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 .
Claim Rejections - 35 USC § 101
Claims 1-13 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method and system for estimating navigation time.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019).).
Step 1: Does the Claim Fall within a Statutory Cateqory?
Yes, with respect to claims 1-13, which recite a method, system or medium that include at least one step. The system is therefore directed to the statutory class of machine or manufacture. Abstract indicates “The disclosure relates to a method for calculating a setpoint value curve for an intervention in an operation of a vehicle component of a vehicle in order to set an operating variable to a target value in the vehicle component.”
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. But for the recited additional elements, the remaining limitations of the claims recite an abstract idea. The methods show using Mathematical concepts to determine many of the limitations, including using mathematical relationships. Formulas or equations and calculations. The claims are directed to a method, system or apparatus for calculating a setpoint trajectory for an intervention of a vehicle component of a vehicle, which are abstract ideas.
Step 2A, Prong Two: Is the Abstract Idea Inteqrated into a Practical Application?
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components are recited at a high level of generality and are merely invoked as a tool to implement the steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept?
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claim, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claim does not provide an inventive concept.
Examiner suggests adding an active step such as indicated in Claim 13 to all the independent claims.
As such, the claims are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claims talk about boundary (in the ART this is operating variables, Examiner believes); operating variable (what is meant by operating variable); fun-in and gradient (are they the same?); which are not described in the Specification, the claims appear to be described in the Specification as mathematical calculations of varying types but only Claim 13 clearly indicates why and what the calculations are supposed to do.
Applicant can amend the claims to be clear and concise on what the calculations are achieving and what the process is in a clear manner.
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.
Claim 1-12 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. See 112(a) for explanation. The claims are confusing and just appear to show mathematical calculations with not real result and no detailed explanation of what is exactly being done.
Correction is appreciated.
Note that the English translation in Espacenet of both the claims and the Specification clarify a lot of the points in the limitations in a clear manner. The instant application claims and specification are vague compared to the English translation in Espacenet of the priority document.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kock Tilo [DE102018206018, now Tilo. Note that this is an AUDI application but is dated more than 1 year prior to the priority date of the instant application. See attached English translation for ¶ numbers], with Marion Ramon Ewert [US 20220324440, now Ewert], further with J. David, R. Valencia, R. Philippsen, P. Bosshard and K. Iagnemma, "Gradient based path optimization method for autonomous driving," 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 2017, pp. 4501-4508 [Now David].
Claim 1
Tilo discloses a computer-implemented method for calculating a setpoint trajectory for an intervention in an operation of a vehicle component of a vehicle in order to set an operating variable in the vehicle component to a target value [see at least Tilo, Abstract for general concepts of these claims and this preamble], comprising:
specifying a progression function that describes a progression shape over time, the progression function having at least one parameter by which the progression shape of the progression function is settable [see at least Tilo, ¶ 0002-0003 (“In particular in the case of a highly automated or fully automated driving mode of a motor vehicle, a motor vehicle is often to be guided along a specific target path or a target trajectory, i.e. a target path with additional time specifications. In this case, a deviation from the target path or from the target trajectory is to be minimized. For this purpose, certain parameters for the motor vehicle, that is to say for example a steering angle on the front axle, are specified in order to guide the motor vehicle along the desired path. [0003]A corresponding method for controlling the lateral dynamics of a motor vehicle is known, for example, from document DE 10 201 4 203 752 A1. There, it is proposed to compare detected driving dynamics variables with driving dynamics variables specified on the basis of the target trajectory in order to determine a control deviation. A steering angle is adjusted with the aid of a state controller which is based on a single-track model of the motor vehicle.”); 0005 (“From the publication F Gottmann, M Bohm, O Sawodny, " Integrated Trajectory and Path Tracking of under-actuated and over-actuated Cars up to the Handling Limits }, in 2017 American Control Conference, 2017, pages 954-960 A method for transverse guidance of a motor vehicle is known, which can use a corresponding additional actuator system in order to follow a path In this case, a kinematic error model is evaluated on the basis of numerous detected kinematic variables of the motor vehicle in order to describe a deviation of the motor vehicle from a predefined path This results in desired values for longitudinal and lateral acceleration as well as for yaw acceleration. A two-track model is used in order to generate setpoint parameters for actuating the motor vehicle therefrom, in particular a position of a rack of a front steering system, a steering angle for a rear steering system, braking torques for individual wheels and a value which describes a torque distribution on the wheels of an axle. In the context of the error model, a detected yaw rate and lateral speed of the motor vehicle are evaluated. However, determining a yaw rate is typically noise-prone. In addition, lateral speeds cannot typically be measured directly in series vehicles. In addition, the optimization problem for determining the control variables can have a plurality of local minima, so that in individual cases the control variables can be changed suddenly for different actuators.”)];
