DETAILED ACTION Status of Claims The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in reply to the application filed on 01/02/2023 . Claims 1-20 are currently pending and have been examined. 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 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. Claim s 1-4, 8, 10-13, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Di Lillo, et al. (US Patent Application Publication 20250368197), “Di Lillo” in view of Farabet (US Patent Application Publication 20190303759), “Farabet” . As per claim s 1 , 10, and 19, Di Lillo discloses : A system comprising: [See Abstract] receive a first configuration of a test parameter associated with testing an autonomous vehicle (AV); [0019], [0023], [0030] For the given example, the test setting module determines a test setting by defining: (1) a first testing protocol for measuring the visibility value by light scattering, the headlight strength by an illuminance meter and an area of illumination by a camera system, (2) a second testing protocol for measuring the road curvature value by GPS positioning analysis, the drowsiness value of the driver by measuring body parameters via a wrist watch and the ADAS driver monitoring value by the time period before sending a drowsiness alarm, and (3) a third testing protocol for measuring the tire pressure value using a pressure gage, a speed value using a tachometer and a cross traffic alert value using a short/medium-range radar unit. Again, the example test setting includes only three testing protocols for the sake of simplicity of the description of the invention. The number of testing protocols basically equals the number of driving scenarios to be measured. However, the test setting may include more or less testing protocols according to test availability, accuracy, and feasibility … In particular the system and the method relate to risk assessment in the rapidly developing fields of ADAS systems and of autonomous vehicle driving. The system and method are particularly realized to provide vehicle testing scores for vehicle with new ADAS functionalities as a service for risk-transfer operations and insurance organizations … After defining the set of measurable scenario characteristics, measuring the characteristics values and determining the multi-dimensional test result signal, the multi-dimensional test result data is weighted by the weighting structure for example by assigning a weighting factor according to a magnitude of the frequency measure and/or a magnitude of the damage severity measure to the quantified measure of frequency of accidents and/or severity of damage associated to the various driving scenarios. receive a set of simulation scenarios from a test suite for testing the AV; [0019], [0023], [0028-0030] , see also [0054] For the given example, the test setting module determines a test setting by defining: (1) a first testing protocol for measuring the visibility value by light scattering, the headlight strength by an illuminance meter and an area of illumination by a camera system, (2) a second testing protocol for measuring the road curvature value by GPS positioning analysis, the drowsiness value of the driver by measuring body parameters via a wrist watch and the ADAS driver monitoring value by the time period before sending a drowsiness alarm, and (3) a third testing protocol for measuring the tire pressure value using a pressure gage, a speed value using a tachometer and a cross traffic alert value using a short/medium-range radar unit. Again, the example test setting includes only three testing protocols for the sake of simplicity of the description of the invention. The number of testing protocols basically equals the number of driving scenarios to be measured. However, the test setting may include more or less testing protocols according to test availability, accuracy, and feasibility … In particular the system and the method relate to risk assessment in the rapidly developing fields of ADAS systems and of autonomous vehicle driving. The system and method are particularly realized to provide vehicle testing scores for vehicle with new ADAS functionalities as a service for risk-transfer operations and insurance organizations … The various driving scenarios 5 each include at least one ADAS variable of the ADAS vehicle 2, preferably they include all ADAS variables of ADAS functionalities 200 available in the ADAS vehicle 2 that is to be tested. The test setting module 42 is configured for determining a test setting 6 for measuring the set of measurable scenario characteristics 50, 51, 52, . . . of each of the various driving scenarios 5 by the driving testing system 3. The test setting 6 is defined by testing protocols 61, 62, 63, . . . transmitted to the driving testing system 3, which then provides measured values of the measurable scenario characteristics according to the measuring initiated by the testing protocols as a test result signal. run a predetermined number of simulations of the AV with the first configuration using the set of simulation scenarios; [0054] , [0077] The various