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 .
Application status
This office action is in response to application filed on 09/23/2024. Claims 1-15 are pending. Claims 1-15 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the parent Application No. DE10 2023 209 460.6 filed on 09/27/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/23/2024 and 10/31/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, all the limitations in claim 5-13 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
More specifically, following limitations must be shown with the drawings;
wherein the first and second scene-specific data is generated from at least one of the following sources: sensor-based environment perception of the vehicle, map data, aggregated environment data from a number of different data sources.
wherein each of the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer, is configured to aggregate and model an environment of the vehicle using a modeling module, to examine control signals received from other layers using a verification module and in each case to create layer-specific trajectory planning for the vehicle using a planning module, which layer-specific trajectory planning can be used as a specification for a layer arranged directly below it.
wherein each layer, which is directly subordinate to another layer, is configured to overwrite a control signal received and serving as a specification from the layer directly superordinate to it, when the received control signal is in conflict with layer-specific trajectory planning.
wherein the first hardware-based layer is configured to process sensor-specific data of the vehicle using a sensor module and to provide the processed sensor specific data to layers arranged above it, to examine sensor signals received from the layers located above it using a check module, and to directly access a control system of the vehicle, in order to execute a trajectory planning transmitted by the second reflex-based layer located directly above it using an execution module.
wherein the second reflex-based layer is configured to determine, based on a localization unit, a deviation between a trajectory planning transmitted by the third motion-based layer arranged directly above it and the layer-specific trajectory planning of the second reflex-based layer.
wherein the third motion-based layer is configured to create a trajectory planning for the second reflex-based layer arranged directly below it based on an environment model implemented in the second reflex-based layer, wherein the trajectory planning of the third layer provides for planning alternative trajectories for the vehicle), which take into account: (i) safety-relevant parameters and/or (ii) vehicle-relevant parameters and/or (iii) environment-relevant parameters.
wherein the fourth intent-based layer is configured to undertake trajectory planning based on the first scene-specific data, which takes into account an interaction of the vehicle with an environment of the vehicle.
wherein the second scene-specific data of the fifth context-based layer includes data of a driving situation of the vehicle, which extends over a longer planning horizon than a planning horizon for generating the first scene-specific data.
wherein respective safety requirements of the first hardware-based layer, the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer decrease as the layer hierarchy increases.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 14 and 15 are objected to because of the following informalities:
“Data carrier” is inconsistent with “non-transitory” and “stored”, as recited in claim 14 and 15. For example, a wire or RF signal per se, could be “data carriers”
Appropriate correction is required.
When claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented.
Misnumbered claim 10 should has been renumbered as 3,
Misnumbered claim 3 should has been renumbered as 4,
Misnumbered claim 11 should has been renumbered as 5,
Misnumbered claim 4 should has been renumbered as 6,
Misnumbered claim 5 should has been renumbered as 7,
Misnumbered claim 7 should has been renumbered as 8,
Misnumbered claim 8 should has been renumbered as 9,
Misnumbered claim 12 should has been renumbered as 10,
Misnumbered claim 13 should has been renumbered as 11,
Misnumbered claim 6 should has been renumbered as 12, and
Misnumbered claim 9 should has been renumbered as 13.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6-7 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “specification” in claim 6 and 7 is a relative term which renders the claim indefinite. The term “specification” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear that the “specification” is referring to description, command, a specific step or condition, or other related meaning.
The term “Safety requirements” in claim 13 is a relative term which renders the claim indefinite. The term “Safety requirements” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear that the “Safety requirements” is referring to specific safety parameters of the vehicle, specific safety parameters of the surrounding environment of the vehicle, specific safety requirements of the hierarchical system, or other related requirements. Furthermore, specific type of safety requirements or examples of the safety requirements need to be shown or provided for clear understanding of the “safety requirements”.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claim(s) 1-6, 8-11, and 14-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Maniatopoulos (US 20240219906 A1).
