DETAILED ACTION
This Office Action is a first Office Action on the merits of the application. Claims 1 - 20 are presented for examination. Claims 1 - 20 are rejected.
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 .
Drawings Objections
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Elements 1557c, 1560b, 1560c, disclosed in paragraph [0095], are not shown in the drawings. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
As per claims 15 - 20, they are being rejected in view of the OG Notice on Subject Matter Eligibility of Computer Readable Media posted on USPTO’s website on 01/28/2010, and in reference to 2106.03 II of the MPEP. It is being reproduced partially below to maintain a clear record:
“The broadest reasonable interpretation of a claim drawn to a computer-readable media (also called machine readable medium and other such variations, in this case it is a non-transitory computer-readable storage media) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. The specification does not appear to provide a definition of the computer readable media that would limit the media as being non-transitory only, and leaves the definition open to also cover other types of propagating signals or data carrier waves. Paragraph [00109] recites a non-transient computer-readable medium. However, “non-transient” is not equivalent to “non-transitory” for the computer readable medium. In addition, the claim recites a non-transitory computer program product, but the computer program product is defined by the computer readable medium. The specification does not actually provide the definition of the computer-readable medium to limit the medium as being non-transitory only, and leaves the definition open that it can also cover other types of propagating signals or data carrier waves. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. {[]}
It is recommended that the computer readable medium recited in claims 15 - 20 are amended to recite “non-transitory computer readable medium”.
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.
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.
Claims 1 - 5, 7 - 12, and 14 - 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zong et al. (“An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles”), hereinafter “Zong”, in view of Pedersen (“Crashworthiness Design of Transient Frame Structures Using Topology Optimization”), hereinafter “Pedersen”, and further in view of Jeong et al. (“Non-Linear Dynamic Response Structural Optimization of an Automobile Frontal Structure Using Equivalent Static Loads”), hereinafter “Jeong”.
As per claim 1, Zong discloses:
a computer-implemented method of automatically determining an optimized design of a real-world object, the method comprising, by a processor (Zong, page 66242, right col, ln 35 - 37 discloses constructing a front and rear intelligent driver model (IDM) from an existing model, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with at least one type of processor and at least one form of memory.)
defining, in memory of the processor, a computer-based model representing the real-world object (Zong, page 6643, left col, ln 7 - 14 discloses connected and autonomous vehicles (CAV) modeled as multi-front and rear IDM vehicles, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with at least one type of processor and at least one form of memory.)
using the defined computer-based model, determining equilibriums of the real- world object for each of a plurality of time steps (Zong, page 66243, left col, ln 7 - 9 discloses front and rear vehicles in the form of intelligent driver models (IDM), with page 66243, right col, ln 21 - 24 discloses front and rear vehicles with equilibrium terms, which includes velocity Vn and time (t) terms, based on page 66243, left col, ln 19 - 21 and 66243, rt col, ln 32 - 34), with page 66248, right col, ln 24 - 28 providing the time period of 0s to 125s, along with the full time period in FIG.5.)
wherein determining the equilibriums determines velocities of the real-world object across the plurality of time steps (Zong, page 66248, right col, ln 24 - 28 discloses determining velocities for the IDM vehicles, from t = 0s to t = 120s.)
for each of a plurality of time windows defined across the plurality of time steps, determining average acceleration using the determined velocities (Zong, page 66248, right col, ln 24 - 28 discloses velocities determined from time = 0s to t = 125s in the time window of t = 0 to t = 1000s, shown in FIG. 5, and page 66249, left col, ln 30 - 43 discloses velocity of the models to analyze average acceleration and average deceleration, with the average acceleration and deceleration affected by the velocity of the vehicle models.)
Zong does not expressly disclose:
calculating sensitivity of each determined average acceleration using sensitivities of the determined velocities;
using each determined average acceleration, defining at least one of: a constraint and an objective function; and
using the calculated sensitivity of each determined average acceleration, iteratively optimizing the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iteratively optimizing resulting in an updated computer-based model representing the optimized design of the real-world object.
