DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending and examined below. This action is in response to the claims filed 2/28/24.
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 6-12 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 pre-AIA the applicant regards as the invention.
Claim 6 recites the following:
6. The method of claim 5, wherein the vehicle is controlled by a controller with unknown gains, and wherein the robust invariant set is determined for a set of possible controllers that describe how the dynamical system toward the setpoint, wherein the set of possible controllers describe the gains of the disturbed vehicle model and are defined with polytopic uncertainty.
The claim element “a set of possible controllers that describe how the dynamical system toward the setpoint” does not appear to be semantic English. It is unclear as to how the controllers describe how the dynamic(al?) system (moves?) toward the set point or what the dynamic system is doing with relation to the setpoint.
Claim 8 recites the following claim element:
“the safe invariant set is determined for each time step, along each point of the prediction horizon or both”
This claim includes a list of three or more elements that fail to include an Oxford comma clearly separating the 2nd-to-last element and the final element in the list. This lack of Oxford comma may cause ambiguity within the claim limitations. Even in recent years and in high-level courts, drivers for Oakhurst Dairy sued their company for $10M in a class-action lawsuit to get overtime because they said the rules for receiving overtime were ambiguous due to the lack of an Oxford comma, and they won their case. Applicant may also consider this sentence that includes an Oxford comma: "I admire my parents, Gandhi, and Mother Teresa". It's clear that this person admires his/her parents, AS WELL AS Gandhi and Mother Teresa. But without the Oxford comma, the phrase "I admire my parents, Gandhi and Mother Teresa" implies that the parents ARE Gandhi and Mother Teresa. So the lack of Oxford comma can certainly create ambiguity. As another example, Applicant can consider the following: “Upon my death, my inheritance should be split evenly between Bob, Betty, and Sue” (with an Oxford comma), which can only be interpreted to mean 1/3 goes to each of Bob, Betty, and Sue; however, “Upon my death, my inheritance should be split evenly between Bob, Betty and Sue” (without an Oxford comma) is questionable/ambiguous as to whether it could instead mean Bob gets 50% and Betty and Sue each get 25% because Betty and Sue were potentially grouped as one entity rather than two separate entities. As such, it is clear that the use of an Oxford comma removes any potential ambiguity by clearly separating each of the elements in a list of three or more. Appropriate corrections are required.
Claim 9 recites the claim element “differentiable trajectory”. There is no definition within the specification regarding the finite limits of this claim element. Without such supporting definition, the claim element will be interpreted utilizing BRI below.
Claim 10 recites the following limitation:
“subject to constraints of define by the constrained environment” which does not appear to be proper semantic English.
Dependent claims are likewise rejected.
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 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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-6, 8-12, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Convens, B. et al. (2021). Invariant set distributed explicit reference governors for provably safe on-board control of Nano-quadrotor swarms. Frontiers in Robotics and AI, 8., herein “Convens”
Regarding claims 1, 19, and 20, Convens discloses a provably safe and computationally efficient distributed constrained control system for UAVs including a method/ feedback controller/ non-transitory computer readable storage medium for controlling the movement of a vehicle in a constrained environment subject to disturbed vehicle model including uncertainty on the dynamics governing the movement of the vehicle, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor, performs the steps of the method (Abstract, Page 3: Section 2.1, and Page 5: Section 4.3 - on-board resources for computation, memory, and communication corresponding to the recited controller/ non-transitory computer readable storage medium for controlling the UAVs in a constrained environment subject to disturbed vehicle model including uncertainty on the dynamics governing the movement of the vehicle), comprising:
collecting a feedback signal indicative of a state of the vehicle and a setpoint for controlling the vehicle according to a task (Page 4: Section 4.2-Page 5: Section 5 - feedback control law utilizing the states of the agents corresponding to the recited vehicle where the agent controls are decoupled into sub-tasks corresponding to the recited a setpoint for controlling the vehicle according to a task);
determining a robust invariant set centered on the setpoint for the operation of the vehicle in an unconstrained environment using the disturbed vehicle model (Page 7: Section 7.1 and Fig. 4 – invariant level set corresponding to the recited robust invariant set (RIS) around an initial reference (0) = v(0) corresponding to the recited centered setpoint in an unconstrained environment using the disturbed vehicle model);
inflating the robust invariant set equally in all directions until a termination condition defined by the constraint environment is met to produce a safe invariant set (Page 7: Section 7.1 and Fig. 4 – invariant level set corresponding to the recited robust invariant set is scaled equally in all directions utilizing a dynamic safety margin (DSM) corresponding to the recited termination condition defined by the constraint environment to produce a safe invariant set); and
controlling the operation of the vehicle according to the task while maintaining the state of the vehicle within the safe invariant set (Page 14: Section 8.2 – generating control parameters defined by the DSM scaled invariant level set corresponding to the recited safe invariant set).