Regarding claim 1, Maniatopoulos teaches A hierarchical system for controlling an automated vehicle (Maniatopoulos, at least one para. 0013; “the AV stack or AV compute process may communicate with various hardware components (e.g., on-board sensors and control system of the AV) and/or with an AV infrastructure over a network. In some cases, the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack. The planning stack may generate one or more plans and transmit the one or more plans to the control stack.”), comprising:
a first hardware-based layer, which is configured to receive a first control signal and to carry out a conversion in terms of control technology of the first received control signal, or another base layer (Maniatopoulos, at least one para. 0063; “The control stack 618 may manage the operation of the vehicle propulsion system 630, the braking system 632, the steering system 634, the safety system 636, and the cabin system 638.”); and
a second reflex-based layer (Maniatopoulos, at least one para. 0062; “The planning stack 616 may determine how to maneuver or operate the AV 602 safely and efficiently in its environment. For instance, the planning stack 616 may produce a plan for the AV 602”), which is hierarchically superordinate to the first hardware layer and which is configured to receive a control signal from at least one further superordinate layer and to convert the received control signal into the first control signal for the first hardware-based layer (Maniatopoulos, at least one para. 0063; “Control stack 618 may receive a plan from the planning stack 616.”), wherein the second reflex-based layer is further configured to intervene based on first scene-specific data (Maniatopoulos, at least one para. 0062; “the planning stack 616 may receive the location, speed, and direction of the AV 602, geospatial data, data regarding objects sharing the road with the AV 602 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., an Emergency Vehicle (EMV) blaring a siren, intersections, occluded areas, street closures for construction or street repairs, DPVs, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV 602 from one point to another.”).
Regarding claim 2, Maniatopoulos teaches The hierarchical system according to claim 1, further comprising: a third motion-based layer, which is hierarchically superordinate to the second reflex-based layer and which is configured to receive a base trajectory from an overlying layer and to convert the received based trajectory into a second control signal for the second reflex-based layer, and wherein the third motion-based layer is configured to intervene based on the first scene-specific data (Maniatopoulos, at least one para. 0062; “The planning stack 616 may determine how to maneuver or operate the AV 602 safely and efficiently in its environment. For instance, the planning stack 616 may produce a plan for the AV 602, which can include a (reference) trajectory.”).
Regarding claim 3, Maniatopoulos teaches The hierarchical system according to claim 2, further comprising: a fourth intent-based layer, which is configured to generate the base trajectory for the third motion-based layer based on the first scene-specific data and to transmit the base trajectory to the third motion-based layer via a third control signal (Maniatopoulos, at least one para. 0021; “For instance, one planner may be specialized in generating paths for the AV in structured, nominal driving (e.g., tasks or scenarios that involve the AV driving forward). Structured, nominal driving may involve path planning based on a detailed, lane-level map and detected objects of the AV's surroundings. Another planner may be specialized in generating paths for the AV in unstructured, freespace driving. Unstructured, freespace driving may involve collision-free and safe path planning based on sensor data and potentially without a detailed, lane-level map of the AV's surroundings. Yet another planner may be specialized in generating paths for the AV to drive in reverse. Yet a further planner may be specialized in producing instructions for tasks or scenarios that involve the AV staying still. Other planners may be specialized in generating paths for completing other tasks such as: parking, maneuvering around inside a building structure, pulling over, driving on a highway, driving on a freeway, driving off-road, driving in inclement weather conditions, etc.”).
Regarding claim 4, Maniatopoulos teaches The hierarchical system according to claim 3, further comprising: a fifth context-based layer, which is configured to generate a fourth control signal for the fourth intent-based layer based on second scene-specific data (Maniatopoulos, at least one para. 0023; “Path follower 180 may generate a local path for the vehicle to take. The local path may be optimized based on tracking error of the local path relative to a reference trajectory in the output plan received from the unified interface 108. The local path may include a corrective action to get the AV 130 to converge on and stick to the reference trajectory of the received plan when the AV deviates from the reference trajectory. Path follower 180 may produce a local path that follows the reference trajectory of the received plan as closely as possible, given certain constraint(s). Constraints can include: comfort, speed, feasibility, lateral acceleration, curvature, curvature rate, lateral jerk, etc.”).