Pedersen however discloses:
calculating sensitivity of each determined average acceleration using sensitivities of the determined velocities (Pedersen, page 662, lines 17 - 19 discloses evaluations to determine velocities and accelerations, with velocity variables in the expression to determine accelerations, along with page 663, lines 22 - 26 discloses sensitivities of velocities and acceleration with the same variables, with page 664, lines 18 - 23 adds an expression (equation 40, for example) to obtain sensitivities for the velocities and accelerations using the respective variables for velocity and acceleration, showing velocity variables used to obtain acceleration and sensitivity of acceleration.)
It is interpreted that the expression to calculate acceleration can calculate acceleration at any one time point, and calculate acceleration at every time point, giving a median of the acceleration based on the expression (28).)
using each determined average acceleration, defining at least one of: a constraint and an objective function (Pedersen, page 655, ln 17 - 23 discloses objective function as a function of acceleration during a crash event, interpreted as the acceleration used to define the objective function, along with an optimization problem with the objective function as well as constraints involving the acceleration variable. FIG. 2 shows constraints on the curve for the acceleration vs time curve in the optimization performed.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson. The motivation to do so would have been because Pedersen discloses the benefit of comparison of different optimizations applied to a conceptual design, with a model type leading to a robust modeling, and analyzing the analytical sensitivities of displacements and accelerations give possibilities of being able to compare different optimization formulations, along with the additional use of numerical damping in time integration effective to obtain smooth sensitivities (Pedersen, page 674, lines 18 - 22).
The combination of Zong and Pedersen does not expressly disclose:
using the calculated sensitivity of each determined average acceleration, iteratively optimizing the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iteratively optimizing resulting in an updated computer-based model representing the optimized design of the real-world object.
Jeong however discloses:
using the calculated sensitivity of each determined average acceleration (Jeong, page 493, right col, ln 17 - 27 discloses noisy response in the acceleration and velocity, shown in FIG. 6, with filtering performed for the noisy response, in which it is interpreted the noisy response regarding the velocity and acceleration is similar to obtaining sensitivity of accelerations and average acceleration.)
iteratively optimizing the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function (Jeong, page 497, left col, ln 5 - 11 discloses solving an optimization in design formulations using nodes for each constraints in FIG. 3, in reference to page 496, right col, ln 5 - 14, and the acceleration, velocity, and displacement constraints, and FIG. 3 showing the frontal structure FE model.)
the iteratively optimizing resulting in an updated computer-based model representing the optimized design of the real-world object (Jeong, page 497, left col, ln 5 - 17 discloses optimization using a program an optimization algorithm, including updating design variables to perform analysis regarding the acceleration, velocity, and displacement constraints for the FE frontal structure model in FIG. 3 in cycles until a convergence satisfies a criterion.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
As per claim 8, Zong discloses:
a system for automatically determining an optimized design of a real-world object, the system comprising a processor (Zong, page 66242, right col, ln 35 - 37 discloses constructing a front and rear intelligent driver model (IDM) from an existing model, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with at least one type of processor and at least one form of memory.)
and a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions being configured to cause the system to: define, in the memory, a computer-based model representing the real- world object (Zong, page 6643, left col, ln 7 - 14 discloses connected and autonomous vehicles (CAV) modeled as multi-front and rear IDM vehicles, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with at least one type of processor and at least one form of memory.)
using the defined computer-based model, determine equilibriums of the real-world object for each of a plurality of time steps (Zong, page 66243, left col, ln 7 - 9 discloses front and rear vehicles in the form of intelligent driver models (IDM), with page 66243, right col, ln 21 - 24 discloses front and rear vehicles with equilibrium terms, which includes velocity Vn and time (t) terms, based on page 66243, left col, ln 19 - 21 and 66243, rt col, ln 32 - 34), with page 66248, right col, ln 24 - 28 providing the time period of 0s to 125s, along with the full time period in FIG.5.)
wherein determining the equilibriums determines velocities of the real-world object across the plurality of time steps (Zong, page 66248, right col, ln 24 - 28 discloses determining velocities for the IDM vehicles, from t = 0s to t = 120s.)
for each of a plurality of time windows defined across the plurality of time steps, determine average acceleration using the determined velocities (Zong, page 66248, right col, ln 24 - 28 discloses velocities determined from time = 0s to t = 125s in the time window of t = 0 to t = 1000s, shown in FIG. 5, and page 66249, left col, ln 30 - 43 discloses velocity of the models to analyze average acceleration and average deceleration, with the average acceleration and deceleration affected by the velocity of the vehicle models.)