Regarding claim 2, Convens further discloses wherein the robust invariant set is the smallest set of a predetermined shape characterized by a Lyapunov function and a level of the Lyapunov function for the vehicle corresponding to the disturbed vehicle model (Page 12: Section 7.3.1 – Page 13: Section 7.3.3 – invariant level set corresponding to the recited RIS is inflated utilizing the most conservative DSM corresponding to the recited smallest set utilizing convex inequality constraints corresponding to the recited predetermined shape as characterized by a Lyapunov function and a level of the Lyapunov function).
Regarding claim 3, Convens further discloses wherein the robust invariant set is inflated by one or a combination of changing a level of the Lyapunov function and scaling parameters of the Lyapunov function (Page 11: Section 7.3 – DSM corresponding to the recited scaling parameters of the Lyapunov function with safe threshold values of Lyapunov level sets corresponding to the recited changing a level of the Lyapunov function are used for inflating the RIS. The “one or a combination of” claim element only requires one of the following to be present to disclose the element as claimed.).
Regarding claim 4, Convens further discloses wherein the predetermined shape of the robust invariant set is ellipsoidal or polyhedral (Page 4: Section 4.2.2 and Fig. 4 – RIS is constrained to convex polytope corresponding to the recited polyhedral or ellipsoid. The “or” claim element only requires one of the following to be present to disclose the element as claimed.).
Regarding claim 5, Convens further discloses wherein the operation of the vehicle is done by closed-loop feedback control, wherein the robust invariant set is determined to have an ellipsoidal volume with geometry computed in the parameters of the disturbed vehicle model (Page 3: Section 2.3 and Fig. 4 – closed-form feedback control system utilizing ellipsoidal constrained invariant level sets corresponding to the recited RIS with an ellipsoidal volume computer in the parameters of the disturbed vehicle model).
Regarding claim 6, Convens further discloses wherein the vehicle is controlled by a controller with unknown gains, and wherein the robust invariant set is determined for a set of possible controllers that describe how the dynamical system toward the setpoint, wherein the set of possible controllers describe the gains of the disturbed vehicle model and are defined with polytopic uncertainty (Page 4: Section 4.2.2 - Page 5: Section 4.3 and Page 7: Section 7.1 – control objectives are derived from priori unknown reference positioning for the agents corresponding to the recited unknown gains utilizing polytopic state constraints in the presence of model uncertainty based on an initial reference (0) = v(0) corresponding to the recited setpoint).
Regarding claim 8, Convens further discloses wherein the control is performed using a model predictive controller over a prediction horizon, such that the safe invariant set is determined for each time step, along each point of the prediction horizon or both (Page 5: Section 4.3 – steady state control strategy is planned continuously relative to time t to determine the DSM applied to the invariant level set to determine the safe invariant set for each time step. Due to the lack of clarity as noted in 35 USC 112(b) rejection above regarding which elements are required, the claim is interpreted to read “the safe invariant set is determined for each time step, along each point of the prediction horizon, or both”).
Regarding claim 9, Convens further discloses collecting a differentiable trajectory defining the operation of the dynamical system to perform the task, wherein the differentiable trajectory is not guaranteed to be safe or feasible; determining a differential equation having a solution defining a setpoint trajectory, using the safe invariant set, unsafe differentiable trajectory, and the state of the vehicle; integrate the differential equation for each time step of the control to produce the setpoint trajectory (Page 7: Section 7.1, Page 11: Section 7.3 – Page 13: Section 7.3.4, and Fig. 7 – control strategies are used to generate an input constrained double integrator system which includes a differential equation having a solution defining a setpoint trajectory using an optimally aligned invariant level set strategy including inadmissible regions corresponding to the recited differentiable trajectory is not guaranteed to be safe or feasible, state vectors corresponding to the recited state of the vehicle, and the DSM limited invariant set corresponding to the recited safe invariant set where these determined invariant level set/DSM determinations are manipulated derivatives of a generated desired steady-state admissible path corresponding to the recited setpoint trajectory); and
controlling the vehicle according to the computed setpoint trajectory (Page 14: Section 8.2 – generating control parameters defined by the DSM scaled invariant level set corresponding to the recited safe invariant set).
Regarding claim 10, Convens further discloses wherein the differentiable trajectory is determined using an optimization method that minimizes a cost function including a total variation of the movement of the vehicle and its derivatives subject to constraints of define by the constrained environment (Page 7: Section 7.1, Page 11: Section 7.3 – Page 13: Section 7.3.4, and Fig. 7 – control strategies are used to generate an input constrained double integrator system which utilizes Lyapunov theory and optimization to design the DSM based on a navigation field including a total variation of the movement of the vehicle and its derivatives subject to constraints of define by the constrained environment).
