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 .
This communication is in response to communication on 03-FEB-2026. Claims 1-19 and 21 are currently pending and have been examined. Claims 1-19 and 21 have been rejected as follows.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03-FEB-2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 03-FEB-2026, with respect to the rejections of independent claims 1, 8, and 15 under 102 have been fully considered. The amendments change the scope of the claims and a new rejection has been made in light of Mittal et al. (US 20220229954 A1).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11, 14-18, 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kabirzadeh (US 11150660 B1) in view of Mittal et al. (US 20220229954 A1)
Regarding claim 1, Kabirzadeh teaches: A method comprising: providing, by a processing device ([Col 7, lines 26-30], “The vehicle computing device can include one or more processor(s) and memory communicatively coupled to the one or more processor(s)”), a plurality of run segments ([Col 14, lines 55-56], “As discussed above, a database can store one or more log data”) each corresponding to a respective scenario for simulating operation of an autonomous vehicle (AV) ([Col 14, lines 53-67], "a set of log data to be used to generate a simulated scenario"), wherein each respective scenario includes one or more agent objects in a respective simulation environment ([Col 2, lines 58-61], “Additionally, objects (e.g., other vehicles, pedestrians, animals, cyclists, etc.) in the log data can be represented as simulated objects”); receiving, by the processing device (element 234, 600), a user-specified configuration for one or more target run segment each corresponding to a respective scenario of interest within the plurality of run segments wherein the user- specified configuration comprises a plurality of parameters (Figure 6; [Col 18, lines 35-53], “the user can, using the filtering component 244, remove simulated objects from the simulated scenario and/or add in objects that the filtering component 244 removed. In some instances, the user can add new simulated objects that do not correspond to the log data”); identifying, by the processing device, the one or more target run segments from the plurality of run segments ([Col 24, lines 4-6], “At operation 708, the scenario component 246 can generate a simulated scenario based at least in part on the log data that comprises a simulated environment.”); causing, by the processing device, one or more simulations to be performed at least one target run segment to generate a simulation output associated with the scenario of interest (Figure 5; element 514, 516, 518): and causing, by the processing device, the AV to be controlled in a real-world environment using one or more AV control operations updated based on the simulation output ([Col 14, lines 6-8], “A single path of the multiple paths in a receding data horizon having the highest confidence level may be selected to operate the vehicle.”; [Col 19, lines 36-39], Successful validation of a proposed controller system may subsequently be downloaded by (or otherwise transferred to) a vehicle for further vehicle control and operation.)
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter
wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters
However, Mittal et al. teaches: and, for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter (Paragraph [82], “a user input comprising a probability indicator associated with the selectable behavioral rule”)… wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator and the selectable behavioral rule”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data, of Kabirzadeh, to include the user input comprising a probability indicator that is utilized for the model as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data and corresponding probability indicators.
Regarding claim 2, Kabirzadeh teaches: The method of claim 1, further comprising: generating the plurality of run segments by identifying as the plurality a set of run segments sampled from a repository of run segments ([Col 14, lines 53-60], “In the memory 236 of the computing device(s) 232, the log data component 238 can determine log data to be used for generating a simulated scenario… In some instances, the computing device(s) 232 can act as the database to store the log data.”), wherein the set is sampled from the repository by selecting run segments comprising a plurality of agent objects present in the corresponding simulation ([Col 14, lines 60-67], “The log data component 238 can use the event component 240 to scan log data stored in the database and identify log data that contains an event of interest. As discussed above, in some instances, the log data can include an event marker that indicates that an event of interest is associated with a particular log data. In some instances, a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 9, lines 66- Col 10, line 1], “The log data 108 can be used by the SES to generate the simulated environment 114 that recreates the environment 102 and includes simulated objects 116(1)-(3)”).
Regarding claim 3, Kabirzadeh teaches: The method of claim 1, wherein the user-specified configuration further comprises a target number of the one or more target run segments to identify within the plurality of run segments (Figure 6; [Col 18, lines 56-61], "In some instances, the simulation component 252 can execute multiple simulated scenarios simultaneously and/or in parallel. This can allow a user to edit a simulated scenario and execute permutations of the simulated scenario with variations between each simulated scenario").
