CTNF 19/190,548 CTNF 88920 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. This office action is in response to an application filed on 04/25/2025. The applicant submits several Information Disclosure Statements dated 06/18/2025, 06/18/2025, 07/22/2025, and 05/04/2026. The applicant does not make a claim for Foreign priority. The applicant does make a claim to Domestic priority to an application dated 05/01/2024. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1 and 11 are directed to transitory signals such as computer readable media or a set of instructions (such as a game or software per se) and are not included in the four patent eligible subject matter categories, and needs to be amended to include "a non-transitory computer readable media" if covered by the specifications. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1 - 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wilkinson US 2021/0080955 . As per claim 1, A system for local path planning, comprising: a memory storing one or more instructions; and a processor executing one or more of the instructions stored on the memory to perform: (Wilkinson paragraph 0007 discloses, “The autonomous vehicle includes one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations.”) classifying an object within an operating environment as an upper bound object, a lower bound object, or infeasible based on a distance between a bounding box associated with the object and an upper environment feature, a distance between the bounding box associated with the object and a lower environment feature, and a cost function; (Wilkinson paragraph 0026 discloses, “The vehicle computing system (e.g., the perception system) can identify one or more objects that are proximate to the autonomous vehicle based on sensor data received from the one or more sensors and/or the map data. In particular, in some implementations, the vehicle computing system can provide, for one or more of the objects, present state data that describes a current state of such object. As examples, the present state data for each object can describe an estimate of the object's: current location (also referred to as position); current speed (also referred to as velocity); current acceleration, current heading; current orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), current status (e.g., current color of a traffic light), and/or other state information.” And paragraph 0007 discloses, “The operation include determining one or more costs associated with each of the one or more branching policies. The operations include selecting a motion plan based at least in part on the one or more costs associated with each of the one or more branching policies.”) generating a boundary associated with the object and the upper environment feature or the lower environment feature based on the classification of the object and boundary points of the bounding box associated with the object; (Wilkinson paragraph 0039 discloses, “The scene data can include present and/or future state data for one or more objects. Present and/or future state data can include, for example, data describing an object's and/or an estimate of the object's: location (also referred to as position); speed (also referred to as velocity); acceleration, heading; orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), status (e.g., color of a traffic light), and/or other state information at one or more present and/or future points in time. In some implementations, the scene can be represented as Partially Observable Markov Decision Processes (POMDP). For instance, the scene can be defined in terms of parameters including a state space S, action space A, transition model T, reward function R, observation space O, and observation model Z.”) and generating a local path planning trajectory for a vehicle based on the classification of the object and the boundary associated with the object. (Wilkinson paragraph 0059 discloses, “the motion planning system can identify one or more viable travel decisions for navigating the surrounding environment of the vehicle based on the scene data. In some implementations, the motion planning system can generate at least one branching policy for each viable travel decision. For example, each branching policy can correspond to a travel decision group. The branching policy can include a series of state changes with respect to the one or more objects associated with each subproblem based at least in part on one travel decision or travel decision group. For example, the motion planning system can generate a branching policy by determining one or more predicted states or actions based at least in part on the one or more travel decisions associated with the branching policy.”) As per claim 2, The system for local path planning of claim 1, wherein the generating the local path planning trajectory for the vehicle is based on transforming a kinematic model from a space-time domain to a space-only domain. (Wilkinson paragraph 0104 discloses, “the prediction system 126 can generate prediction data 132 associated with each of the respective one or more objects proximate to the vehicle 102. The prediction data 132 can be indicative of one or more predicted future locations of each respective object. The prediction data 132 can be indicative of a predicted path (e.g., predicted trajectory) of at least one object within the surrounding environment of the vehicle 102. For example, the predicted path (e.g., trajectory) can indicate a path along which the respective object is predicted to travel over time (and/or the velocity at which the object is predicted to travel along the predicted path).”) As per claim 3, The system for local path planning of claim 2, wherein the kinematic model is a non-linear kinematic bicycle model. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 4, The system for local path planning of claim 1, wherein the generating the local path planning trajectory for the vehicle is performed within a Frenet frame. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 5, The system for local path planning of claim 1, wherein the generating the local path planning trajectory for the vehicle is based on obtaining kinematics of a kinematic model in a space-only domain in terms of a longitudinal distance step. (Wilkinson paragraph 0103 discloses, “the perception system 124 can update the state data 130 for each object at each iteration. Thus, the perception system 124 can detect and track objects (e.g., vehicles, bicycles, pedestrians, etc.) that are proximate to the vehicle 102 over time, and thereby produce a presentation of the world around a vehicle 102 along with its state (e.g., a presentation of the objects of interest within a scene at the current time along with the states of the objects).”) As per claim 6, The system for local path planning of claim 5, wherein the obtaining kinematics of the kinematic model includes applying a curvature-based model correction. (Wilkinson paragraph 0054 discloses, “In some implementations, however, subproblems can be defined along two-dimensional paths. For example, a path curvature of a path traveled by an autonomous vehicle can be compared to reference paths which have pre-computed solutions to find the pre-computed solutions for the most similar reference path. Additionally, and/or alternatively, path curvature can be represented as speed constraints (e.g., speed constraints applied to pre-computed one-dimensional solutions, etc.).”) As per claim 7, The system for local path planning of claim 1, wherein the cost function is based on a deviation from a reference path from a start region to a goal region, a steering effort, a local path planning trajectory curvature, and a distance to an environment boundary. (Wilkinson paragraph 0107 discloses, “the motion planning system 128 and/or the decision system 150 can determine a cost function for each of one or more candidate motion plans for the autonomous vehicle 102 based at least in part on the current locations and/or predicted future locations and/or moving paths of the objects. For example, the cost function can describe a cost (e.g., over time) of adhering to a particular candidate motion plan. For example, the cost described by a cost function can increase when the vehicle 102 approaches impact with another object and/or deviates from a preferred pathway (e.g., a predetermined travel route).” And paragraph 0108 discloses, “The motion planning system 128 then can provide the selected motion plan to a vehicle controller 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.”) As per claim 8, The system for local path planning of claim 1, wherein when the object is a dynamic object, a predicted position for the object over a time horizon is included as a convex cost in the cost function. (Wilkinson paragraph 0103 discloses, “the state data 130 for each object can describe an estimate of the object's: current location (also referred to as position); current speed; current heading (which may also be referred to together as velocity); current acceleration; current orientation; size/footprint (e.g., as represented by a bounding shape such as a bounding polygon or polyhedron); class of characterization (e.g., vehicle class versus pedestrian class versus bicycle class versus other class); yaw rate; and/or other state information. In some implementations, the perception system 124 can determine state data 130 for each object over a number of iterations.”) As per claim 9, The system for local path planning of claim 1, wherein the generating the local path planning trajectory for the vehicle is based on a bounded slack variable accounting for perception noise. (Wilkinson paragraph 0030 discloses, “An aleatoric uncertainty represents an inherent noise in observations, such as present and/or the future state data. By way of example, aleatoric uncertainty can result from noise in components of the vehicle computing system, such as sensor noise, motion noise (e.g., resulting from movement, vibrations, etc. of the autonomous vehicle and/or of one or more components of the autonomous vehicle), transmission noise (e.g., between two components of the autonomous vehicle), and/or electrical noise. Because such uncertainties are included in the operation of electrical components, additional data collection generally does not reduce aleatoric uncertainty.”) As per claim 10, The system for local path planning of claim 1, comprising an actuator implementing the local path planning trajectory for the vehicle. (Wilkinson paragraph 0108 discloses, “The motion planning system 128 then can provide the selected motion plan to a vehicle controller 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.”) As per claim 11, A local path planning vehicle, comprising: a memory storing one or more instructions; a processor executing one or more of the instructions stored on the memory to perform: (Wilkinson paragraph 0007 discloses, “The autonomous vehicle includes one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations.”) classifying an object within an operating environment as an upper bound object, a lower bound object, or infeasible based on a distance between a bounding box associated with the object and an upper environment feature, a distance between the bounding box associated with the object and a lower environment feature, and a cost function; (Wilkinson paragraph 0026 discloses, “The vehicle computing system (e.g., the perception system) can identify one or more objects that are proximate to the autonomous vehicle based on sensor data received from the one or more sensors and/or the map data. In particular, in some implementations, the vehicle computing system can provide, for one or more of the objects, present state data that describes a current state of such object. As examples, the present state data for each object can describe an estimate of the object's: current location (also referred to as position); current speed (also referred to as velocity); current acceleration, current heading; current orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), current status (e.g., current color of a traffic