repeatedly configuring the progression shape of the progression function by calculating a respective parameter value for the at least one parameter of the progression function by using a physical model of the vehicle component and/or by using at least one boundary condition of an operating behavior of the vehicle component [see at least Tilo, ¶ 0015 (:”At least the determination of the preliminary control variable and the prediction value and the final control variable can be repeated at a plurality of points in time, wherein at least one of the points in time is used to determine the preliminary control variable and/or the current prediction value as a function of at least one prediction value determined at a preceding point in time. As already mentioned above, the parameterization of a vehicle model can thus be further developed in terms of time as a function of the provisional control variables. As explained at the outset, this makes it possible, in particular, to determine parameters of the vehicle dynamics which are difficult or complex to measure via a corresponding vehicle model.”)];
determining a starting value for the operating variable, wherein the progression shape begins from the starting value and runs into the target value over time while fulfilling a run-in condition when the progression function is started [see at least Tilo, ¶0007 (“The object is achieved according to the invention by a method for guiding a motor vehicle, comprising the following steps: specifying a target path to be traveled by the motor vehicle, determining at least one provisional control variable for a motor vehicle device influencing the lateral guidance of the motor vehicle as a function of the target path and at least one motor vehicle information item relating to the current position and/or orientation of the motor vehicle, determining at least one forecast value relating to an expected future vehicle movement as a function of the preliminary control variable, determining a final control variable for the motor vehicle device as a function of the prediction value, and controlling the motor vehicle device as a function of the final control variable.”)]; and
cyclically comparing the operating variable with the start value, and if the operating variable is recognized as having the start value, starting a setpoint trajectory that adjusts the operating variable along a progression over time in accordance with the progression shape to the target value by an intervention in a manipulated variable that acts on the operating variable [see at least Tilo, ¶ 0005 (“changed”); 0025 (“At least one prediction parameter can be modified depending on another prediction parameter and a determined measured value for the other prediction parameter, wherein the final control variable is determined as a function of the modified prediction parameter This can be effected in particular by a driving dynamics controller which evaluates deviations of a vehicle model used for determining the preliminary control variable or of its parameters of parameters detected on the real motor vehicle and modifies the prediction parameter in such a way that the actual state of the motor vehicle is approximated to the vehicle model or its parameters Modified forecast values for the lateral acceleration and/or the temporal change of the yaw rate are preferably determined.,,“); 0036 (“Here, vx and vy are the speed of the motor vehicle in the longitudinal and transverse direction, eo of the angles epsi, psi reduced by the attitude angle beta calculated as explained later, psi the orientation of the motor vehicle, whereby the time derivatives thereof is the yaw rate, k is the radius of curvature of the path 12 at the relevant position 19, the time derivative of s is the changes in the position along the path 12, m is the mass of the motor vehicle, Fy,f and Fy,r is the transverse forces acting on the front or rear axle, Iz is the moment of inertia of the motor vehicle, Ff and Ir is the distance of the front or rear axle from the center of gravity of the motor vehicle, cf. and cr is the oblique running stiffnesses at the front or rear axle, zs is the position of the rack of the front wheel steering or the front steering angle, the constant is a parameter of the steering mechanism of the motor vehicle, which indicates a relationship between the rack position and the steering angle, and deltar is the rear steering angle.“); 0044 (“In the vehicle model module 26, a model of the vehicle state is further developed, that is to say certain state variables of the vehicle model are stored and, depending on these state variables and the control variables provided by the path control module 24, the change over time of the motor vehicle state is simulated in order to determine prediction values for the future vehicle movement. A control loop 30 for the vehicle model is thus implemented. With the vector u and the state or observation variables x or y, which can be, in particular, the transverse speed vy of the simulated motor vehicle and the yaw rate {dot over (Psi) } of the simulated motor vehicle, the following equation system can be specified using a single-track model for the motor vehicle:“).