Regarding claim 5, Maniatopoulos teaches The hierarchical system according to claim 4, wherein the first and second scene-specific data is generated from at least one of the following sources: sensor-based environment perception of the vehicle, map data (Maniatopoulos, at least one para. 0062; “the planning stack 616 may receive the location, speed, and direction of the AV 602, geospatial data, data regarding objects sharing the road with the AV 602 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., an Emergency Vehicle (EMV) blaring a siren, intersections, occluded areas, street closures for construction or street repairs, DPVs, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV 602 from one point to another.”, wherein explained first scene-specific data) and (Maniatopoulos, at least one para. 0023; “Path follower 180 may produce a local path that follows the reference trajectory of the received plan as closely as possible, given certain constraint(s). Constraints can include: comfort, speed, feasibility, lateral acceleration, curvature, curvature rate, lateral jerk, etc.”, wherein explained second scene-specific data), aggregated environment data from a number of different data sources (Maniatopoulos, at least one para. 0068; “The data center 650 may include one or more computing devices remote to the local computing device 610 for managing a fleet of AVs and AV-related services. For example, in addition to managing the AV 602, the data center 650 may also support a ridesharing service, a delivery service, a remote/roadside assistance service, street services (e.g., street mapping, street patrol, street cleaning, street metering, parking reservation, etc.), and the like.”).
Regarding claim 6, Maniatopoulos teaches The hierarchical system according claim 4, wherein each of the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer, is configured to aggregate and model an environment of the vehicle using a modeling module (Maniatopoulos, at least one para. 0060; “the perception stack 612 may determine the free space around the AV 602 (e.g., to maintain a safe distance from other objects, change lanes, park the AV, etc.). The perception stack 612 may also identify environmental uncertainties, such as where to look for moving objects, flag areas that may be obscured or blocked from view, and so forth.”), to examine control signals received from other layers using a verification module and in each case to create layer-specific trajectory planning for the vehicle using a planning module (Maniatopoulos, at least one para. 0037; “FIG. 4 illustrates an exemplary unified interface that includes a plan checker, according to some aspects of the disclosed technology. Being placed between upstream planners and downstream controls, the unified interface 108 may be implemented with a plan checker 404 to ensure that the plans to be sent to controls 110 are feasible and consistent.”), which layer-specific trajectory planning can be used as a specification for a layer arranged directly below it (Maniatopoulos, at least one para. 0038; “In some embodiments, plan checker 404 may determine if the vehicle can properly failover from executing a plan generated by one of the primary planners to a plan generated by the fallback planner 306”).
Regarding claim 8, Maniatopoulos teaches The hierarchical system according to claim 4, wherein the first hardware-based layer is configured to process sensor-specific data of the vehicle using a sensor module (Maniatopoulos, at least one para. 0057; “AV 602 may navigate about roadways without a human driver based on sensor signals generated by multiple sensor systems 604, 606, and 608. The sensor systems 604-608 may include different types of sensors and may be arranged about the AV 602. ”) and to provide the processed sensor specific data to layers arranged above it, to examine sensor signals received from the layers located above it using a check module, and to directly access a control system of the vehicle, in order to execute a trajectory planning transmitted by the second reflex-based layer located directly above it using an execution module (Maniatopoulos, at least one para. 0063; “Control stack 618 may include controls 110 in the Figures. The control stack 618 may receive sensor signals from the sensor systems 604-608 as well as communicate with other stacks or components of the local computing device 610 or a remote system (e.g., the data center 650) to effectuate the operation of the AV 602.”).
Regarding claim 9, Maniatopoulos teaches The hierarchical system according to claim 6, wherein the second reflex-based layer is configured to determine, based on a localization unit, a deviation between a trajectory planning transmitted by the third motion-based layer arranged directly above it and the layer-specific trajectory planning of the second reflex-based layer (Maniatopoulos, at least one para. 0061; “Mapping and localization stack 614 may determine the AV's position and orientation (pose) using different methods from multiple systems (e.g., GPS, IMUs, cameras, LIDAR, RADAR, ultrasonic sensors, the HD geospatial database 622, etc.). For example, in some embodiments, the AV 602 may compare sensor data captured in real-time by the sensor systems 604-608 to data in the HD geospatial database 622 to determine its precise (e.g., accurate to the order of a few centimeters or less) position and orientation. The AV 602 may focus its search based on sensor data from one or more first sensor systems (e.g., GPS) by matching sensor data from one or more second sensor systems (e.g., LIDAR).”).