light), and/or other state information.” And paragraph 0007 discloses, “The operation include determining one or more costs associated with each of the one or more branching policies. The operations include selecting a motion plan based at least in part on the one or more costs associated with each of the one or more branching policies.”) generating a boundary associated with the object and the upper environment feature or the lower environment feature based on the classification of the object and boundary points of the bounding box associated with the object; (Wilkinson paragraph 0039 discloses, “The scene data can include present and/or future state data for one or more objects. Present and/or future state data can include, for example, data describing an object's and/or an estimate of the object's: location (also referred to as position); speed (also referred to as velocity); acceleration, heading; orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), status (e.g., color of a traffic light), and/or other state information at one or more present and/or future points in time. In some implementations, the scene can be represented as Partially Observable Markov Decision Processes (POMDP). For instance, the scene can be defined in terms of parameters including a state space S, action space A, transition model T, reward function R, observation space O, and observation model Z.”) and generating a local path planning trajectory for the local path planning vehicle based on the classification of the object, the boundary associated with the object, and transforming a kinematic model from a space-time domain to a space-only domain; (Wilkinson paragraph 0059 discloses, “the motion planning system can identify one or more viable travel decisions for navigating the surrounding environment of the vehicle based on the scene data. In some implementations, the motion planning system can generate at least one branching policy for each viable travel decision. For example, each branching policy can correspond to a travel decision group. The branching policy can include a series of state changes with respect to the one or more objects associated with each subproblem based at least in part on one travel decision or travel decision group. For example, the motion planning system can generate a branching policy by determining one or more predicted states or actions based at least in part on the one or more travel decisions associated with the branching policy.”) and an actuator implementing the local path planning trajectory for the local path planning vehicle. (Wilinson paragraph 0077 discloses, “the motion plan can be implemented to navigate the autonomous vehicle. For example, the motion planning system can be provided for use in controlling a motion of the vehicle. For instance, in some implementations, one or more control actions can be determined based on the motion plan. In some implementations, the one or more control actions can be implemented by the autonomous vehicle (e.g., by a vehicle controller, etc.) to navigate the autonomous vehicle.”) As per claim 12, The local path planning vehicle of claim 11, wherein the kinematic model is a non-linear kinematic bicycle model. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 13, The local path planning vehicle of claim 11, wherein the generating the local path planning trajectory for the vehicle is performed within a Frenet frame. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 14, The local path planning vehicle of claim 11, wherein the generating the local path planning trajectory for the local path planning vehicle is based on obtaining kinematics of the kinematic model in terms of a longitudinal distance step. (Wilkinson paragraph 0103 discloses, “the perception system 124 can update the state data 130 for each object at each iteration. Thus, the perception system 124 can detect and track objects (e.g., vehicles, bicycles, pedestrians, etc.) that are proximate to the vehicle 102 over time, and thereby produce a presentation of the world around a vehicle 102 along with its state (e.g., a presentation of the objects of interest within a scene at the current time along with the states of the objects).”) As per claim 15, The local path planning vehicle of claim 14, wherein the obtaining kinematics of the kinematic model includes applying a curvature-based model correction. (Wilkinson paragraph 0054 discloses, “In some implementations, however, subproblems can be defined along two-dimensional paths. For example, a path curvature of a path traveled by an autonomous vehicle can be compared to reference paths which have pre-computed solutions to find the pre-computed solutions for the most similar reference path. Additionally, and/or alternatively, path curvature can be represented as speed constraints (e.g., speed constraints applied to pre-computed one-dimensional solutions, etc.).”) As per claim 16, A computer-implemented method for local path planning, comprising: classifying an object within an operating environment as an upper bound object, a lower bound object, or infeasible based on a distance between a bounding box associated with the object and an upper environment feature, a distance between the bounding box associated with the object and a lower environment feature, and a cost function; (Wilkinson paragraph 0026 discloses, “The vehicle computing system (e.g., the perception system) can identify one or more objects that are proximate to the autonomous vehicle based on sensor data received from the one or more sensors and/or the map data. In particular, in some implementations, the vehicle computing system can provide, for one or more of the objects, present state data that describes a current state of such object. As examples, the present state data for each object can describe an estimate of the object's: current location (also referred to as position); current speed (also referred to as velocity); current acceleration, current heading; current orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), current status (e.g., current color of a