Note that Tilo is teaching many of the aspects/concepts of the limitations. IN BRI Boundary is the same as limits.
Ewert also teaches the general concepts of the limitations as well as clarifying some aspects of the limitations as shown here.
Ewert also teaches a computer-implemented method for calculating a setpoint trajectory for an intervention in an operation of a vehicle component of a vehicle in order to set an operating variable in the vehicle component to a target value [see at least Ewert, Abstract (“A method for operating an autonomous driving function of a vehicle. The vehicle includes a computer unit and sensors for detecting surroundings data. The computer unit is configured to determine a setpoint trajectory for the vehicle, based on the detected surroundings data. In step a), an actual trajectory, and distances from objects in the surroundings, are detected. In step b), an ascertainment of the quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings. In step c), a control of the quality to a predefined target value takes place by selecting sensors to be used for the autonomous driving function from the plurality of sensors and/or by changing a measuring rate, at which measurements are carried out, of at least one sensor from the plurality of sensors.”)], comprising:
specifying a progression function that describes a progression shape over time, the progression function having at least one parameter by which the progression shape of the progression function is settable [see at least Ewert, ¶ 0006 (“German Patent Application No. DE 10 2013 219 567 A1 describes a method for controlling a micromirror scanner. It is provided in the method to control the micromirror scanner as a function of signals of further sensors. For example, when a particularly interesting object is established, it is finely scanned. The micromirror scanner is also controlled as a function of the weather situation, wherein parameters of the scanner during fog are optimized, for example.”); 0008 (“ In accordance with the present invention, a method for operating an autonomous driving function of a vehicle is provided, the vehicle including a computer unit and a plurality of sensors for detecting surroundings data, and the computer unit being configured to determine a setpoint trajectory, along which the vehicle is guided, based on the detected surroundings data.”); 20 (“Taking at least one further parameter into consideration, preferably at least one assessment factor is determined and taken into consideration during the selection of the sensors to be used and/or during the change of the measuring rate, the at least one further parameter being selected from a capacity utilization of the computer unit, a traffic situation in which the vehicle is situated, a scenario in which the vehicle is situated and/or information about the weather at the location of the vehicle.”); 0053 (“Vehicle 1 is guided along the determined setpoint trajectory 2, the present vehicle position being relative to setpoint trajectory 2, based on a reference point 6 which is situated in the center of the rear axle of vehicle 1 in the illustrated example. In addition, actual trajectory 4 is plotted in FIG. 1, which indicates which path vehicle 1 has actually driven, based on reference point 6. Actual trajectory 4 is also determined with the aid of the satellite navigation system and/or with the aid of the recognition of landmarks in the surroundings sensor data and/or with the aid of radio signals and/or in a combination thereof.”)]; ;
repeatedly configuring the progression shape of the progression function by calculating a respective parameter value for the at least one parameter of the progression function by using a physical model of the vehicle component and/or by using at least one boundary condition of an operating behavior of the vehicle component [see at least Ewert, ¶ 0020; 0053];
determining a starting value for the operating variable, wherein the progression shape begins from the starting value and runs into the target value over time while fulfilling a run-in condition when the progression function is started [see at least Ewert, ¶ 0002; 0016 (“he quality of the autonomous driving function indicates how precisely the vehicle is being guided. According to an example embodiment of the present invention, the quality of the autonomous driving function is determined by comparing the setpoint trajectory, along which the vehicle is guided, to the actual trajectory actually traveled by the vehicle. The quality is thus an indication of how accurately a certain setpoint trajectory is traveled by the vehicle, taking the presently requested surroundings data into consideration…”); 0052 (“or providing an autonomous driving function, during which vehicle 1 is guided from a starting position to a target position without the intervention of a driver, computer unit 30 determines a setpoint trajectory 2. During the determination of setpoint trajectory 2, in particular, surroundings data of sensors 10 and the vehicle position determined, among other things, with the aid of receiver 20 are used.”);; and
cyclically comparing the operating variable with the start value, and if the operating variable is recognized as having the start value, starting a setpoint trajectory that adjusts the operating variable along a progression over time in accordance with the progression shape to the target value by an intervention in a manipulated variable that acts on the operating variable [see at least Ewert, Abstract; ¶ 0010 (“In a subsequent step b), an ascertainment of a quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings.”); 0020; 0054; Note is constantly evaluating and checking the sensors and data, that is repeatedly/cyclically comparing operating variables].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert. Providing a more robust, efficient, effective and safer autonomous vehicle.