driving scenarios 5 each include at least one ADAS variable of the ADAS vehicle 2, preferably they include all ADAS variables of ADAS functionalities 200 available in the ADAS vehicle 2 that is to be tested. The test setting module 42 is configured for determining a test setting 6 for measuring the set of measurable scenario characteristics 50, 51, 52, . . . of each of the various driving scenarios 5 by the driving testing system 3. The test setting 6 is defined by testing protocols 61, 62, 63, . . . FIG. 6 a shows a diagram schematically illustrating a performance for vehicle models with respect to frequency of accidents for the same driving scenarios. The diagram indicates a frequency of accidents on the y-axis starting from a high frequency to a low frequency, and various vehicle models on the x-axis. The frequency ranges from weak to strong. FIG. 6 b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600 , 600 ′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. determine a first distribution with respect to repeatability based on simulation output of the AV with the first configuration; [0077] FIG. 6a shows a diagram schematically illustrating a performance for vehicle models with respect to frequency of accidents for the same driving scenarios. The diagram indicates a frequency of accidents on the y-axis starting from a high frequency to a low frequency, and various vehicle models on the x-axis. The frequency ranges from weak to strong. FIG. 6b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600, 600′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. Vehicle models 610 and 630 lack in terms of performance mostly in the frequency domain. The diagrams of FIGS. 6a and 6b illustrate the relation of vehicle characteristics and their contribution to accident probability values by quantifying accident frequency and damage severity. The quantified values of accident frequency and damage severity as available for historic accident and driving scenarios allows for quantitative assessment of ADAS vehicles comprising the same or similar vehicle variable values. Further, the values of accident frequency and damage severity can serve as a basis for defining weighting factors for driving scenarios, as described above. update a configuration of the test parameter to generate a second configuration of the test parameter associated with testing the AV; [0032], In Re Harze, [0067] The risk scoring measuring system and method of the invention, inter alia, have the advantages (1) that the system is able to provide risk-indexing scores as inputs for pricing to the insurance industry and key insights for Original Equipment Manufacturers (OEM); (2) that the considered ADAS functionalities can be continuously updated to follow up on new hardware and software releases/versions or newly available new ADAS features as well as related claims experience; (3) that the system is able to provide an advanced risk-indexing score which is a risk factor rooted on consistency (i.e., a score of x today will be a score of ˜x tomorrow); (4) that the driving scenarios as a basis for the risk assessment and the test setting for testing the scenarios are based on thorough accidentology … The test setting 6 is transmitted as a test setting input signal 64 to the driving testing system 3, which measures the variable values for each of the sets of measurable scenario characteristics 51, 52 and 53 of the driving scenarios 5 according to the multi-dimensional test matrix 60 of the test setting 6. Alternatively, instead of actually testing the driving behavior of the vehicle and the driver, the simulation structure 31 of the test setting module 42, may generate test results for the variables of the measurable scenario characteristics, as discussed above. The driving testing system 3 provides an output signal 68, that includes test result data for each of the measured scenario characteristics of the driving scenarios 5. For the shown example, the testing system output data 68 provides: (1) a set of emergency braking variable values 140 indicating driver attention value 1401, a braking distance to obstacle value 1402, and a potential maximum braking deceleration value 1403 based on the simulation of the future version of the ADAS functionality of autonomous emergency braking. (2) a set of blind spot variable values 141 indicating a vicinity movement value 1411, a speed of vicinity movement value 1412, and an ADAS movement warning indication value 1413. (3) a set of heavy traffic variable values 142 indicating a vehicle speed value 1421, a traffic signage recognition value 1422, and a speed reduction value 1423. The testing system output data signal 68 is transmitted to the processing unit 4, particularly to the data administration structure 80, which structures the value testing data in a suitable format defining the multi-dimensional test result signal 14 for further processing by the weighting structure 46. run the predetermined number of simulations of the AV with the second configuration using the set of simulation scenarios; [0054] , In Re Harza, [0077] The various driving