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 is a Final Office Action on the merits. Claims 1-21 are currently pending and are addressed below.
Response to Arguments
Applicant's arguments on pgs. 6-9 with respect to the rejection of claims 1-20 under 35 U.S.C. 103 regarding Chen failing to teach the PRA-CBF as claimed have been fully considered but they are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “certificate that a risk threshold will always be satisfied and bounds the probability of constraint violation…”, “…bound the probability of unsafe states…”, “…model the system as a stochastic differential equation with uncertainty”, “…risk specification parameter or user-specified risk tolerance”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant's arguments on pgs. 9-10 with respect to the rejection of claims 1-20 under 35 U.S.C. 103 regarding Chen failing to teach taking a predicted control input into account when assessing risks have been fully considered but they are not persuasive. Applicant argues “there is no teaching in Chen of using predicted future control inputs to assess whether a present control action is acceptable from a risk perspective” since Chen only describes a mathematical technique for obtaining control values from derivatives, rather than a risk assessment based on predicted future control inputs. Examiner respectfully disagrees. As cited in the rejection of claim 1, [0078] of Chen teaches determining an updated set of control inputs according to a set of vehicle stability constraints “in view of the operating data of the vehicle”. Chen additionally teaches that the operating data includes “a rate of change ({dot over (u)}) of a set of control inputs (u) being applied to the vehicle at a current time step”, which shows that the present control inputs are taken into account when determining updated control inputs without violating the stability constraints (See at least [0075]). Additionally, the limitation “when assessing risks associated with the present control input” appears to be intended use (via the term “when”) since it merely recites an intended scenario or condition in which the PRA-CBF determines the final control input, and because the step of assessing risks is not positively recited. The claim also does not explicitly say the final control input is based on any assessed risk, only that the risks are assessed in the determining of the final control input. Lastly, the rejection is based on the combination of Oboril and Chen, where the primary reference Oboril, as clearly cited in the rejection, teaches predicting a risk (i.e., an unsafe state at a future point in time) and determining whether to decelerate a vehicle based on that risk; therefore, the combination of Chen would simply be applying the control barrier function to determine whether to decelerate the vehicle.
Applicant further argues that Chen does not evaluate constraints over a prediction horizon and that there is no teaching of evaluating risk over a future time interval using a sequence of predicted control inputs from a planned trajectory. However, these arguments are not directed to any of the claims 1, 8, or 15 for which the Applicant requests withdrawal of these rejections, since these claims do not recite the features of evaluating risk over a future time interval using a sequence of predicted control inputs from a planned trajectory.
Applicant's arguments on pgs. 10-12 with respect to the rejection of claims 1-20 under 35 U.S.C. 103 regarding the combination of Oboril and Chen being improper and teaching away from the claimed invention have been fully considered but they are not persuasive. Applicant argues that the proposed combination lacks adequate motivation since “Oboril and Chen employ fundamentally different and incompatible approaches to vehicle control”, such that they are “architecturally different approaches that operate on different principles”. Examiner respectfully disagrees. The motivation provided is sufficient because it exhibits reasoning for combining the features of Oboril with the features of Chen. Furthermore, Oboril was used to teach the functions of determining a predicted trajectory and determining whether the vehicle needs to decelerate at a current point in time if an object is predicted to put the vehicle in an unsafe state at a future point in time, and Chen improves upon these functions by updating the control inputs of Oboril (i.e., determining whether the vehicle should decelerate) using the TCBF.
Applicant further argues that “neither reference suggests combining RSS-based prediction with control barrier functions. Oboril does not mention control barrier functions, and Chen does not mention RSS or parametric safety models. There is no teaching or suggestion in either reference that would lead a POSITA to integrate these disparate approaches”. Examiner respectfully disagrees. First, these statements are merely conclusory and provide no evidence or reasoning as to why these statements are true or why these references cannot be combined. Second, even if Oboril does not mention control barrier functions and Chen does not mention RSS or parametric safety models, Oboril was used to teach the functions of determining the predicted trajectory and whether the vehicle needs to decelerate at a current point in time based on the behavior of an object at a future point in time, and Chen modifies the functions of Oboril by providing a function or a different way of updating the control inputs.