David more specifically teaches the concepts of using Gradient path optimization so as to further clarify the limitations of Claim 1 (“This paper discusses the possibilities of extending and adapting the CHOMP motion planner [1] to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck and a set of experiments conducted on a real-time environment. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles.
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 2
Tilo, Ewert and David teach the method of Claim 1.
Tilo further teaches for the determining the starting value, an end gradient of the progression function at the target value is specified as the run- in condition, with which the progression shape is to intersect or touch the target value or approach the target value asymptotically [see at least Tilo, ¶ 0008 (“values”); 0020 (“target path”)].
Ewert also teaches these limitations [see at least Ewert, Abstract, 0011-0012 (“distance values”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 3
Tilo, Ewert and David teach the method of Claim 1.
Tilo further teaches the at least one boundary condition includes: a maximum gradient of the shape, and/or a gradient of the progression shape in an initial value has a value, and/or a manipulated variable limitation of the manipulated variable generated for the intervention [see at least Tilo, ¶ 0013 (“the forecast value and the final control variable can each be determined as a function of model parameters relating to the motor vehicle...“)].
Ewert teaches the uses of “manipulated variables” [see at least Ewert, ¶ 0018.
David more specifically teaches these limitations when using a gradient to determine variables [see at least David, Page 4502, Col. 2, ¶ 3 (“The use of gradient based path optimization approaches has been successful for articulated robots. They are faster and produces optimal solutions compared to other methods. For multi-robot task allocation method as in SPADES, there is a need for a very fast reactive approach that can be coupled with the task assignment. Hence, in this paper, we seek to adopt the CHOMP algorithm [1] as the local obstacle avoidance algorithm for SPADES. It is used for optimizing the path generated by E∗ algorithm for an autonomous vehicle depending upon the local obstacles. In the next section, a detailed description of the lower level of the navigation framework of SPADES is explained.“); Page 4503, Column 2, Section C.
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 4
Tilo, Ewert and David teach the method of Claim 1.
Tilo further teaches cyclic updating of a shape of a desired trajectory provides that: a desired target trajectory is planned cyclically or event-triggered, taking into account current boundary conditions, and/or in a case of a target trajectory which has already been started, repeatedly checked whether the target value will be successfully achieved with a current target trajectory and/or whether all boundary conditions are being complied with, wherein in an event that the target value is recognized as not having been being achieved and/or the boundary conditions are not being complied with, a switchover is made to a substitute trajectory form [see at least Tilo, ¶ 0002-0003 0023 (“The provisional control variable can be determined by minimizing an error measure depending on the provisional control variable, wherein the minimization takes place while maintaining at least one boundary condition relating to occurring tire forces Trajectories that can actually travel or paths that can be driven at a certain speed are in particular dependent on whether the frictional forces or tire forces at the individual wheels of the motor vehicle are sufficiently large that no excessive slip occurs In particular in the case of forces which exceed a maximum coefficient of friction of a tire force slip curve, stable vehicle guidance is no longer possible, since the frictional force also decreases with further increasing slip. The error measure can in particular take into account position and/or orientation errors. In particular, a location or orientation deviation from the path or from a setpoint orientation predefined, in particular as a function of the setpoint attitude angle, is described by a differential equation system which results from the used vehicle model. This can be linearized, for example by corresponding feedback, as described in the already cited book by M A Henson. The linear differential equation system could be solved exactly while neglecting the boundary condition. The error measure can describe, for example, the deviation of the solution found under the boundary condition from this exact solution.”)].