Regarding claim 4, Kabirzadeh teaches: The method of claim 1, wherein the parameters for the one or more target run segments comprise one or more of roadway type, roadway construction, number of vehicles present, number of cyclists present, number of pedestrians present, number of non-moving vehicles present near- collision event, or agent heading direction ([Col 2, lines 46-64], “the log data can include perception data that identifies objects (e.g., roads, sidewalks, road markers, signage, traffic lights, other vehicles, pedestrians, cyclists, animals, etc.) and/or bounding boxes that represent the objects”; [Col 3, lines 10-19], “event of interest can include detecting a collision with another object…can include a lane change, a change of direction (of object(s) in the environment)”).
Regarding claim 7, Kabirzadeh teaches: The method of claim 1, wherein the simulation output is used to validate updated decision logic by comparing simulated performance of the AV with the updated decision logic to simulated performance of the AV based on a prior decision logic version ([Col 18, line 62 - Col 19, line 3], “generate the simulation data indicating how the autonomous controller performed (e.g., responded) and can compare the simulation data to a predetermined outcome and/or determine if any predetermined rules/assertions were broken/triggered”; [Col 19, line 18-36], “can determine that the autonomous controller succeeded. Based at least in part on determining that the autonomous controller performance was inconsistent with the predetermined outcome (that is, the autonomous controller did something that it wasn't supposed to do) and/or determining that a rule was broken or than an assertion was triggered, the simulation component 252 can determine that the autonomous controller failed.”).
Regarding claim 8, Kabirzadeh teaches: A system comprising: a memory device; and a processing device, operatively coupled to the memory device, to perform operations comprising ([Col 7, lines 26-30], “The vehicle computing device can include one or more processor(s) and memory communicatively coupled to the one or more processor(s)”): providing a plurality of run segments each corresponding to a respective scenario for simulating the operation of an autonomous vehicle (AV) (Paragraph [Col 14, lines 53-67], "a set of log data to be used to generate a simulated scenario"), wherein each respective scenario includes one or more respective agent objects in a respective simulation environment ([Col 2, lines 58-61], “Additionally, objects (e.g., other vehicles, pedestrians, animals, cyclists, etc.) in the log data can be represented as simulated objects”); receiving a user-specified configuration comprising for one or more target run segment each corresponding to a respective scenario of interest within the plurality of run segments wherein the user- specified configuration comprises a plurality of parameters (Figure 6; [Col 18, lines 35-53], “the user can, using the filtering component 244, remove simulated objects from the simulated scenario and/or add in objects that the filtering component 244 removed. In some instances, the user can add new simulated objects that do not correspond to the log data”); identifying the one or more target run segments from the plurality of run segments ([Col 24, lines 4-6], “At operation 708, the scenario component 246 can generate a simulated scenario based at least in part on the log data that comprises a simulated environment.”);; causing one or more simulations to be performed using at least one target run segment to generate a simulation output associated with the scenario of interest (Figure 5; element 514, 516, 518) and causing the AV to be controlled in a real-world environment using one or more AV control operations updated based on the simulation output ([Col 14, lines 6-8], “A single path of the multiple paths in a receding data horizon having the highest confidence level may be selected to operate the vehicle.”; [Col 19, lines 36-39], Successful validation of a proposed controller system may subsequently be downloaded by (or otherwise transferred to) a vehicle for further vehicle control and operation.).
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter
wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters
However, Mittal et al. teaches: and, for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter (Paragraph [82], “a user input comprising a probability indicator associated with the selectable behavioral rule”)… wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator and the selectable behavioral rule”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data, of Kabirzadeh, to include the user input comprising a probability indicator that is utilized for the model as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data and corresponding probability indicators.