Zong does not expressly disclose:
calculate the sensitivity of each determined average acceleration using sensitivities of the determined velocities;
using each determined average acceleration, define at least one of. a constraint and an objective function; and
using the calculated sensitivity of each determined average acceleration, iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object.
Pedersen however discloses:
calculate the sensitivity of each determined average acceleration using sensitivities of the determined velocities (Pedersen, page 662, lines 17 - 19 discloses evaluations to determine velocities and accelerations, with velocity variables in the expression to determine accelerations, along with page 663, lines 22 - 26 discloses sensitivities of velocities and acceleration with the same variables, with page 664, lines 18 - 23 adds an expression (equation 40, for example) to obtain sensitivities for the velocities and accelerations using the respective variables for velocity and acceleration, showing velocity variables used to obtain acceleration and sensitivity of acceleration.)
It is interpreted that the expression to calculate acceleration can calculate acceleration at any one time point, and calculate acceleration at every time point, giving a median of the acceleration based on the expression (28).)
using each determined average acceleration, define at least one of. a constraint and an objective function (Pedersen, page 655, ln 17 - 23 discloses objective function as a function of acceleration during a crash event, interpreted as the acceleration used to define the objective function, along with an optimization problem with the objective function as well as constraints involving the acceleration variable. FIG. 2 shows constraints on the curve for the acceleration vs time curve in the optimization performed.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson. The motivation to do so would have been because Pedersen discloses the benefit of comparison of different optimizations applied to a conceptual design, with a model type leading to a robust modeling, and analyzing the analytical sensitivities of displacements and accelerations give possibilities of being able to compare different optimization formulations, along with the additional use of numerical damping in time integration effective to obtain smooth sensitivities (Pedersen, page 674, lines 18 - 22).
The combination of Zong and Pedersen does not expressly disclose:
using the calculated sensitivity of each determined average acceleration, iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object.
Jeong however discloses:
using the calculated sensitivity of each determined average acceleration (Jeong, page 493, right col, ln 17 - 27 discloses noisy response in the acceleration and velocity, shown in FIG. 6, with filtering performed for the noisy response, in which it is interpreted the noisy response regarding the velocity and acceleration is similar to obtaining sensitivity of accelerations and average acceleration.)
iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function (Jeong, page 497, left col, ln 5 - 11 discloses solving an optimization in design formulations using nodes for each constraints in FIG. 3, in reference to page 496, right col, ln 5 - 14, and the acceleration, velocity, and displacement constraints, and FIG. 3 showing the frontal structure FE model.)
the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object (Jeong, page 497, left col, ln 5 - 17 discloses optimization using a program an optimization algorithm, including updating design variables to perform analysis regarding the acceleration, velocity, and displacement constraints for the FE frontal structure model in FIG. 3 in cycles until a convergence satisfies a criterion.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
As per claim 15, Zong discloses:
a non-transitory computer program product for automatically determining an optimized design of a real-world object, the computer program product executed by a server in communication across a network with one or more clients and comprising: a computer readable medium (Zong, page 66242, right col, ln 35 - 37 discloses constructing a front and rear intelligent driver model (IDM) from an existing model, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with network capabilities, and at least one type of processor and at least one form of memory.)
the computer readable medium further comprising program instructions which, when executed by a processor, cause the processor to: define, in memory of the processor, a computer-based model representing the real-world object (Zong, page 6643, left col, ln 7 - 14 discloses connected and autonomous vehicles (CAV) modeled as multi-front and rear IDM vehicles, and page 66248, left column, lines 1 - 2 adds a numerical simulation used to perform optimization regarding intelligent driver models (IDM), in which the simulation used is interpreted to be performed on a computer with at least one type of processor and at least one form of memory.)