scenarios 5 each include at least one ADAS variable of the ADAS vehicle 2, preferably they include all ADAS variables of ADAS functionalities 200 available in the ADAS vehicle 2 that is to be tested. The test setting module 42 is configured for determining a test setting 6 for measuring the set of measurable scenario characteristics 50, 51, 52, . . . of each of the various driving scenarios 5 by the driving testing system 3. The test setting 6 is defined by testing protocols 61, 62, 63, . . . FIG. 6 a shows a diagram schematically illustrating a performance for vehicle models with respect to frequency of accidents for the same driving scenarios. The diagram indicates a frequency of accidents on the y-axis starting from a high frequency to a low frequency, and various vehicle models on the x-axis. The frequency ranges from weak to strong. FIG. 6 b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600 , 600 ′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. determine a second distribution with respect to repeatability based on simulation output of the AV with the second configuration; and [0077] FIG. 6a shows a diagram schematically illustrating a performance for vehicle models with respect to frequency of accidents for the same driving scenarios. The diagram indicates a frequency of accidents on the y-axis starting from a high frequency to a low frequency, and various vehicle models on the x-axis. The frequency ranges from weak to strong. FIG. 6b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600, 600′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. Vehicle models 610 and 630 lack in terms of performance mostly in the frequency domain. The diagrams of FIGS. 6a and 6b illustrate the relation of vehicle characteristics and their contribution to accident probability values by quantifying accident frequency and damage severity. The quantified values of accident frequency and damage severity as available for historic accident and driving scenarios allows for quantitative assessment of ADAS vehicles comprising the same or similar vehicle variable values. Further, the values of accident frequency and damage severity can serve as a basis for defining weighting factors for driving scenarios, as described above. validate the configuration update based on a comparison between the first distribution and the second distribution. [0015], [0075], [0077-0078] T he present invention of an automated vehicle testing system allows to provide full vehicle performance testing, but also individual systems/software and sensors and combinations of sensors. The inventive vehicles testing system are aimed to assess not only the risk-transfer and risk impact of the performance of vehicles/software/systems but also their safety impact, where risk is defined as a physical measure defining an accident and/or vehicle failure rate within a future time window .. . The vehicle variables are tested by a multi-dimensional test matrix. The approach of multi-dimensional test matrix is a theoretical-physics inspired approach accounting for varying multiple scenarios and multiple configurations. For the purpose of demonstrating the multi-dimensional nature of the test setting, FIG. 4 shows a development of ADAS functionalities over time … The frequency ranges from weak to strong. FIG. 6b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600, 600′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. Vehicle models 610 and 630 lack in terms of performance mostly in the frequency domain. The diagrams of FIGS. 6a and 6b illustrate the relation of vehicle characteristics and their contribution to accident probability values by quantifying accident frequency and damage severity. Di Lillo does not expressly disclose the following, Farabet in a similar field of endeavor , however discloses: at least one memory; and [0235-0238] at least one processor coupled to the at least one memory, the at least one processor configured to: [0235-0238] It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to embody the system in non-volatile memory as taught by Farabet , doing so allows the system to be implemented by a computer comprising CPUs and non-volatile memory [0 235-0238 ]. As per claim s 2 , 11, and 20 , Di Lillo discloses : wherein validating the configuration update includes comparing the first distribution and the second distribution to determine if the configuration update introduced noise into the test suite. [0077-0078] The frequency ranges from weak to strong. FIG. 6b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600, 600′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. Vehicle models 610 and 630 lack in terms of performance mostly in the frequency domain. The diagrams of FIGS. 6a and 6b illustrate the relation of vehicle characteristics and their contribution to accident probability values by quantifying accident frequency and damage severity. The quantified values of accident frequency and damage severity as available for historic accident and driving scenarios allows for quantitative assessment of ADAS vehicles comprising the same or similar vehicle variable