traffic light), and/or other state information.” And paragraph 0007 discloses, “The operation include determining one or more costs associated with each of the one or more branching policies. The operations include selecting a motion plan based at least in part on the one or more costs associated with each of the one or more branching policies.”) generating a boundary associated with the object and the upper environment feature or the lower environment feature based on the classification of the object and boundary points of the bounding box associated with the object; (Wilkinson paragraph 0039 discloses, “The scene data can include present and/or future state data for one or more objects. Present and/or future state data can include, for example, data describing an object's and/or an estimate of the object's: location (also referred to as position); speed (also referred to as velocity); acceleration, heading; orientation; size/footprint (e.g., as represented by a bounding polygon); class (e.g., vehicle vs. pedestrian vs. bicycle), status (e.g., color of a traffic light), and/or other state information at one or more present and/or future points in time. In some implementations, the scene can be represented as Partially Observable Markov Decision Processes (POMDP). For instance, the scene can be defined in terms of parameters including a state space S, action space A, transition model T, reward function R, observation space O, and observation model Z.”) and generating a local path planning trajectory for a vehicle based on the classification of the object and the boundary associated with the object. (Wilkinson paragraph 0059 discloses, “the motion planning system can identify one or more viable travel decisions for navigating the surrounding environment of the vehicle based on the scene data. In some implementations, the motion planning system can generate at least one branching policy for each viable travel decision. For example, each branching policy can correspond to a travel decision group. The branching policy can include a series of state changes with respect to the one or more objects associated with each subproblem based at least in part on one travel decision or travel decision group. For example, the motion planning system can generate a branching policy by determining one or more predicted states or actions based at least in part on the one or more travel decisions associated with the branching policy.”) As per claim 17, The computer-implemented method for local path planning of claim 16, wherein the generating the local path planning trajectory for the vehicle is based on transforming a kinematic model from a space-time domain to a space-only domain. (Wilkinson paragraph 0104 discloses, “the prediction system 126 can generate prediction data 132 associated with each of the respective one or more objects proximate to the vehicle 102. The prediction data 132 can be indicative of one or more predicted future locations of each respective object. The prediction data 132 can be indicative of a predicted path (e.g., predicted trajectory) of at least one object within the surrounding environment of the vehicle 102. For example, the predicted path (e.g., trajectory) can indicate a path along which the respective object is predicted to travel over time (and/or the velocity at which the object is predicted to travel along the predicted path).”) As per claim 18, The computer-implemented method for local path planning of claim 17, wherein the kinematic model is a non-linear kinematic bicycle model. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 19, The computer-implemented method for local path planning of claim 16, wherein the generating the local path planning trajectory for the vehicle is performed within a Frenet frame. (Wilkinson paragraph 0168 discloses, “the decision system 150 can send the optimal travel path through a nonlinear optimization strategy such as a frenet frame optimization strategy that draws discrete samples from a parametric frenet frame sampling strategy.”) As per claim 20, The computer-implemented method for local path planning of claim 16, wherein the generating the local path planning trajectory for the vehicle is based on obtaining kinematics of a kinematic model in a space-only domain in terms of a longitudinal distance step. (Wilkinson paragraph 0103 discloses, “the perception system 124 can update the state data 130 for each object at each iteration. Thus, the perception system 124 can detect and track objects (e.g., vehicles, bicycles, pedestrians, etc.) that are proximate to the vehicle 102 over time, and thereby produce a presentation of the world around a vehicle 102 along with its state (e.g., a presentation of the objects of interest within a scene at the current time along with the states of the objects).”) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER D PAIGE whose telephone number is (571)270-5425. The examiner can normally be reached M-F 7:00am - 6:00pm (mst). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kito Robinson can be reached at 5712703921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TYLER D PAIGE/Primary Examiner, Art Unit 3664 Application/Control Number: 19/190,548 Page 2 Art Unit: 3664 Application/Control Number: 19/190,548 Page 3 Art Unit: 3664 Application/Control Number: 19/190,548 Page 4 Art Unit: 3664 Application/Control Number: 19/190,548 Page 5 Art Unit: 3664 Application/Control Number: 19/190,548 Page 6 Art Unit: 3664 Application/Control Number: 19/190,548 Page 7 Art Unit: 3664 Application/Control Number: 19/190,548 Page 8 Art Unit: 3664 Application/Control Number: 19/190,548 Page 9 Art Unit: 3664 Application/Control Number: 19/190,548 Page 10 Art Unit: 3664 Application/Control Number: 19/190,548 Page 11 Art Unit: 3664 Application/Control Number: 19/190,548 Page 12 Art Unit: 3664 Application/Control Number: 19/190,548 Page 13 Art Unit: 3664 Application/Control Number: 19/190,548 Page 14 Art Unit: 3664 Application/Control Number: 19/190,548 Page 15 Art Unit: 3664
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