using the defined computer-based model, determine equilibriums of the real-world object for each of a plurality of time steps (Zong, page 66243, left col, ln 7 - 9 discloses front and rear vehicles in the form of intelligent driver models (IDM), with page 66243, right col, ln 21 - 24 discloses front and rear vehicles with equilibrium terms, which includes velocity Vn and time (t) terms, based on page 66243, left col, ln 19 - 21 and 66243, rt col, ln 32 - 34), with page 66248, right col, ln 24 - 28 providing the time period of 0s to 125s, along with the full time period in FIG.5.)
wherein determining the equilibriums determines velocities of the real-world object across the plurality of time steps (Zong, page 66248, right col, ln 24 - 28 discloses determining velocities for the IDM vehicles, from t = 0s to t = 120s.)
for each of a plurality of time windows defined across the plurality of time steps, determine average acceleration using the determined velocities (Zong, page 66248, right col, ln 24 - 28 discloses velocities determined from time = 0s to t = 125s in the time window of t = 0 to t = 1000s, shown in FIG. 5, and page 66249, left col, ln 30 - 43 discloses velocity of the models to analyze average acceleration and average deceleration, with the average acceleration and deceleration affected by the velocity of the vehicle models.)
Zong does not expressly disclose:
calculate the sensitivity of each determined average acceleration using sensitivities of the determined velocities;
using each determined average acceleration, define at least one of: a constraint and an objective function; and
using the calculated sensitivity of each determined average acceleration, iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object.
Pedersen however discloses:
calculate the sensitivity of each determined average acceleration using sensitivities of the determined velocities (Pedersen, page 662, lines 17 - 19 discloses evaluations to determine velocities and accelerations, with velocity variables in the expression to determine accelerations, along with page 663, lines 22 - 26 discloses sensitivities of velocities and acceleration with the same variables, with page 664, lines 18 - 23 adds an expression (equation 40, for example) to obtain sensitivities for the velocities and accelerations using the respective variables for velocity and acceleration, showing velocity variables used to obtain acceleration and sensitivity of acceleration.)
It is interpreted that the expression to calculate acceleration can calculate acceleration at any one time point, and calculate acceleration at every time point, giving a median of the acceleration based on the expression (28).)
using each determined average acceleration, define at least one of: a constraint and an objective function (Pedersen, page 655, ln 17 - 23 discloses objective function as a function of acceleration during a crash event, interpreted as the acceleration used to define the objective function, along with an optimization problem with the objective function as well as constraints involving the acceleration variable. FIG. 2 shows constraints on the curve for the acceleration vs time curve in the optimization performed.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson. The motivation to do so would have been because Pedersen discloses the benefit of comparison of different optimizations applied to a conceptual design, with a model type leading to a robust modeling, and analyzing the analytical sensitivities of displacements and accelerations give possibilities of being able to compare different optimization formulations, along with the additional use of numerical damping in time integration effective to obtain smooth sensitivities (Pedersen, page 674, lines 18 - 22).
The combination of Zong and Pedersen does not expressly disclose:
using the calculated sensitivity of each determined average acceleration, iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function, the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object
Jeong however discloses:
using the calculated sensitivity of each determined average acceleration (Jeong, page 493, right col, ln 17 - 27 discloses noisy response in the acceleration and velocity, shown in FIG. 6, with filtering performed for the noisy response, in which it is interpreted the noisy response regarding the velocity and acceleration is similar to obtaining sensitivity of accelerations and average acceleration.)
iteratively optimize the computer-based model representing the real-world object with respect to the defined at least one of the constraint and the objective function (Jeong, page 497, left col, ln 5 - 11 discloses solving an optimization in design formulations using nodes for each constraint in FIG. 3, in reference to page 496, right col, ln 5 - 14, and the acceleration, velocity, and displacement constraints, and FIG. 3 showing the frontal structure FE model.)