values. Further, the values of accident frequency and damage severity can serve as a basis for defining weighting factors for driving scenarios, as described above. As per claim s 3 , and 12 , Di Lillo provides for testing different hardware/software/systems for risk impact [0045], Farabet, however doesn’t expressly disclose the following : wherein updating the configuration of the AV includes a configuration change in at least one of an operating system and a Graphics Processing Unit (GPU) associated with testing the AV. [0055-0059], HIL vehicles or objects may use hardware that is used in the physical vehicles or objects to at least assist in some of the control of the HIL vehicles or objects in the simulated environment. For example, a vehicle controlled in a HIL environment may use one or more SoCs 1104 (FIG. 11C), CPU(S) 1118, GPU(s) 1120, etc. in a data flow loop for controlling the vehicle in the simulated environment. In some examples, the hardware from the vehicles may be an NVIDIA DRIVE AGX Pegasus™ compute platform and/or an NVIDIA DRIVE PX Xavier™ compute platform. For example, the vehicle hardware (e.g., vehicle hardware 104) may include some or all of the components and/or functionality described in U.S. Non-Provisional application Ser. No. 16/186,473, filed on Nov. 9, 2018, which is hereby incorporated by reference in its entirety. In such examples, at least some of the control decisions may be generated using the hardware that is configured for installation within a real-world autonomous vehicle (e.g., the vehicle 102) to execute at least a portion of a software stack(s) 116 (e.g., an autonomous driving software stack) … The simulated environment 410 may be generated using virtual data, real-world data, or a combination thereof. For example, the simulated environment may include real-world data augmented or changed using virtual data to generate combined data that may be used to simulate certain scenarios or situations with different and/or added elements (e.g., additional AI objects, environmental features, weather conditions, etc.). For example, pre-recorded video may be augmented or changed to include additional pedestrians, obstacles, and/or the like, such that the virtual objects (e.g., executing the software stack(s) 116 as HIL objects and/or SIL objects) may be tested against variations in the real-world data. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to use different HIL objects in testing of the simulated environments as taught by Farabet , doing so allows different combinations of the actual hardware in the physical vehicles to be used in simulation [0056] . As per claim s 4 , and 13 , Di Lillo discloses : wherein the repeatability includes a likelihood of having the same simulation output in the predetermined number of simulations. [0077] The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. Vehicle models 610 and 630 lack in terms of performance mostly in the frequency domain. The diagrams of FIGS. 6a and 6b illustrate the relation of vehicle characteristics and their contribution to accident probability values by quantifying accident frequency and damage severity. The quantified values of accident frequency and damage severity as available for historic accident and driving scenarios allows for quantitative assessment of ADAS vehicles comprising the same or similar vehicle variable values. Further, the values of accident frequency and damage severity can serve as a basis for defining weighting factors for driving scenarios, as described above. As per claim s 8 , and 17 , Di Lillo discloses : wherein the comparison between the first distribution and the second distribution includes comparing statistical characteristics of the first distribution and the second distribution. [0016], [0077] For example the various driving scenarios include more than 10 differing driving scenarios, preferably more than 15 differing driving scenarios, and advantageously more than 20 differing driving scenarios. The selection and determination of the various driving scenarios can be based accidentology and on statistical findings thereof … The frequency ranges from weak to strong. FIG. 6 b shows a diagram schematically illustrating a performance for vehicle models with respect to severity of damage for the same driving scenarios. The diagram indicates a severity of damage on the y-axis in form of damage mitigation power and the same various vehicle models on the x-axis. A median bar 600 , 600 ′ indicates a statistical median of the accident frequencies and the mitigation power, respectively, for five vehicle models. The five models differ with respect to their vehicle variables and ADAS variables. The first vehicle model 610 has a 61% higher accident frequency and a 41% weaker damage mitigation power than the median. The second vehicle model 620 has a 13% lower accident frequency and a 15% stronger damage mitigation power than the median. The third vehicle model 630 has a 47% higher accident frequency and a 40% weaker damage mitigation power than the median. The fourth vehicle model 640 has a 36% lower accident frequency