DETAILED ACTION
Claims 1-15 are currently presented for examination.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) have been considered by the Examiner.
Claim Objections
Claim 3 is objected to because of the following informalities: the claim recites “the driving situation parameters” when the previous recitation is “the at least one parameter set of driving situation parameters”. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: the claim recites “the driving situation parameters” when the previous recitation is “the at least one parameter set of driving situation parameters”. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: the claim recites “the database” when it is the first recitation. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: the claim recites “the driving situation parameters” when the previous recitation is “the at least one parameter set of driving situation parameters”. Appropriate correction is required.
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.
Regarding claims 1-15, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-12 are directed to a computer implemented method, which is a process, which is a statutory category of invention. Claim 13 is directed to a computer implemented method, which is a process, which is a statutory category of invention. Claim 14 are directed to a device, which is a machine, which is a statutory category of invention. Claim 15 is directed to a non-transitory computer readable medium, which is a manufacture, which is a statutory category of invention. Therefore, claims 1-15 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 13, 14 and 15 recite the abstract idea of simulating the computational effort of a driving motor vehicle, constituting an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitation of "providing at least one parameter set of driving situation parameters and of configuration data of a first algorithm that performs the virtual test, wherein the virtual test performed by the first algorithm simulates the at least one parameter set of driving situation parameters, and wherein the result of the simulation is used to determine at least one further parameter set of driving situation parameters that is simulated in a subsequent iteration;” covers mental processes including making a judgement on what a data set to describe a system should be and evaluating that data set using a set of parameters iteratively. Additionally, the limitation of “applying a second algorithm to the at least one parameter set of driving situation parameters and the configuration data of the first algorithm, which performs the virtual test, for determining the computational effort of the virtual test, performed by the first algorithm, of the device for driving the motor vehicle at least partly autonomously; and” covers mental processes including making a judgement about how to evaluate the first evaluation performed and evaluating the first evaluation using a different set of criteria. Additionally, the limitation of “outputting at least one numerical value that represents the computational effort of the virtual test” covers mental processes including making a judgment about what the output is of the evaluations and can include writing it down on a piece of paper. While the method is computer implemented, and the device and NT-CRM require a processor and memory, there is nothing in the claimed limitations that precludes operation in the human mind. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-12 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claim 13, the additional element of “a machine learning algorithm”, and “training the machine learning algorithm using the driving situation parameters, the configuration data of the first algorithm that performs the virtual test, and the numerical values representing the computational effort of the virtual test, via an optimization algorithm that calculates an extremum of a loss function”, as well as “one or more processors and one or more non-transitory memories” in claim 14, as well as “a non-transitory computer-readable medium” in claim 15, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “wherein the results of each simulation, in particular a duration and/or the computational effort of each simulation, are stored in a database, and wherein the average computational effort is determined using the stored simulation results” in claim 6, as well as “wherein a number of simulations per iteration, a number of iterations, an identifier of a type of the first algorithm, an identifier of the driving situation parameters, and/or a value range, which is to be tested, of the driving situation parameters are additionally stored in the database” in claim 7, as well as “wherein the computational effort of the virtual test, as determined by the second algorithm, is updated during the runtime of the first algorithm using the average computational effort, in particular of previous iterations of the virtual test, stored in the database.” in claim 8, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)) Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-12 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1, 13, 14 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claim 13, the additional element of “a machine learning algorithm”, and “training the machine learning algorithm using the driving situation parameters, the configuration data of the first algorithm that performs the virtual test, and the numerical values representing the computational effort of the virtual test, via an optimization algorithm that calculates an extremum of a loss function”, as well as “one or more processors and one or more non-transitory memories” in claim 14, as well as “a non-transitory computer-readable medium” in claim 15, merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “wherein the results of each simulation, in particular a duration and/or the computational effort of each simulation, are stored in a database, and wherein the average computational effort is determined using the stored simulation results” in claim 6, as well as “wherein a number of simulations per iteration, a number of iterations, an identifier of a type of the first algorithm, an identifier of the driving situation parameters, and/or a value range, which is to be tested, of the driving situation parameters are additionally stored in the database” in claim 7, as well as “wherein the computational effort of the virtual test, as determined by the second algorithm, is updated during the runtime of the first algorithm using the average computational effort, in particular of previous iterations of the virtual test, stored in the database.” in claim 8, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)) Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 is directed to further defining the parameters used, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 3 is directed to further defining the values and parameters used, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 4 is directed to further defining the values outputted from the evaluation, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 5 is directed to further defining the determination of numerical values including the calculation of an average, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claim 9 is directed to further defining the determination of additional values, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 10 is directed to further defining a numerical value, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 11 is directed to further defining the abort criteria to stop the evaluation, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claim 12 is directed to further defining the selection of values, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claims 1-9, 11-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Beglerovic et al. “Testing of Autonomous Vehicles Using Surrogate Models and Stochastic Optimization.”