Ewert also teaches these limitations/concepts in general [see at least Ewert, ¶ 0029 (“ A processing rate of the computer unit for the surroundings data preferably corresponds to the measuring rate and is accordingly adapted in the event of a change in the measuring rate”)].
David also teaches these concepts and clarifies target trajectory which has already been started, repeatedly checked whether the target value will be successfully achieved with a current target trajectory and/or whether all boundary conditions are being complied with, wherein in an event that the target value is recognized as not having been being achieved and/or the boundary conditions are not being complied with, a switchover is made to a substitute trajectory form [see at least David, Page 44503, Col. 2, Section C.].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 5
Tilo, Ewert and David teach the method of Claim 1.
Tilo further discloses the at least one boundary condition specifies that a gradient of the progression shape in an initial value is to be equal to a gradient of a time progression of an actual value of the operating variable [see at least Tilo, ¶ 0023].
Ewert also teaches these limitations [see at least Ewert, ¶ 0025 (“Preferably, an individual assessment factor is determined for each individual sensor from the plurality of sensors. Due to these individual assessment factors, it is possible to regard the individual properties of the sensors during the control of the quality. In this way, it is ensured that the respective contribution of a sensor to the quality of the autonomous driving function is assessed during the control, and preferably those sensors which presently make the greatest contributions to the quality are selected.”)].
David also teaches the limitation. [see at least David, Page 4503, Col. 2, ¶ 02].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 6
Tilo, Ewert and David teach the method of Claim 1.
Tilo further discloses the progression shape is provided as a mathematical equation, and wherein the method further comprises: receiving a respective observation signal for at least one model variable of the physical model and/or the at least one boundary condition; updating the at least one parameter of the progression function from a respective current signal value of the respective observation signal; and updating the progression shape by using the mathematical equation and an initial value [see at least Tilo, ¶ 0004; 0017 (“The provisional control variable and/or the prediction value can be determined on the basis of a first vehicle model and the provided and/or the further control variable can be determined on the basis of a second vehicle model which is different from the first vehicle model. The first and the second vehicle model describe in particular a respective mathematical relationship between the provisional or final control variables and the vehicle dynamics, that is to say for example between speed, acceleration and yaw rate and the control variables, that is to say for example steering angles or rack positions, braking torques on wheels or a torque distribution.“).
David further teaches updating the at least one parameter of the progression function from a respective current signal value of the respective observation signal; and updating the progression shape by using the mathematical equation and an initial value [see at least David, Page 4504, Col. 2, section B; Page 4505, Col. 1].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 7
Tilo, Ewert and David teach the method of Claim 6.
Tilo further teaches detecting a change in an operating state of the vehicle components via the respective observation signal; and adapting the progression shape and/or the target value to a changed operating state [see at least Tilo, ¶ 0005; 0025; 0036 (“Here, vx and vy are the speed of the motor vehicle in the longitudinal and transverse direction, eo of the angles epsi, psi reduced by the attitude angle beta calculated as explained later, psi the orientation of the motor vehicle, whereby the time derivatives thereof is the yaw rate, k is the radius of curvature of the path 12 at the relevant position 19, the time derivative of s is the changes in the position along the path 12, m is the mass of the motor vehicle, Fy,f and Fy,r is the transverse forces acting on the front or rear axle, Iz is the moment of inertia of the motor vehicle, Ff and Ir is the distance of the front or rear axle from the center of gravity of the motor vehicle, cf. and cr is the oblique running stiffnesses at the front or rear axle, zs is the position of the rack of the front wheel steering or the front steering angle, the constant is a parameter of the steering mechanism of the motor vehicle, which indicates a relationship between the rack position and the steering angle, and deltar is the rear steering angle.”)].
David also teaches these limitations, [see at least David, Page 4502, Col. 1, ¶ 03 (“path calculator”); Page 4503, Col. 1, Section B].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 8
Tilo, Ewert and David teach the method of Claim 1.