Regarding claim 10, Maniatopoulos teaches The hierarchical system according to claim 2, wherein the third motion-based layer is configured to create a trajectory planning for the second reflex-based layer arranged directly below it based on an environment model implemented in the second reflex-based layer, wherein the trajectory planning of the third layer provides for planning alternative trajectories for the vehicle), which take into account: (i) safety-relevant parameters and/or (ii) vehicle-relevant parameters and/or (iii) environment-relevant parameters (Maniatopoulos, at least one para. 0033; “In some embodiments, the fallback manager 302 may manage the fallback planner and output a second selection signal 350 to a second switch 322 (or a second multiplexer). An output of the first switch 204 and an output of fallback planner 306 are coupled to inputs of the second switch 322. The second selection signal 350 may cause the second switch 322 to select one of the inputs to be provided as output of the second switch 322. The inputs to the second switch 322 may include an output plan from the first switch 204 and a plan generated by the fallback planner 306. If the fallback manager 302 determines that the vehicle is not in a degraded state, the second selection signal 350 may cause the second switch 322 to output the output plan from the first switch 204. If the fallback manager 302 determines that the vehicle is in a degraded state, the second selection signal 350 may cause the second switch 322 to output the output plan from the fallback planner 306. Effectively, the second selection signal 350 from the fallback manager 302 may dictate whether the fallback planner 306 is to control the vehicle, such as AV 130. The fallback manager 302 may subscribe to one or more triggers that may indicate that the vehicle is in a degraded state. If one or more triggers is active, the fallback manager 302 may generate the second selection signal 350 accordingly.”).
Regarding claim 11, Maniatopoulos teaches The hierarchical system according to claim 3, wherein the fourth intent-based layer is configured to undertake trajectory planning based on the first scene-specific data, which takes into account an interaction of the vehicle with an environment of the vehicle (Maniatopoulos, at least one para. 0021; “For instance, one planner may be specialized in generating paths for the AV in structured, nominal driving (e.g., tasks or scenarios that involve the AV driving forward). Structured, nominal driving may involve path planning based on a detailed, lane-level map and detected objects of the AV's surroundings.”).
Regarding claim 14, Maniatopoulos teaches A non-transitory machine-readable data carrier on which is stored a computer program when, when executed by one or more computers and/or computer instances, implements (Maniatopoulos, at least one para. 0083; “Embodiments within the scope of the present disclosure may also include tangible and/or non-transitory computer-readable storage media or devices for carrying or having computer-executable instructions or data structures stored thereon.”):
a hierarchical system for controlling an automated vehicle (Maniatopoulos, at least one para. 0013; “the AV stack or AV compute process may communicate with various hardware components (e.g., on-board sensors and control system of the AV) and/or with an AV infrastructure over a network. In some cases, the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack. The planning stack may generate one or more plans and transmit the one or more plans to the control stack.”), including:
a first hardware-based layer, which is configured to receive a first control signal and to carry out a conversion in terms of control technology of the first received control signal, or another base layer (Maniatopoulos, at least one para. 0063; “The control stack 618 may manage the operation of the vehicle propulsion system 630, the braking system 632, the steering system 634, the safety system 636, and the cabin system 638.”), and
a second reflex-based layer (Maniatopoulos, at least one para. 0062; “The planning stack 616 may determine how to maneuver or operate the AV 602 safely and efficiently in its environment. For instance, the planning stack 616 may produce a plan for the AV 602”), which is hierarchically superordinate to the first hardware layer and which is configured to receive a control signal from at least one further superordinate layer and to convert the received control signal into the first control signal for the first hardware-based layer (Maniatopoulos, at least one para. 0063; “Control stack 618 may receive a plan from the planning stack 616.”), wherein the second reflex-based layer is further configured to intervene based on first scene-specific data (Maniatopoulos, at least one para. 0062; “the planning stack 616 may receive the location, speed, and direction of the AV 602, geospatial data, data regarding objects sharing the road with the AV 602 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., an Emergency Vehicle (EMV) blaring a siren, intersections, occluded areas, street closures for construction or street repairs, DPVs, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV 602 from one point to another.”).