Applicant further argues that even if Oboril and Chen are combined, it would not yield the claimed invention since “the combination of Oboril’s RSS-based trajectory prediction with Chen’s TCBF constraint enforcement would not produce a “predictive risk-aware control barrier function” that bounds the probability of unsafe states over a time interval using predicted control inputs from a planned trajectory”. However, this argument is not directed to the claims as written, since the claims do not recite bounding the probability of unsafe states over a time interval using predicted control inputs from a planned trajectory or any of these listed requirements for a PRA-CBF on pg. 11 of the response: “(1) probabilistic risk bounding with a user-specified risk parameter, (2) operation on a stochastic system model”.
Applicant further argues that Chen’s approach teaches away from the claimed invention, such that “Chen applies TCBF constraints at each time step based on the current state and control inputs, without considering predicted future control actions over a trajectory”. Examiner respectfully disagrees. [0077] of Chen recites “determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step”, which shows that a set of control inputs are updated for a future time step (i.e., to provide a “predicted control input”), and the arguments do not provide any evidence or reasoning as to why the teachings of Chen do not consider predicted future control actions over a trajectory.
Applicant's arguments on pgs. 12-13 with respect to the rejection of claims 1-20 under 35 U.S.C. 103 regarding the combination of Oboril and Chen being inoperable for its intended purpose have been fully considered but they are not persuasive. Applicant argues that the combination renders Chen inoperable because Chen recites that an advantage of the CLF-CBF method is that it “avoids additional triggers or switches between the performance and safety control algorithms”, whereas Oboril uses “discrete triggering and override mechanisms”, and “introducing Oboril’s trigger-based override would inject precisely the kind of triggers or switches that Chen’s approach is designed to avoid, fundamentally altering Chen’s principle of operation”. Examiner respectfully disagrees. Since the term “additional” suggests an extra switch or trigger beyond what is needed, the combination of Oboril and Chen is still valid because in Chen, there are no “additional” control inputs beyond what is taught in Oboril. Chen teaches a process of updating a singular set of control inputs rather than providing a new or additional set of control inputs. Furthermore, the rejection did not rely on Chen to teach a “trigger” or a “switch”, as these features were not cited in the claims.
Claim Objections
Claim 21 objected to because of the following informalities:
Claim 21 recites “sysetm”, which appears to be a typographical error.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-9, 11-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oboril of US 20210009121 A1, filed 09/24/2020, hereinafter “Oboril”, in view of Chen of US 20240075955 A1, filed 09/05/2023, hereinafter “Chen”.
Regarding claim 1, Oboril teaches:
A system, comprising: a processor; and a memory communicably coupled to the processor and storing machine-readable instructions that, when executed by the processor, cause the processor to: (See at least Abstract: “A safety device for a vehicle including a memory configured to store instructions; one or more processors coupled to the memory to execute the instructions stored in the memory…”)
predict a future state of a vehicle; (See at least [0080]: “Autonomous driving may be improved by considering a predicted trajectory of perceived objects or traffic participants. The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
plan a trajectory based on the future state of the vehicle; (See at least [0080]: “…the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…” & [0102]: “…the minimum safe distance is compared to a sensed distance between the ego vehicle and an object (e.g., another vehicle or traffic participant). A vehicle state is determined based on the comparison. If the sensed distance is less than the minimum safe distance, the vehicle is determined unsafe 710. Unsafe determination 710 triggers a vehicle response 714. For example, the ego vehicle may have to decelerate at a minimum deceleration rate…”)
determine a present control input based on the trajectory; (See at least [0080]: “…If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
Oboril does not explicitly teach:
apply a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input,
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and
execute the final control input on the vehicle.