and a 37% stronger damage mitigation power than the median. The fifth vehicle model 650 has a 57% lower accident frequency and 57% stronger damage mitigation power than the median. The fifth vehicle model 650 is an exceptional performer both in terms of frequency and severity compared to the other vehicle models. As per c laims 10-13 and 17 , claim s 10-13, and 17 recite substantially similar limitations to those found in claims 1-4, and 8 , respectively. Therefor claims 10-13 and 17 are rejected under the same art and rationale as claims 1-4 , and 8 . Furthermore, Di Lillo discloses a method [0015] . As per c laims 19-20 , claims 19-20 recite substantially similar limitations to those found in claims 1-2 , respectively except that the claims are directed towards the non-transitory computer-readable storage embodiment of the invention which, while Di Lillo is silent on this embodiment, Farabet discloses this feature . Therefor claims 19-20 are rejected under the same art and rationale as claims 1- 2 in view of Farabet. Further, Farabet discloses embody ing the system in non-volatile memory [0235-0238]. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to embody the system in non-volatile memory as taught by Farabet , doing so allows the system to be implemented by a computer comprising CPUs and non-volatile memory [0235-0238]. Claim 5, 6, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Di Lillo, et al. (US Patent Application Publication 20250368197), “Di Lillo” in view of Farabet (US Patent Application Publication 20190303759), “Farabet” in further view of Bagschik, et al. (US Patent Application Publication 20210097148), “Bagschik” . As per claim s 5 , and 14, Di Lillo discloses using a failure rate [0015], however does not expressly disclose the following, Bagschik , however discloses : wherein the simulation output is associated with a passing rate of the AV. [0020] Aggregating the simulation data related to the parameterized scenario can provide safety metrics associated with the parameterized scenario. For example, the simulation data can indicate a success rate and/or a failure rate of the autonomous vehicle controller and the parameterized scenario. In some instances, meeting or exceeding a success rate can indicate a successful validation of the autonomous vehicle controller which can subsequently be downloaded by (or otherwise transferred to) a vehicle for further vehicle control and operation. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to use success rates as taught by Bagschik doing so allows the success rates to be used to indicate successful validation of the autonomous vehicle [0020] . As per claim s 6 , and 15 , Di Lillo does not expressly disclose the following, Bagschik, however discloses : wherein the simulation output is associated with a maximum divergence of a pose of the AV. [0047-0048], [0055], [0066] A parameterized scenario component 120 can use the data determined by the parameter component 112 (e.g., initial scenario(s) 110, scenario parameter(s), the set of regions, and/or the error model data) to generate a parameterized scenario 122. For example, the initial scenario(s) 110 can indicate scenarios such as a lane change scenario, a right turn scenario, a left turn scenario, an emergency stop scenario, etc.). The scenario parameter(s) can indicate a speed associated with a vehicle controlled by the autonomous vehicle controller, a pose of the vehicle , a distance between the vehicle and an object, and the like. In some instances, the scenario parameter(s) can indicate objects, positions associated with the objects, velocities associated with the objects, and the like. Further, the error model (e.g., the perception error model, the prediction error model, etc.) can indicate an error associated with the scenario parameter(s) and provide a range of values and/or probabilities associated with the scenario parameter(s). By way of example and without limitation, a scenario parameter such as a speed of the vehicle can be associated with a range of speeds such as 8-12 meters per second). As discussed above, the range of speeds can be associated with a probability distribution that indicates a probability of a speed within the range of speeds of occurring … An analysis component 128 can be configured to determine degrees of a success or a failure. By way of example and without limitation, a rule can indicate that a vehicle controlled by an autonomous vehicle controller must stop within a threshold distance of an object. The simulation data 126 can indicate that in a first variation of the parameterized scenario 122, the simulated vehicle stopped in excess of 5 meters from the threshold distance. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to analyze the scenarios based on data related to the pose of the vehicle as taught by Bagschik doing so further allows the analysis to be based on parameters associated with the pose [0047-0048], [0055]. As per c laims 1 4-15 , claims 14-15 recite substantially similar limitations to those found in claims 5-6 , respectively. Therefor claims 14-15 are rejected under the same art and rationale as claims 5-6 . Furthermore, Di Lillo discloses a method [0015] . Claim s 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Di Lillo, et al. (US Patent Application Publication 20250368197), “Di Lillo” in view of Farabet (US Patent Application Publication 20190303759), “Farabet” in further view of Brizzi, et al. (US Patent Application publication 20210397854), “Brizzi” . As per claim s 7 , and 16 , Farabet does not expressly disclose the following, Brizzi, however discloses : wherein the comparison between the first distribution and the second distribution is based on a Kolmogorov-Smirnov (KS) test. [0129] For instance, as one possibility, evaluating whether the first approach for collecting data characterizing a given scenario type is sufficiently accurate may involve: (i) based on the first dataset characterizing the given scenario type, generating a respective “first” probability distribution for each parameter that is used to characterize the scenario type, (ii) based on the second dataset characterizing the given scenario type, generating a respective second probability distribution for each parameter that is used to characterize the scenario type, (iii) comparing the first probability distribution for each parameter to the corresponding second probability distribution for the parameter, and (iv) based on the comparison, extracting insights regarding the ability of the first approach to accurately collect data characterizing the given scenario type. In this respect, the comparison between the first and second probability distributions for each parameter may take any of various forms, examples of which may include a point-by-point comparison between the first and second probability distributions for a given parameter and/or a comparison using a statistical test such as the Kolmogorov-Smirnov test. Likewise, the insights that are extracted based on the comparison may take any of various forms, examples of which may include a set of error values that quantify the parameter-by-parameter error between the first and second probability distributions across the entirety of the distributions and/or a set of error values that quantify the parameter-by-parameter error between the first and second probability distributions at one or more reference points along the probability distributions (e.g., the median point and/or the P90 point), among other possibilities. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to use the Kolmogorov-Smirnov tes t as taught by Brizzi doing so allows statistical test such as the Kolmogorov-Smirnov test to be used to gain insights [0129]. As per c laim 16 , claim 16 recite substantially similar limitations to those found in claim 7 . Therefor claim 16 is rejected under the same art and rationale as claim 7 . Furthermore, Di Lillo discloses a method [0015] . Claim s 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Di Lillo, et al. (US Patent Application Publication 20250368197), “Di Lillo” in view of Farabet (US Patent Application Publication 20190303759), “Farabet” in further view of Semple, et al. (US Patent 12,552,396 B1), “Semple”. As per claim s 9 , and 18 , Di Lillo discloses using statistics [0016], [0077] , however is silent on the following, Semple, however discloses : wherein the statistical characteristics include at least one of a mean, a standard deviation, and a variance. Col. 39 lines 60-65, col. 39 lines 25-32, col. 45 lines 11-19 , 40-48 In some examples, the recommendation may include one or more actions to perform to mitigate the error. In such examples, the simulation system may be configured to determine mitigating actions based at least in part on the error. In various examples, the simulation system may utilize one or more of machine learning techniques, heuristics, and statistical analysis in the mitigating action determination … In at least on example, the first value may include an average and/or maximum value associated differences between the locations, trajectories, speeds, accelerations, jerks, yaws, yaw rates, and the like of the first simulated object and the first object. It would have been obvious to one having ordinary skill in the art at the time the invention was filed to modify Di Lillo with the ability to determine metrics including an average as taught by Semple doing so allows statistical to be based on averages [Col. 39 lines 60-65, col. 39 lines 25-32]. As per c laim 1 8 , claim 1 8 recite substantially similar limitations to those found in claim 9 . Therefor claim 1 8 is rejected under the same art and rationale as claim 9 . Furthermore, Di Lillo discloses a method [0015] . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT GREGORY S CUNNINGHAM II whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (313)446-6564 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon-Fri 8:30am-4pm . 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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. FILLIN "Examiner Stamp" \* MERGEFORMAT GREGORY S. CUNNINGHAM II Primary Examiner Art Unit 3694 /GREGORY S CUNNINGHAM II/ Primary Examiner, Art Unit 3694