Regarding claim 1, Beglerovic anticipates A computer-implemented method for determining a computational effort of a virtual test of a device for driving a motor vehicle at least partly autonomously, comprising: (Abstract, Table 1, Section VI A and B Matlab is used to simulate the driving of an autonomous vehicle where the computation effort of each calculation algorithm is tabled
PNG
media_image1.png
353
722
media_image1.png
Greyscale
Section III, the model represents a real system running on an autonomous vehicle.)
providing at least one parameter set of driving situation parameters and of configuration data of a first algorithm that performs the virtual test, (Table 1, Section VI A and B, Figures 3 and 4, driving speed, direction and position, as well as the simulation configuration of the scenario is used
PNG
media_image2.png
339
804
media_image2.png
Greyscale
PNG
media_image3.png
281
697
media_image3.png
Greyscale
)
wherein the virtual test performed by the first algorithm simulates the at least one parameter set of driving situation parameters, and (Figures 2 and 4, Section VI A-C the system simulates using the speed, position and direction parameters)
wherein the result of the simulation is used to determine at least one further parameter set of driving situation parameters that is simulated in a subsequent iteration; (Figures 2 and 4, Section VI A-C, as seen in the figures and described in the sections, the system is iterative)
applying a second algorithm to the at least one parameter set of driving situation parameters and the configuration data of the first algorithm, which performs the virtual test, for determining the computational effort of the virtual test, performed by the first algorithm, of the device for driving the motor vehicle at least partly autonomously; and (Table 1, Section III and VI A-D, the optimization algorithms are used on the simulation to determine the number of function calls and the execution time)
outputting at least one numerical value that represents the computational effort of the virtual test. (Section VI B-D, Table 1, the numerical values are outputted by Matlab in the form of the shown table)
Regarding claim 2, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein the at least one parameter set of driving situation parameters comprises at least one surroundings parameter describing the surroundings of the motor vehicle, and (Figure 3, Section VI A, red dot obstacles are shown in the surrounding of the vehicle
PNG
media_image2.png
339
804
media_image2.png
Greyscale
)
at least one ego parameter describing the state of the motor vehicle. (Figure 3, Section VI A, speed, direction and position are parameters describing the state of the vehicle)
Regarding claim 3, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein the configuration data of the first algorithm that performs the virtual test comprise value ranges, which are to be tested, of the driving situation parameters, (Section VI A, 0 to 100 KM/H and position of 0 to 128m is used)
a step size of the driving situation parameters to be tested, which is either predetermined or parameterizable by the first algorithm, (Section VI A, a step size from 0 to 10 seconds is used)
and/or a number of simulations per iteration. (Section VI A, the number of simulations is set at 100 runs)
Regarding claim 4, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein the second algorithm outputs a first numerical value which represents a minimum computational effort of the virtual test and a second numerical value which represents a maximum computational effort of the virtual test. (Table 1, min and max function call and time to run the algorithm are shown in the table)
Regarding claim 5, Beglerovic anticipates the limitations of claim 4. Beglerovic also anticipates wherein determining the computational effort of the virtual test by the second algorithm comprises calculating an average computational effort, (Table 1, Section VI D, the average number of function calls is listed in the table)
wherein the second algorithm outputs a third numerical value which represents the average computational effort. (Table 1, Section VI D, the average number of function calls from all of the algorithms, including 5 values, are shown in the table)