Regarding claim 11, Convens further discloses wherein the differentiable trajectory is determined using a navigation field computed in simplified world geometry having obstacles of the constraint environment represented as spheres, wherein the simplified world geometry is mapped to the obstacles in the constrained environment using one or more diffeomorphisms (Page 5: Section 4.2.3, Page 7: Section 7.1, Page 11: Section 7.3 – Page 13: Section 7.3.4, and Figs. 4 and 7 – control strategies are used to generate an input constrained double integrator system which utilizes Lyapunov theory and optimization to design the DSM based on a navigation field including a total variation of the movement of the vehicle and its derivatives subject to constraints of define by the constrained environment where obstacles are simplified to spheres which is a simplified model of the identified obstacles corresponding to the recited one or more diffeomorphisms as seen in Fig. 4).
Regarding claim 12, Convens further discloses wherein the diffeomorphisms are partitioned into a first diffeomorphism that scales the world geometry, and a second diffeomorphism that maps the scaled geometry to the simplified world geometry using obstacle and boundary influence functions computed as solutions to an optimization problem (Pages 9-11: Section 7.2 and Pages 19-20: Section 8.5 – cubic environment filled with No spherical obstacles and N spherical agents is mapped relative to the world environment utilizing a navigation field corresponding to the recited first diffeomorphism that scales the world geometry before introducing the DSM to generate a second diffeomorphism that maps scaled geometry to the simplified world geometry using obstacle and boundary influence functions computed as solutions to an optimization problem).
Regarding claim 17, Convens further discloses wherein the vehicle is a drone operating in an indoor environment and the robust invariant set is computed in six-dimensional space comprising the positions and velocities of the drone (Pages 3-4: Section 3, Page 17: Section 8.4.2, and Fig. 1 – vectors including position, velocity, and rotational velocities are mapped on an XYZ coordinate based system as applied to a quadrotor agent corresponding to the recited drone operating inside a confined environment bounded by four walls).
Regarding claim 18, Convens further discloses wherein the dynamical system (or a vehicle) is a drone, wherein the constraint environment includes an indoor environment having obstacles defining the constraints (Pages 3-4: Sections 2.3-3, Page 17: Section 8.4.2, and Fig. 1 – vectors including position, velocity, and rotational velocities are mapped on an XYZ coordinate based system as applied to a quadrotor agent corresponding to the recited drone with obstacle collision avoidance constraints in a constrained indoor environment corresponding to the recited an indoor environment having obstacles defining the constraints).
Allowable Subject Matter
Claims 7 and 13-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims as well as amending the claims to overcome any other rejections.
Regarding claim 7, while the art of record does disclose in theory the majority of the claimed elements including utilizing ellipsoidal volumes in solving optimization problems for obstacle avoidance by mapping the bounds of the environment including obstacles and their projected movements utilizing uncertainties and polytopic constraints as cited in relation to the claims above, it does not explicitly apply the theory into the steps as claimed below.
7. The method of claim 6, wherein the ellipsoidal volume is determined by solving optimization problem that includes bounds on the maximum rotation angle of the attitude tracking error in the closed-loop vehicle control system, bounded input disturbances of the disturbed vehicle model, and the polytopic uncertainties in the gains of the disturbed vehicle model.
Regarding claim 13, while the art of record does disclose in theory the majority of the claimed elements including computing different invariant level sets as applied to mapped object constraints inflated utilizing the DSM to generate safe invariant sets through Lyapunov based manipulation relative to different goals and setpoints in order to plan a safe path as cited in relation to the claims above, it does not explicitly apply the theory into the steps as claimed below.
13. The method of claim 1, further comprising: computing a set of setpoints and a set of safe invariant sets centered on the corresponding set points; and
constructing a graph having vertices defined by the set of setpoints; and
determining connectivity of the graph based on the safe invariant sets, such that a connection is established if the robust invariant set of one node is contained in the safe invariant set of another;
finding a solution path of vertices connecting an initial vertex in the graph to a terminal vertex in the graph corresponding to the specified task; and
controlling the vehicle according to the solution on the graph by switching the setpoints based on the Lyapunov function and its size in relation to the safe invariant sets of the nodes along the vertices in the solution path.
Dependent claims are likewise objected to.
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Geiger et al. (US 2023/0281511) discloses a control strategy for obtaining a safe action including utilizing scalar vectors in the action space and applying a piecewise diffeomorphism to determine the exact density of the safe action (Abstract and ¶38).
Durling et al. (US 2012/0158219) discloses a trajectory based sense and avoid system including utilizing 4-D polytopes derived for regions of interest as well as other agents in the environment (¶23).
Kolter et al. (US 2021/0302921) discloses a neural network based control system for drones including utilizing polytopic uncertainty for optimizing dynamic systems (¶119).
Berntorp et al. (US 2022/0234570) discloses a vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle. (Abstract)
Conclusion
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/MATTHEW J. REDA/Primary Examiner, Art Unit 3665