Chen teaches:
apply a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input, (See at least [0077-0078]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…The set of vehicle stability constraints can include a first constraint (e.g., second-order TCBF outlined in sections 2 and 3 and equation (56)) that enforces a time-varying control barrier function defining time-varying safety boundaries for operation of the vehicle, the time-varying control barrier function being a second-order time-varying control barrier function that incorporates the rate of change of the set of control inputs…”)
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and (See at least [0078]: “…the time-varying safety boundaries of the first constraint ensure that the global lateral displacement value that results from the updated rate of change of the set of control inputs does not violate the set of lane boundaries…” & [0081]: “Step 206 of method 200 includes determining updated values of the set of control inputs for application to the vehicle at the future time step by integrating the updated rate of change with respect to the set of control inputs associated with the current time step…”. See also [0029] & [0031] regarding determining an updated rate of change and updated set of control inputs.)
execute the final control input on the vehicle. (See at least [0081]: “…Step 208 of method can include applying the updated set of control inputs as input to a mobility system of the vehicle.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril’s system with Chen’s technique of applying a predictive risk-aware control barrier function (PRA-CBF) to determine a final control input, where the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input, and executing the final control input. Doing so would be obvious “to achieve safety-guaranteed driving control for AVs” (See [0026] of Chen).
Regarding claim 2, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Oboril additionally teaches:
wherein the vehicle is a ground, water or airborne vehicle. (See at least [0031]: “A vehicle may be or may include an automobile…”)
Regarding claim 4, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Chen additionally teaches:
wherein the PRA-CBF applies: one or more constraints comprising at least one of an obstacle or collision avoidance, speed limit, or truck-trailer jackknife angle; and (See at least [0079]: “…For the example implementation corresponding to the emergent lane change maneuver, the control-dependent safety boundaries of the second constraint ensure that the yaw rate and the lateral velocity of the vehicle that result from the updated rate of change of the set of control inputs are within the stability region of the vehicle.”)
a finite operating time interval. (See at least [0075]: “Step 202 of method 200, a method includes accessing operating data descriptive of operation of a vehicle (e.g., vehicle 100 of FIG. 1B), including a rate of change ({dot over (u)}) of a set of control inputs (u) being applied to the vehicle at a current time step” & [0077]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…”)
Regarding claim 5, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Oboril additionally teaches:
wherein the future state of the vehicle is at least one of a location, heading, or velocity. (See at least [0080]: “…The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time. Parametric models, may determine the vehicle is in a safe state at a current point in time. However, predictive models may instruct a vehicle to decelerate to avoid a collision at a future point in time…”)
Regarding claim 6, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Chen additionally teaches:
wherein the machine-readable instruction to apply the PRA-CBF is implemented via a quadratic program. (See at least [0031]: “The vehicle safety control system 160 can apply a quadratic programming method to determine the updated derivative of the set of control inputs that satisfies the set of vehicle stability constraints in view of the operating data of the vehicle 100. The updated derivative can then be integrated to determine updated values of the set of control inputs, which can then be applied as input to the mobility system 180 of the vehicle 100.”)
Regarding claim 7, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Oboril additionally teaches:
wherein the system is an Advanced Driver Assistance System or Automated Driving System. (See at least [0061]: “…the control system 200 may include a driving model, e.g., implemented in an advanced driving assistance system (ADAS)…”)
Regarding claim 8, Oboril teaches:
A non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to: (See at least Abstract: “A safety device for a vehicle including a memory configured to store instructions; one or more processors coupled to the memory to execute the instructions stored in the memory…”)
predict a future state of a vehicle; (See at least [0080]: “Autonomous driving may be improved by considering a predicted trajectory of perceived objects or traffic participants. The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
plan a trajectory based on the future state of the vehicle; (See at least [0080]: “…the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…” & [0102]: “…the minimum safe distance is compared to a sensed distance between the ego vehicle and an object (e.g., another vehicle or traffic participant). A vehicle state is determined based on the comparison. If the sensed distance is less than the minimum safe distance, the vehicle is determined unsafe 710. Unsafe determination 710 triggers a vehicle response 714. For example, the ego vehicle may have to decelerate at a minimum deceleration rate…”)
determine a present control input based on the trajectory; (See at least [0080]: “…If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
Oboril does not explicitly teach:
apply a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input,
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and
execute the final control input on the vehicle.