Regarding claim 15, Maniatopoulos teaches One or more computers and/or compute instances equipped with a non-transitory machine-readable data carrier on which is stored a computer program when, when executed the one or more computers and/or computer instances, implements (Maniatopoulos, at least one para. 0083; “Embodiments within the scope of the present disclosure may also include tangible and/or non-transitory computer-readable storage media or devices for carrying or having computer-executable instructions or data structures stored thereon.”):
a hierarchical system for controlling an automated vehicle (Maniatopoulos, at least one para. 0013; “the AV stack or AV compute process may communicate with various hardware components (e.g., on-board sensors and control system of the AV) and/or with an AV infrastructure over a network. In some cases, the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack. The planning stack may generate one or more plans and transmit the one or more plans to the control stack.”), including:
a first hardware-based layer, which is configured to receive a first control signal and to carry out a conversion in terms of control technology of the first received control signal, or another base layer (Maniatopoulos, at least one para. 0063; “The control stack 618 may manage the operation of the vehicle propulsion system 630, the braking system 632, the steering system 634, the safety system 636, and the cabin system 638.”), and
a second reflex-based layer (Maniatopoulos, at least one para. 0062; “The planning stack 616 may determine how to maneuver or operate the AV 602 safely and efficiently in its environment. For instance, the planning stack 616 may produce a plan for the AV 602”), which is hierarchically superordinate to the first hardware layer and which is configured to receive a control signal from at least one further superordinate layer and to convert the received control signal into the first control signal for the first hardware-based layer (Maniatopoulos, at least one para. 0063; “Control stack 618 may receive a plan from the planning stack 616.”), wherein the second reflex-based layer is further configured to intervene based on first scene-specific data (Maniatopoulos, at least one para. 0062; “the planning stack 616 may receive the location, speed, and direction of the AV 602, geospatial data, data regarding objects sharing the road with the AV 602 (e.g., pedestrians, bicycles, vehicles, ambulances, buses, cable cars, trains, traffic lights, lanes, road markings, etc.) or certain events occurring during a trip (e.g., an Emergency Vehicle (EMV) blaring a siren, intersections, occluded areas, street closures for construction or street repairs, DPVs, etc.), traffic rules and other safety standards or practices for the road, user input, and other relevant data for directing the AV 602 from one point to another.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Maniatopoulos (US 20240219906 A1) as applied to claim 1 above, and further in view of KABZAN (DE 102021132851 A1).
Regarding claim 7, Maniatopoulos teaches The hierarchical system according to claim 4, wherein each layer, which is directly subordinate to another layer (Maniatopoulos, at least one para. 0013; “the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack.”), is configured to overwrite a control signal received and serving as a specification from the layer directly superordinate to it, when the received control signal is in conflict with layer-specific trajectory planning.
Maniatopoulos does not explicitly teaches is configured to overwrite a control signal received and serving as a specification from the layer directly superordinate to it, when the received control signal is in conflict with layer-specific trajectory planning.
KABZAN, in the same field of endeavor (KABZAN, general overview; “This document presents methods, systems and devices for operating an autonomous vehicle (AV) using maneuver generation.”) teaches is configured to overwrite a control signal received and serving as a specification from the layer directly superordinate to it, when the received control signal is in conflict with layer-specific trajectory planning (KABZAN, Architecture of an autonomous vehicle; “The cost function is applied to metrics (eg, Boolean values) associated with violating and/or satisfying a hierarchy of rules in one or more rule books based on priority or relative importance.”) and (KABZAN, Example 1; “predicting, by the at least one processor, a potential collision between the vehicle and an object moving on the road segment based on the sensor data and the first trajectory; determining, by the at least one processor, a set of constraints for the vehicle to avoid the potential collision, the set of constraints being determined based on the sensor data; determining, by the at least one processor, a maneuver for the vehicle by overlaying each constraint of the set of constraints onto every other constraint of the set of constraints, the maneuver including a second trajectory independent of the plurality of trajectories; and transmitting, by the at least one processor, instructions to a control circuit of the vehicle to: overwrite the first trajectory; and traversing the road segment according to the second trajectory to perform the maneuver.”).
Maniatopoulos and KABZAN are both considered to be analogous to the claimed invention because both of them are in the same field as controlling an autonomous vehicle as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the hierarchical system of the Maniatopoulos with teaching of KABZAN. One of the ordinary skill in the art would have been motivated to make this modification so that potential collisions can be avoided (KABZAN; example 1).
