an exploration constant c such that if c=0 the “best” child n.sub.c of a node n can be that which has the maximum average value, custom-character(n.sub.c)/ N(n.sub.c) where custom-character(n.sub.c) can be the total reward accumulated at node n. and N(n.sub.c) can be the number of visits of the same node)
See claim 4 for a statement of obviousness rationale.
Claim 16.
Rejected under the same rationale as claim 4.
Claim 20.
Zeng does not explicitly teach the following limitations: wherein the embedding is via a pruning process of proposed trajectories.
However, Martin teaches:
wherein the embedding is via a pruning process of proposed trajectories. (Martin – [0056] The UCB formula can use an exploration constant c such that if c=0 the “best” child n.sub.c of a node n can be that which has the maximum average value, custom-character(n.sub.c)/ N(n.sub.c) where custom-character(n.sub.c) can be the total reward accumulated at node n. and N(n.sub.c) can be the number of visits of the same node)
See claim 4 for a statement of obviousness rationale.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng (US20200159225A1) in view of Tebbens (US20220187837), and further in view of Zhang (Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control).
Claim 15.
Zeng does not explicitly teach the following limitations: wherein generating the planned trajectory includes modifying a proposed trajectory using an error trace.
However, Zhang teaches: wherein generating the planned trajectory includes modifying a proposed trajectory using an error trace. (Zhang – pages 1-4 and 10 – trajectory planner and tracking controller for autonomous vehicle to implement trace planning and tracking… trace tracking error… to continuously plan new trajectory based on the current vehicle position during the moving to modify the errors between the desired position and the current position)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zeng with Zhang in order to minimize the error and obtain the desired control performance. (Zhang – page 4)
Claims 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng (US20200159225A1) in view of Tebbens (US20220187837), and further in view of Li (US20220277652).
Claim 17.
Zeng does not explicitly teach the following limitations: wherein the embedding is explicit.
However, Li teaches:
wherein the embedding is explicit. (Li – [0008] In some embodiments, the inputting the plurality current features associated with the vehicle into the trained neural network comprises: inputting the one grid cell in which the vehicle is currently located into a mask-based embedding layer of the neural network to obtain an embedded vector representation of the one grid cell)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zeng with Li in order to predict the conditional action values of repositioning options for a vehicle. (Li – [0078])
Claim 19.
Zeng does not explicitly teach the following limitations: wherein the embedding is via a UPC rule robustness vector.
However, Li teaches:
wherein the embedding is via a UPC rule robustness vector. (Li – [0080] The purpose of performing cerebellar embedding to some of the input features may include obtaining distributed, robust, and generalizable feature representations of the features. In some embodiments, to better ensure the robustness of the neural network against input perturbations)
See claim 17 for a statement of obviousness rationale.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng (US20200159225A1) in view of Tebbens (US20220187837) and further in view of Efrat (US20210276574).
Claim 18.
Zeng does not explicitly teach the following limitations: wherein the embedding is via a UPC probability vector.
However, Efrat teaches:
wherein the embedding is via a UPC probability vector. (Efrat – [Abstract] - The concatenation, or clustering is accomplished via the feature embeddings. [0060] The angle bin estimation is optimized using a soft multi-label objective, and the ground truth is calculated as the segment's angle proximity to the a bin centers, e.g. for θ.sub.seg=0 the ground truth class probability vector would be pα=(1, 0, 0, 0) and for θ.sub.seg=π the probability vector would be pα=(0.5, 0.5, 0, 0).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zeng with Efrat in order to provide a deep learning approach to unify the feature extraction process and the classification step through several layers of an artificial neural network. (Efrat – [0043])
Allowable Subject Matter
Claims 7-9 and 23 would be allowable if rewritten to include all of the limitations of the base claim and any intervening claims. Claims 7-9 and 23 are objected for its dependency on the rejected base claim, but would otherwise be allowable if rewritten to include all the limitations of the base claim and any intervening claims. The allowable subject matter found in the claim that have not been found to have been adequately taught or disclosed in the prior art found at this time are all the limitations as specifically claimed.
Response to Arguments
Applicant's arguments, see pages 8-9 filed 6/17/2027, with respect to the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive. Applicant argues that the steps of scalably expressing and generating a planned trajectory using a neural motion planner cannot be performed in the human mind. Applicant has not provided a clear argument for why these steps cannot be performed in the human mind. Applicant merely asserts that these features are not one of the recognized mental processes of observations, evaluations, judgements, or opinions. With respect to the limitation of scalably expressing traffic laws and additional planning criteria in a universal planning criteria (UPC) framework, the examiner respectfully disagrees that this cannot be performed in the human mind, as this step encompasses a person organizing rules into a rulebook or framework, which can be performed in the human mind. With respect to the limitation of generating using a neural motion planner, but for the “using a neural motion planner” language, generating based at least on the UPC framework and a rule hierarchy of the UPC framework, a planned trajectory for the AV encompasses a person using the organized rules to determine a trajectory for the AV. The neural motion planner itself is examined as an additional limitation. Upon further examination, dependent claim 11 and independent claims 12, 22, 27, and 29 perform a step of operating or directing movement of the autonomous vehicle which integrates the otherwise abstract idea into a practical application. Therefore the 35 U.S.C. 101 rejection of claims 11-30 has been withdrawn.
Applicant's arguments, see pages 9-11 filed 6/17/2026, with respect to the prior art rejections have been fully considered but they are not persuasive. Applicant asserts that Zeng does not appear to teach “using a rule hierarchy” in the amended independent claims. The examiner agrees that Zeng does not appear to teach this limitations. Applicant further asserts that Tebbens, Martin, Li, and Efrat do not cure the deficiencies of Zeng and the applied combinations fail to establish a prima facie case of obviousness. However, this is merely an assertion because Applicant provides no explanation or reasoning as to why. The examiner respectfully disagrees. Tebbens teaches the newly amended limitation in the independent claims of using a rule hierarchy.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAITLIN MCCLEARY whose telephone number is (703)756-1674. The examiner can normally be reached Monday - Friday 10:00 am - 7:00 pm.
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/CAITLIN R MCCLEARY/Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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