Chen teaches:
apply a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input, (See at least [0077-0078]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…The set of vehicle stability constraints can include a first constraint (e.g., second-order TCBF outlined in sections 2 and 3 and equation (56)) that enforces a time-varying control barrier function defining time-varying safety boundaries for operation of the vehicle, the time-varying control barrier function being a second-order time-varying control barrier function that incorporates the rate of change of the set of control inputs…”)
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and (See at least [0078]: “…the time-varying safety boundaries of the first constraint ensure that the global lateral displacement value that results from the updated rate of change of the set of control inputs does not violate the set of lane boundaries…” & [0081]: “Step 206 of method 200 includes determining updated values of the set of control inputs for application to the vehicle at the future time step by integrating the updated rate of change with respect to the set of control inputs associated with the current time step…”. See also [0029] & [0031] regarding determining an updated rate of change and updated set of control inputs.)
execute the final control input on the vehicle. (See at least [0081]: “…Step 208 of method can include applying the updated set of control inputs as input to a mobility system of the vehicle.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril’s system with Chen’s technique of applying a predictive risk-aware control barrier function (PRA-CBF) to determine a final control input, where the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input, and executing the final control input. Doing so would be obvious “to achieve safety-guaranteed driving control for AVs” (See [0026] of Chen).
Regarding claim 9, Oboril and Chen in combination teach all the limitations of claim 8 as discussed above.
Oboril additionally teaches:
wherein the vehicle is a ground, water or airborne vehicle. (See at least [0031]: “A vehicle may be or may include an automobile…”)
Regarding claim 11, Oboril and Chen in combination teach all the limitations of claim 8 as discussed above.
Chen additionally teaches:
wherein the PRA-CBF applies: one or more constraints comprising: at least one of an obstacle or collision avoidance, speed limit, or tractor-trailer jackknife angle; and (See at least [0079]: “…For the example implementation corresponding to the emergent lane change maneuver, the control-dependent safety boundaries of the second constraint ensure that the yaw rate and the lateral velocity of the vehicle that result from the updated rate of change of the set of control inputs are within the stability region of the vehicle.”)
a finite operating time interval. (See at least [0075]: “Step 202 of method 200, a method includes accessing operating data descriptive of operation of a vehicle (e.g., vehicle 100 of FIG. 1B), including a rate of change ({dot over (u)}) of a set of control inputs (u) being applied to the vehicle at a current time step” & [0077]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…”)
Regarding claim 12, Oboril and Chen in combination teach all the limitations of claim 8 as discussed above.
Oboril teaches:
wherein the future state of the vehicle is at least one of a location, heading, or velocity. (See at least [0080]: “…The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time. Parametric models, may determine the vehicle is in a safe state at a current point in time. However, predictive models may instruct a vehicle to decelerate to avoid a collision at a future point in time…”)
Regarding claim 13, Oboril and Chen in combination teach all the limitations of claim 8 as discussed above.
Chen additionally teaches:
wherein the PRA-CBF is implemented via a quadratic program. (See at least [0031]: “The vehicle safety control system 160 can apply a quadratic programming method to determine the updated derivative of the set of control inputs that satisfies the set of vehicle stability constraints in view of the operating data of the vehicle 100. The updated derivative can then be integrated to determine updated values of the set of control inputs, which can then be applied as input to the mobility system 180 of the vehicle 100.”)
Regarding claim 14, Oboril and Chen in combination teach all the limitations of claim 8 as discussed above.
Oboril additionally teaches:
wherein the one or more processors comprise part of an Advanced Driver Assistance System or Automated Driving System. (See at least [0061]: “…the control system 200 may include a driving model, e.g., implemented in an advanced driving assistance system (ADAS)…”)
Regarding claim 15, Oboril teaches:
A method, comprising: predicting a future state of a vehicle; (See at least [0080]: “Autonomous driving may be improved by considering a predicted trajectory of perceived objects or traffic participants. The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
planning a trajectory based on the state of the vehicle; (See at least [0080]: “…the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…” & [0102]: “…the minimum safe distance is compared to a sensed distance between the ego vehicle and an object (e.g., another vehicle or traffic participant). A vehicle state is determined based on the comparison. If the sensed distance is less than the minimum safe distance, the vehicle is determined unsafe 710. Unsafe determination 710 triggers a vehicle response 714. For example, the ego vehicle may have to decelerate at a minimum deceleration rate…”)
determining a present control input based on the trajectory; (See at least [0080]: “…If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time…”)
Oboril does not explicitly teach:
applying a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input,
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and
executing the final control input on the vehicle.