the iterative optimization resulting in an updated computer-based model representing the optimized design of the real-world object (Jeong, page 497, left col, ln 5 - 17 discloses optimization using a program an optimization algorithm, including updating design variables to perform analysis regarding the acceleration, velocity, and displacement constraints for the FE frontal structure model in FIG. 3 in cycles until a convergence satisfies a criterion.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
For claim 2: The combination of Zong, Pedersen and Jeong discloses claim 2: The method of claim 1, wherein the computer-based model is a finite element model, a boundary element method, a finite difference method, a finite volume method, or a discrete element method (Jeong, page 491, right col, ln 1 - 2 discloses a frontal structure in the form of a finite element model, as shown in FIGS. 2 and 3.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong, and the additional teaching of the finite element model representing the frontal structure, also found in Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
For claim 3: The combination of Zong, Pedersen, and Jeong discloses claim 3. The method of claim 1, wherein for a given time window of the plurality of time windows, determining the average acceleration using the determined velocities comprises:
from amongst the determined velocities, identifying (i) velocity at a start point of the given time window (Zong, page 66248, right col, ln 24 - 28 discloses the velocities of each IDM vehicle, shown in FIG. 5, with each having the starting velocity of 0 at t = 0s.)
(ii) velocity at an end point of the given time window (Zong, page 66248, right col, ln 24 - 28 discloses the velocities of each IDM vehicle, shown in FIG. 5, with each having an ending velocity at t = 125s, and page 66249, left col, ln 3 - 5 adds that the process for each vehicle typically ends at t = 114s.)
calculating the average acceleration for the given time window using (i) the identified velocity at the start point (Zong, page 66249, left col, ln 30 - 37 discloses changes in velocity for the vehicle models in analysis of a plurality of vehicles, with Table 2 showing the average acceleration and average deceleration distribution for each model (IDM, multi-front, multi-front and rear), associated with the changes in velocity of the vehicles shown in the time window starting at t = 0s in FIG. 5 and page 66248, rt col, ln 24 - 28).
(ii) the identified velocity at the end point (Zong, page 66249, left col, ln 30 - 37 discloses changes in velocity for the vehicle models in analysis of a plurality of vehicles, with Table 2 showing the average acceleration and average deceleration distribution for each model (IDM, multi-front, multi-front and rear), associated with the changes in velocity of the vehicles shown in the ending point of t = 125s of the time window in FIG. 5 and page 66248, rt col, ln 24 - 28).
(iii) a length, in time, of the given time window (Zong, page 66248, right col, ln 24 - 28 t = 0 at the starting process of the changes in velocity for vehicles, and an ending velocity at t = 125s.)
For claim 4: The combination of Zong, Pedersen, and Jeong discloses claim 4: The method of claim 3, wherein
calculating sensitivity of the average acceleration for the given time window comprises: calculating the sensitivity of the average acceleration for the given time window (Jeong, page 493, right col, ln 15 - 19 discloses noise levels in velocity and acceleration in LS-DYNA non-linear analysis, shown in FIG. 6 with the range of noise in the acceleration and velocity graphs for the majority of the duration of time, 0s to 0.08s.)
with respect to a given design variable (Jeong, page 497, left col, ln 9 - 13 discloses non-linear response, using design variables regarding the parts of the frontal structure in FIG. 2.)