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Maniatopoulos (US 20240219906 A1) as applied to claim 1 above, and further in view of GUPTA (KR 20250083227 A).
Regarding claim 12, Maniatopoulos teaches The hierarchical system (Maniatopoulos, at least one para. 0013; “the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack.”) according to claim 4, wherein the second scene-specific data of the fifth context-based layer includes data of a driving situation of the vehicle, which extends over a longer planning horizon than a planning horizon for generating the first scene-specific data.
Maniatopoulos does not explicitly teaches wherein the second scene-specific data of the fifth context-based layer includes data of a driving situation of the vehicle, which extends over a longer planning horizon than a planning horizon for generating the first scene-specific data.
GUPTA, in the same field of endeavor (GUPTA, translated copy, page 3 para. 1; “This paper describes an example of a system and technique for providing multi-policy lane change assistance for a vehicle. In some implementations, the ADAS uses a Markov decision process (MDP) with a cost-based architecture to support the evaluation and use of multi-policies to control the vehicle.”) teaches wherein the second scene-specific data of the fifth context-based layer includes data of a driving situation of the vehicle, which extends over a longer planning horizon than a planning horizon for generating the first scene-specific data (GUPTA, translated copy, page 5 para. 3; “The behavior planner (108) may receive information from the perception and map components (106) and serve as a central unit for decision making in the ADAS. This may include, but is not limited to, evaluating multiple scenarios and attempting to find an optimal behavior for the vehicle (104). As such, the operation of the behavior planner (108) may involve relatively simple vehicle dynamics, such as planning for multiple scenarios over the next few seconds (or longer or shorter periods of time).”).
Maniatopoulos and GUPTA are both considered to be analogous to the claimed invention because both of them are in the same field as controlling an autonomous vehicle as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the hierarchical system of the Maniatopoulos with teaching of GUPTA. One of the ordinary skill in the art would have been motivated to make this modification so that selected behaviors that have been determined to be superior (e.g., optimal) over other possibilities (GUPTA; translated copy, page 5 para. 3).
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Maniatopoulos (US 20240219906 A1) as applied to claim 1 above, and further in view of LEE (US 20210358296 A1).
Regarding claim 13, Maniatopoulos teaches The hierarchical system (Maniatopoulos, at least one para. 0013; “the AV stack may include a layered arrangement of stacks, including stacks such as a perception stack, a prediction stack, a planning stack, and a control stack.”) according to claim 4, wherein respective safety requirements of the first hardware-based layer, the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer decrease as the layer hierarchy increases.
Maniatopoulos does not explicitly teaches wherein respective safety requirements of the first hardware-based layer, the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer decrease as the layer hierarchy increases.
LEE, in the same field of endeavor (LEE, at least one para. 0041; “FIG. 1 illustrates an example autonomous or semi-autonomous vehicle with which embodiments of the disclosed technology may be implemented. In this example, vehicle 100 includes a computing system 110, sensors 120, AV control systems, 130 and vehicle systems 140.”) teaches wherein respective safety requirements of the first hardware-based layer, the second reflex-based layer, the third motion-based layer, the fourth intent-based layer, and the fifth context-based layer decrease as the layer hierarchy increases (LEE, at least one para. 0082; “At operation 430, the pillar features are further encoded via a feature pyramid network 340. Feature pyramid network 340 may be implemented as a feature extractor for object detection operating on a pyramid of features. Feature pyramid network 340 may include bottom-up and top-down pathways. The bottom-up pathway is the usual convolutional network for feature extraction. Moving up the bottom-up pathway, the spatial resolution decreases, but the semantic value for each layer increases. Moving down the top-down pathway, the spatial resolution increases, but the semantic value for each layer decreases.”).
Maniatopoulos and LEE are both considered to be analogous to the claimed invention because both of them are in the same field as controlling an autonomous vehicle as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the hierarchical system of the Maniatopoulos with teaching of LEE. One of the ordinary skill in the art would have been motivated to make this modification so that the embodiments may be configured to perform self-supervised learning for multiple hierarchical resolutions and minimize losses for each hierarchical resolutions (LEE; 0114).
Conclusion
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/U.P.C./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665