Regarding claim 9, Kabirzadeh teaches: The system of claim 8, the operations further comprising: generating the plurality of run segments by identifying as the plurality a set of run segments sampled from a repository of run segments ([Col 14, lines 53-60], “In the memory 236 of the computing device(s) 232, the log data component 238 can determine log data to be used for generating a simulated scenario… In some instances, the computing device(s) 232 can act as the database to store the log data.”), wherein the set is sampled from the repository by selecting run segments comprising a plurality of agent objects present in the corresponding simulation ([Col 14, lines 60-67], “The log data component 238 can use the event component 240 to scan log data stored in the database and identify log data that contains an event of interest. As discussed above, in some instances, the log data can include an event marker that indicates that an event of interest is associated with a particular log data. In some instances, a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 9, lines 66- Col 10, line 1], “The log data 108 can be used by the SES to generate the simulated environment 114 that recreates the environment 102 and includes simulated objects 116(1)-(3)”)
Regarding claim 10, Kabirzadeh teaches: The system of claim 8, wherein the user-specified configuration further comprises a target number of the one or more target run segments to identify within the plurality of run segments (Figure 6; [Col 18, lines 56-61], "In some instances, the simulation component 252 can execute multiple simulated scenarios simultaneously and/or in parallel. This can allow a user to edit a simulated scenario and execute permutations of the simulated scenario with variations between each simulated scenario").
Regarding claim 11, Kabirzadeh teaches: The system of claim 8, wherein the parameters for the one or more target run segments comprise one or more of roadway type, roadway construction, number of vehicles present, number of cyclists present, number of pedestrians present, number of non-moving vehicles present near- collision event, or agent heading direction ([Col 2, lines 46-64], “the log data can include perception data that identifies objects (e.g., roads, sidewalks, road markers, signage, traffic lights, other vehicles, pedestrians, cyclists, animals, etc.) and/or bounding boxes that represent the objects”; [Col 3, lines 10-19], “event of interest can include detecting a collision with another object…can include a lane change, a change of direction (of object(s) in the environment)”)
Regarding claim 14, Kabirzadeh teaches: The system of claim 8, wherein the simulation output is used to validate updated decision logic by comparing simulated performance of the AV with the updated decision logic to simulated performance of the AV based on a prior decision logic version ([Col 18, line 62 - Col 18, line 3], “generate the simulation data indicating how the autonomous controller performed (e.g., responded) and can compare the simulation data to a predetermined outcome and/or determine if any predetermined rules/assertions were broken/triggered”; [Col 19, line 18-36], “can determine that the autonomous controller succeeded. Based at least in part on determining that the autonomous controller performance was inconsistent with the predetermined outcome (that is, the autonomous controller did something that it wasn't supposed to do) and/or determining that a rule was broken or than an assertion was triggered, the simulation component 252 can determine that the autonomous controller failed.”).
Regarding claim 15, Kabirzadeh teaches: A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processing device, cause the processing device to perform operations comprising (Paragraph [Col 26, lines 39-49], “A non-transitory computer-readable medium storing instructions executable by a processor, wherein the instructions, when executed, cause the processor to perform operations”): providing a plurality of run segments each corresponding to a respective scenario for simulating the operation of an autonomous vehicle (AV) ([Col 14, lines 53-67], "a set of log data to be used to generate a simulated scenario"), wherein each respective scenario includes one or more respective agent objects in a respective simulation environment ([Col 2, lines 58-61], “Additionally, objects (e.g., other vehicles, pedestrians, animals, cyclists, etc.) in the log data can be represented as simulated objects”); receiving a user-specified configuration for one or more target run segments each corresponding to a respective scenario of interest within the plurality of run segments wherein the user-specified configuration comprises a plurality of parameters (Figure 6; [Col 18, lines 35-53], “the user can, using the filtering component 244, remove simulated objects from the simulated scenario and/or add in objects that the filtering component 244 removed. In some instances, the user can add new simulated objects that do not correspond to the log data”); identifying the one or more target run segments from the plurality of run segments ([Col 24, lines 4-6], “At operation 708, the scenario component 246 can generate a simulated scenario based at least in part on the log data that comprises a simulated environment.”); causing one or more simulations to be performed using at least one target run segment to generate a simulation output associated with the scenario of interest (Figure 5; element 514, 516, 518) and causing the AV to be controlled in a real-world environment using one or more AV control operations updated based on the simulation output ([Col 14, lines 6-8], “A single path of the multiple paths in a receding data horizon having the highest confidence level may be selected to operate the vehicle.”; [Col 19, lines 36-39], Successful validation of a proposed controller system may subsequently be downloaded by (or otherwise transferred to) a vehicle for further vehicle control and operation.)