Chen teaches:
applying a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input, (See at least [0077-0078]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…The set of vehicle stability constraints can include a first constraint (e.g., second-order TCBF outlined in sections 2 and 3 and equation (56)) that enforces a time-varying control barrier function defining time-varying safety boundaries for operation of the vehicle, the time-varying control barrier function being a second-order time-varying control barrier function that incorporates the rate of change of the set of control inputs…”)
wherein, in determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input; and (See at least [0078]: “…the time-varying safety boundaries of the first constraint ensure that the global lateral displacement value that results from the updated rate of change of the set of control inputs does not violate the set of lane boundaries…” & [0081]: “Step 206 of method 200 includes determining updated values of the set of control inputs for application to the vehicle at the future time step by integrating the updated rate of change with respect to the set of control inputs associated with the current time step…”. See also [0029] & [0031] regarding determining an updated rate of change and updated set of control inputs.)
executing the final control input on the vehicle. (See at least [0081]: “…Step 208 of method can include applying the updated set of control inputs as input to a mobility system of the vehicle.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril’s system with Chen’s technique of applying a predictive risk-aware control barrier function (PRA-CBF) to determine a final control input, where the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input, and executing the final control input. Doing so would be obvious “to achieve safety-guaranteed driving control for AVs” (See [0026] of Chen).
Regarding claim 16, Oboril and Chen in combination teach all the limitations of claim 15 as discussed above.
Oboril additionally teaches:
wherein the vehicle is a ground, water or airborne vehicle. (See at least [0031]: “A vehicle may be or may include an automobile…”)
Regarding claim 18, Oboril and Chen in combination teach all the limitations of claim 15 as discussed above.
Chen additionally teaches:
wherein the PRA-CBF applies one or more constraints comprising: at least one of an obstacle or collision avoidance, speed limit, or truck-trailer jackknife angle; and (See at least [0079]: “…For the example implementation corresponding to the emergent lane change maneuver, the control-dependent safety boundaries of the second constraint ensure that the yaw rate and the lateral velocity of the vehicle that result from the updated rate of change of the set of control inputs are within the stability region of the vehicle.”)
a finite operating time interval. (See at least [0075]: “Step 202 of method 200, a method includes accessing operating data descriptive of operation of a vehicle (e.g., vehicle 100 of FIG. 1B), including a rate of change ({dot over (u)}) of a set of control inputs (u) being applied to the vehicle at a current time step” & [0077]: “Step 204 of method 200 includes determining an updated rate of change of the set of control inputs for application to the vehicle at a future time step that satisfies a set of vehicle stability constraints in view of the operating data of the vehicle…”)
Regarding claim 19, Oboril and Chen in combination teach all the limitations of claim 15 as discussed above.
Oboril additionally teaches:
wherein the state of the vehicle is at least one of a location, heading, or velocity. (See at least [0080]: “…The determination of the state of a vehicle may include predicted trajectories for a period of time in the future. For example, the period of time in the future may be a time horizon of 2-5 seconds. If the predictive model predicts an object trajectory that puts the vehicle in an unsafe state at a point in time in the future, the vehicle may decelerate at the current point in time. Parametric models, may determine the vehicle is in a safe state at a current point in time. However, predictive models may instruct a vehicle to decelerate to avoid a collision at a future point in time…”)
Regarding claim 20, Oboril and Chen in combination teach all the limitations of claim 15 as discussed above.