using the identified velocity at the start point and the identified velocity at the end point (Jeong, page 493, col, ln 22 - 27 through page 494, left col, ln 1 - 2 discloses acceleration and velocity vectors regarding the acceleration and velocity computed for FIG. 6, with the vectors containing velocity and acceleration variables for the LS-DYNA non-linear analysis. FIG. 6 shows the velocity value for each time in the time period from the non-linear dynamic analysis results.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong, and the additional teaching of design variables, velocity and acceleration variables, noise levels, also found in Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
For claim 5: The combination of Zong, Pedersen, and Jeong discloses claim 5: The method of claim 1, further comprising:
using the calculated sensitivity of each determined average acceleration and at least one of:
discrete accelerations, sensitivities of a structural measure, and sensitivities of a multi-physics measure in the iteratively optimizing the computer-based model (Jeong, page 491, right col, Table 1 discloses the FE model in the optimization process includes a number of discrete time points of non-linear analysis and page 493, right col, ln 17 - 26 adds the non-linear optimization using a filtering technique to deal with the noise with regards to velocity and acceleration, interpreted to be considered showing sensitivity in the velocity and acceleration.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong, and the additional teaching of the non-linear optimization regarding noise in velocity and acceleration, also found in Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
For claim 7: The combination of Zong, Pedersen, and Jeong discloses claim 7: The method of claim 1, wherein
the real-world object represented by the computer-based model is an automobile, a plane, an electronic device, a medical instrument, or armor (Jeong, page 491, left col, ln 19 - 30 discloses the frontal structure of an automobile, shown in FIG. 1, with page 491, right col, ln 1 - 2, in which the frontal structure in the form of a finite element model.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong with the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson and the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong, and the additional teaching of the frontal structure of an automobile represented by a finite element model, also found in Jeong. The motivation to do so would have been because Jeong discloses the benefit of overcoming difficulties and limitations by using ESLs (equivalent static loads) to improve the accuracy of optimum solution due to using a gradient-based optimization method in the design optimization process, while not being very influenced by the number of design variables since it uses a linear static response optimization method (Jeong, page 490, right col, ln 14 - 22).
As per claims 9 - 12, 14, and 16 - 19, note the rejections of claims 2 - 5, and 7 above. The instant claims 9 - 12, 14, and 16 - 19 recite substantially the same limitations as the above rejected claims 2 - 5 and 7, and are therefore rejected under the same prior art teachings.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zong et al. (“An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles”), in view of Pedersen (“Crashworthiness Design of Transient Frame Structures Using Topology Optimization”), in view of Jeong et al. (“Non-Linear Dynamic Response Structural Optimization of an Automobile Frontal Structure Using Equivalent Static Loads”), and further view of Kim (U.S. PG Pub 2022/0388511 A1), hereinafter “Kim”.
As per claim 6, the combination of Zong, Pedersen, and Jeong discloses claim 1.
The combination of Zong, Pedersen, and Jeong does not expressly disclose:
further comprising: receiving a user defined requirement; and
using both the user defined requirement and each determined average acceleration in the defining at least one of: the constraint and the objective function.
Kim however discloses:
further comprising: receiving a user defined requirement (Kim, par [0051] discloses the maximum impulse or sensitivity that a user can tolerate or consider acceptable based on a criterion that includes average acceleration (as criterion is defined in par [0049], which includes “an average value of the acceleration”).)
using both the user defined requirement and each determined average acceleration in the defining at least one of: the constraint and the objective function (Kim, par [0051] discloses a minimum value set for the sensitivity for each specific criterion, in that the sensitivity does not exceed the value, with the minimum value being the constraint in which the sensitivity is not to exceed.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skilled in the art to combine the equilibrium, average acceleration, velocity, and vehicle-related modeling of Zong, the sensitivities of velocity and acceleration, along with equilibrium and model of a moving vehicle teaching of Pederson, the acceleration constraint for the finite element frontal structure model and updating design variables in an optimization performed teaching of Jeong, and the sensitivity regarding a passenger and acceleration teaching of Kim. The motivation to do so would have been because Kim discloses the benefit of calculating not only sensitivities for one passenger, but also sensitivities for multiple passengers, using the criterion, to obtain a minimum value based on the plurality of sensitivities (Kim, par [0053])
As per claims 13 and 20, note the rejections of claim 6 above. The instant claims 13 and 20 recite substantially the same limitations as the above rejected claim 6, and are therefore rejected under the same prior art teachings.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CEDRIC D JOHNSON whose telephone number is (571)270-7089. The examiner can normally be reached M-Th 4:30am - 2:00pm, F 4:30am - 11:30am.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at 571-270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Cedric Johnson/Primary Examiner, Art Unit 2186
May 16, 2026
Prosecution Insights