Regarding claim 6, Beglerovic anticipates the limitations of claim 5. Beglerovic also anticipates wherein the results of each simulation, in particular a duration and/or the computational effort of each simulation, are stored in a database, and (Table 1, Section VI B and D the simulation uses Matlab which must store the result)
wherein the average computational effort is determined using the stored simulation results. (Table 1, Section VI B and D, the average shown in the table is determined using the stored results)
Regarding claim 7, Beglerovic anticipates the limitations of claim 6. Beglerovic also anticipates wherein a number of simulations per iteration, a number of iterations, an identifier of a type of the first algorithm, an identifier of the driving situation parameters, and/or a value range, which is to be tested, of the driving situation parameters are additionally stored in the database. (Table 1, Section VI D, 100 simulation runs are the number of iterations, the type of algorithm is listed in the table; Figures 2-4, Section VI A, the driving parameters are listed, including a range of 0-100 KM/H and 0 to 128M; Table 1, Section VI B and D the simulation uses Matlab which must store the result)
Regarding claim 8, Beglerovic anticipates the limitations of claim 5. Beglerovic also anticipates wherein the computational effort of the virtual test, as determined by the second algorithm, is updated during the runtime of the first algorithm using the average computational effort, in particular of previous iterations of the virtual test, stored in the database. (Figures 2-4, Table 1, Section VI A-D the average is updated over 100 runs)
Regarding claim 9, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein the second algorithm performs a first computational-effort determination using the at least one parameter set of driving situation parameters, and (Figures 2-4, Table 1, Section VI A-D, the first optimization uses the KRI algorithm)
a second computational-effort determination using the configuration data of the first algorithm that performs the virtual test, (Figures 2-4, Table 1, Section VI A-D, the second optimization uses the Rg algorithm)
wherein the at least one numerical value representing the computational effort of the virtual test is calculated using a result of the first computational-effort determination and a result of the second computational effort determination. (Table 1, Section VI D, the values that show the results of all the algorithms are shown in the table)
Regarding claim 11, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein a criterion for aborting the virtual test, performed by the first algorithm, of the device for driving a motor vehicle at least partly autonomously is provided, wherein the criterion is met when a predetermined computational effort is reached. (Table 1, Section VI D, the simulation is aborted after 100 runs)
Regarding claim 12, Beglerovic anticipates the limitations of claim 1. Beglerovic also anticipates wherein the first algorithm selects initial values of the driving situation parameters and adapts them in subsequent iterations using simulation results. (Figures 2 and 4, Sections II, IV, V, VI A-d, the system is iterative and feeds back the results for use in subsequent iterations)
In regards to claim 14, it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Examiner’s Note: As the system is run on Matlab (Section VI B), the system contains a processor and memory. This follows for all iterations.
In regards to claim 15, it is the computer readable medium embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
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.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Beglerovic in view of Chandrasekaran et al. “A Combinatorial Approach to Testing Deep Neural Network-based Autonomous Driving Systems.”
Regarding claim 10, Beglerovic anticipates the limitations of claim 1. Beglerovic does not explicitly recite, wherein a numerical value of a number of computing nodes used to perform the virtual test is provided, wherein the numerical value output by the second algorithm and representing the computational effort of the virtual test relates to the provided number of computing nodes that are used.