Chen additionally teaches:
wherein the PRA-CBF is implemented via a quadratic program. (See at least [0031]: “The vehicle safety control system 160 can apply a quadratic programming method to determine the updated derivative of the set of control inputs that satisfies the set of vehicle stability constraints in view of the operating data of the vehicle 100. The updated derivative can then be integrated to determine updated values of the set of control inputs, which can then be applied as input to the mobility system 180 of the vehicle 100.”)
Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oboril in view of Chen and further in view of Srinivasan of US 20230182721 A1, filed 12/15/2021, hereinafter “Srinivasan”.
Regarding claim 3, Oboril and Chen in combination teach all the limitations of claim 2 as discussed above.
Oboril and Chen in combination do not explicitly teach:
wherein the ground vehicle is a tractor-trailer.
Srinivasan teaches:
wherein the ground vehicle is a tractor-trailer. (See at least [0005]: “The system and techniques described herein may prevent a collision between an obstacle and both a vehicle and a trailer being towed by the vehicle…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril and Chen’s system with Srinivasan’s technique of including a tractor-trailer as a ground vehicle. Doing so would be obvious to “…allow a vehicle with a trailer to avoid obstacles with improved efficiency” (See [0005] of Srinivasan).
Regarding claim 10, Oboril and Chen in combination teach all the limitations of claim 9 as discussed above.
Oboril and Chen in combination do not explicitly teach:
wherein the ground vehicle is a tractor-trailer.
Srinivasan teaches:
wherein the ground vehicle is a tractor-trailer. (See at least [0005]: “The system and techniques described herein may prevent a collision between an obstacle and both a vehicle and a trailer being towed by the vehicle…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril and Chen’s system with Srinivasan’s technique of including a tractor-trailer as a ground vehicle. Doing so would be obvious to “…allow a vehicle with a trailer to avoid obstacles with improved efficiency” (See [0005] of Srinivasan).
Regarding claim 17, Oboril and Chen in combination teach all the limitations of claim 16 as discussed above.
Oboril and Chen in combination do not explicitly teach:
wherein the ground vehicle is a tractor-trailer.
Srinivasan teaches:
wherein the ground vehicle is a tractor-trailer. (See at least [0005]: “The system and techniques described herein may prevent a collision between an obstacle and both a vehicle and a trailer being towed by the vehicle…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril and Chen’s system with Srinivasan’s technique of including a tractor-trailer as a ground vehicle. Doing so would be obvious to “…allow a vehicle with a trailer to avoid obstacles with improved efficiency” (See [0005] of Srinivasan).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oboril in view of Chen and further in view of Breeden of “Predictive Control Barrier Functions for Online Safety Critical Control”, published in 2022, hereinafter “Breeden”.
Regarding claim 21, Oboril and Chen in combination teach all the limitations of claim 1 as discussed above.
Oboril and Chen in combination do not explicitly teach:
wherein the PRA-CBF takes a sequence of predicted control inputs derived from the planned trajectory over a prediction time horizon into account when assessing risks associated with the present control input.
Breeden teaches:
wherein the PRA-CBF takes a sequence of predicted control inputs derived from the planned trajectory over a prediction time horizon into account when assessing risks associated with the present control input. (See at least Section I, pg. 924: “Intuitively, PCBFs are functions that encode both the present and future safety of the system in a model-predictive manner, while still providing the convenient CBF condition on the current control input… “, Section II.B, pg. 925: “…Given a function h : T ×X → R, called a constraint function, we seek to render trajectories always inside the safe set…” & Section III.B, pg. 926: “Given a path function, we can examine the safety of the agent forward in time. To do this, we assume that the agent knows both the current and future construction of its safe set (e.g. future locations of obstacles) on a finite receding horizon T > 0, so that the agent is able to compute h across this horizon…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Oboril and Chen’s system with Breeden’s technique of taking a sequence of predicted control inputs derived from the planned trajectory over a prediction time horizon into account when assessing risks associated with the present control input. Doing so would be obvious since “this strategy is proactive rather than reactive and thus potentially results in smaller modifications to the nominal trajectory” (See Abstract of Breeden).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/NIKKI MARIE M MOLINA/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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