Tilo further discloses a history function includes: a function with a horizontal asymptote, and/or an exponential function, and/or a hyperbola, or a ramp-shaped progression into the target value [see at least Tilo, ¶ 0042 (“The calculation of the vector u, which is to be provided to a vehicle model module 26 as shown by the arrow 25, depends on numerous input variables which are provided in various ways The curvature of the path k and the distance from this d can be determined in the path control module 24 itself from the provided path and the motor vehicle position 13 The position 13 and orientation 14 of the motor vehicle 1 is detected via sensors 28 on the motor vehicle side and is provided as indicated by the arrow 27. In addition, the longitudinal acceleration and longitudinal speed of the motor vehicle are provided in this way. Parameters of the motor vehicle, that is to say in particular its mass m, whose moment of inertia Iz, the distances of the front and rear axles from the center of gravity If and Ir and the oblique-running stiffnesses cf and cr, are provided by a parameter module 32. This uses in particular a friction mu of the tires provided by a friction module 33, which friction can be provided, for example, by a driver assistance system for electronic lane control. As will be explained in more detail later, the desired float angle beta can likewise be determined by the path control module 24 itself.”)].
David also teaches these limitations [see at least David, Pate 4504, Col. 1, last ¶].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert, further with the use of gradient path optimization of David. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 9
Tilo, Ewert and David teach the method of Claim 1.
Tilo further discloses a gradient function comprises a PT1 function term
PNG
media_image1.png
32
102
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Greyscale
wherein t is a time, K is an amplification, and T is a time constant of a PT1 member, and a respective calculation rule is specified for K and T based on the physical model and/or the at Least one boundary condition [see at least Tilo, ¶ 0025].
Ewert also teaches the concept of this limitation [see at least Ewert, ¶ 0019].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify/combine, with a reasonable expectation of success, the method of guiding and controlling a vehicle using setpoint parameters of Tilo, with the more specific determining of a setpoint trajectory of Ewert. Providing a more robust, efficient, effective and safer autonomous vehicle.
Claim 10
Tilo, Ewert and David teach the method of Claim 1.
Tilo further discloses for a speed limitation, the vehicle component represents a drive motor of the vehicle and the operating variable represents a speed of the drive motor and the target value represents a maximum speed of the drive motor [see at least Tilo, ¶ 0028].
Claim 11
With regards to Claim 11, this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
Claim 12
Tilo, Ewert and David teach the method of Claim 11.
Tilo further discloses vehicle comprising the processor circuit according to claim 11 [see at least Tilo, Figs, 4 and 5].
Claim 13
Tilo, Ewert and David teach the method of Claim 12.
Tilo further discloses wherein a drive engine is provided as the vehicle component, wherein an engine control unit of the drive engine includes the processor circuit, and wherein the engine control unit, in operation, performs a speed limitation to a target value on the drive engine by the program code of the processor circuit [see at least Tilo, ¶ 0025 (“controller”); 0029 (“the control device 2 can actuate the motor vehicle devices 4-8 in order to carry out automated transverse guidance along the path In addition, the control device 2 can also control the motor 3 and the motor vehicle devices 6, 7, i.e. brakes, in order to carry out a longitudinal guidance of the motor vehicle In order to be able to detect the position and orientation of the motor vehicle 1 with respect to a predefined target path, the motor vehicle 1 has a position sensor 9. In addition, an acceleration sensor 0 and a speed sensor 1 are provided. As will be explained later, the guidance of the motor vehicle 1 along a predetermined path could be further improved if, in addition, a yaw rate of the motor vehicle 1 and/or a transverse speed of the motor vehicle 1 were detected. This could be done via additional sensors (not shown). For example, acceleration sensors arranged at different points of the motor vehicle can be used for yaw rate detection, and a transverse speed can take place via a highly accurate position determination, for example via a differential satellite positioning system, by evaluating camera data or the like.”)].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
W. Schwarting, J. Alonso-Mora, L. Paull, S. Karaman and D. Rus, "Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model," in IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 9, pp. 2994-3008, Sept. 2018.
E. Xargay, V. Dobrokhodov, I. Kaminer, A. M. Pascoal, N. Hovakimyan and C. Cao, "Time-Critical Cooperative Control of Multiple Autonomous Vehicles: Robust Distributed Strategies for Path-Following Control and Time-Coordination over Dynamic Communications Networks," in IEEE Control Systems Magazine, vol. 32, no. 5, pp. 49-73, Oct. 2012.
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/JOAN T GOODBODY/
Examiner, Art Unit 3667
(571) 270-7952