Chandrasekaran teaches wherein a numerical value of a number of computing nodes used to perform the virtual test is provided, wherein the numerical value output by the second algorithm and representing the computational effort of the virtual test relates to the provided number of computing nodes that are used. (Section H 1-3, Figures 6-9, the number of neurons and the computation efforts of the used neurons is shown)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Beglerovic with Chandrasekaran as the references deal with simulating autonomous vehicles, in order to implement a system that uses a number of computing nodes that is tied to the computational effort of the system. Chandrasekaran would modify Beglerovic by using a number of computing nodes that is tied to the computational effort of the system. The benefit of doing so is the number of tests needed, using a given number of groups of computing nodes, can be determined to increase the total coverage of the system and aid in fault detection. (Chandrasekaran Section H3)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Beglerovic in view of Kexin et al. “DeepXplore: Automated Whitebox Testing of Deep Learning Systems.”
Regarding claim 13, Beglerovic teaches A computer-implemented method for providing a … algorithm for determining a computational effort of a virtual test of a device for driving a motor vehicle at least partly autonomously, comprising: (Abstract, Table 1, Section VI A and B Matlab is used to simulate the driving of an autonomous vehicle where the computation effort of each calculation algorithm is tabled
PNG
media_image1.png
353
722
media_image1.png
Greyscale
Section III, the model represents a real system running on an autonomous vehicle.)
providing a first … dataset of driving situation parameters and of configuration data of a first algorithm that performs the virtual test; (Table 1, Section VI A and B, Figures 3 and 4, driving speed, direction and position, as well as the simulation configuration of the scenario is used
PNG
media_image2.png
339
804
media_image2.png
Greyscale
PNG
media_image3.png
281
697
media_image3.png
Greyscale
)
providing a second … dataset of numerical values representing a computational effort of the virtual test; and(Table 1, Section III and VI A-D, the optimization algorithms are used on the simulation to determine the number of function calls and the execution time)
Beglerovic does not explicitly recite a machine learning algorithm, training dataset, training the machine learning algorithm using the driving situation parameters, the configuration data of the first algorithm that performs the virtual test, and the numerical values representing the computational effort of the virtual test, via an optimization algorithm that calculates an extremum of a loss function.
Kexin teaches a machine learning algorithm, (Abstract, Section 2.1, a deep learning machine learning algorithm is used)
training dataset, (Abstract, Section 1, a training data set is provided)
training the machine learning algorithm using the driving situation parameters, (Abstract, Section 1, the system is trained using datasets containing common driving situations)
the configuration data of the first algorithm that performs the virtual test, and (Algorithm 1, Figures 5-7, Section 3, 5-6.2, the configuration of the neural network that runs the test is provided as well as the algorithm that performs it)
the numerical values representing the computational effort of the virtual test, (Tables 1, 7-10, the number of activated neurons and the time to complete the computation is shown in the tables)
via an optimization algorithm that calculates an extremum of a loss function. (Algorithm 1, Section 4.2, Table 1-2, 5-7 and 8-11, the DeepXplore optimization algorithm calculates the extremum of the loss function by quantifying the difference between the model output and the target values and providing a percentage accuracy)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Beglerovic with Kexin as the references deal with simulating vehicles, in order to implement a system that trains a machine learning model using virtual tests, numerical values from the tests and optimizes the system to calculate the extremum of a loss function. Kexin would modify Beglerovic by training a machine learning model using virtual tests, numerical values from the tests and optimizing the system to calculate the extremum of a loss function. The benefit of doing so is the accuracy of the machine learning model can be improved. (Kexin Section 7.3)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Allamaa et al. “Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving”: Also teaches the quantification of how long it takes for an autonomous vehicle simulation to be run, which is the computing effort of the system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COCCHI whose telephone number is (469)295-9079. The examiner can normally be reached 7:15 am - 5:15 pm CT Monday - Thursday.
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, Ryan Pitaro can be reached at 571-272-4071. 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.
/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188
Prosecution Insights