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter
wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters
However, Mittal et al. teaches: and, for each respective parameter of the plurality of parameters, a target sampling rate of the one or more target run segments having the respective parameter (Paragraph [82], “a user input comprising a probability indicator associated with the selectable behavioral rule”)… wherein a distribution of the plurality of parameters across the one or more target run segments corresponds to a target sampling rate for each of the plurality of parameters (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator and the selectable behavioral rule”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data, of Kabirzadeh, to include the user input comprising a probability indicator that is utilized for the model as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data and corresponding probability indicators.
Regarding claim 16, Kabirzadeh teaches: The non-transitory computer-readable medium of claim 15, the operations further comprising: generating the plurality of run segments by identifying as the plurality a set of run segments sampled from a repository of run segments ([Col 14, lines 53-60], “In the memory 236 of the computing device(s) 232, the log data component 238 can determine log data to be used for generating a simulated scenario… In some instances, the computing device(s) 232 can act as the database to store the log data.”), wherein the set is sampled from the repository by selecting run segments comprising a plurality of agent objects present in the corresponding simulation ([Col 14, lines 60-67], “The log data component 238 can use the event component 240 to scan log data stored in the database and identify log data that contains an event of interest. As discussed above, in some instances, the log data can include an event marker that indicates that an event of interest is associated with a particular log data. In some instances, a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 9, lines 66- Col 10, line 1], “The log data 108 can be used by the SES to generate the simulated environment 114 that recreates the environment 102 and includes simulated objects 116(1)-(3)”).
Regarding claim 17, Kabirzadeh teaches: The non-transitory computer-readable medium of claim 15, wherein the user-specified configuration further comprises a target number of the one or more target run segments to identify within the plurality of run segments (Figure 6; [Col 18, lines 56-61], "In some instances, the simulation component 252 can execute multiple simulated scenarios simultaneously and/or in parallel. This can allow a user to edit a simulated scenario and execute permutations of the simulated scenario with variations between each simulated scenario")
Regarding claim 18, Kabirzadeh teaches: The non-transitory computer-readable medium of claim 15, wherein the parameters for the one or more target run segments comprise one or more of roadway type, roadway construction, number of vehicles present, number of cyclists present, number of pedestrians present, number of non-moving vehicles present near-collision event, or agent heading direction ([Col 2, lines 46-64], “the log data can include perception data that identifies objects (e.g., roads, sidewalks, road markers, signage, traffic lights, other vehicles, pedestrians, cyclists, animals, etc.) and/or bounding boxes that represent the objects”; [Col 3, lines 10-19], “event of interest can include detecting a collision with another object…can include a lane change, a change of direction (of object(s) in the environment)”; Being interpreted as the alternative in light of “one or more”, where the limitation requires only the roadway type, number of vehicles present, number of cyclists present, number of pedestrians present, number of non-moving vehicles present near-collision event, or agent heading direction).
Claim 5, 6, 12, 13, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kabirzadeh (US 11150660 B1) in view of Mittal et al. (US 20220229954 A1) and in further view of Kobilarov (US 20240092357 A1).
Regarding claim 5, Kabirzadeh teaches: The method of claim 1, by applying a minimization algorithm to the plurality of run segments for each respective parameter ([Col 21, lines 42-45], "The simulation component 314 can, using cost minimization algorithms (e.g., gradient descent), determine a trajectory to perform the stop in a safe manner while minimizing or optimizing costs"; [Col 20, lines 47-54], “Additionally, cost(s) that indicates a simulation cost (e.g., a reference, cost, an obstacle cost, an acceleration cost, and/or a steering cost) can be determined for the first waypoint 324. For example, cost(s) can be determined based at least in part on object attributes (e.g., a speed, a steering angle, yaw rate, acceleration, classification, predicted trajectory, uncertainty, and/or other object parameters) associated with the trajectory 320”).
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
based on the target sampling rate
However, Mittal et al. teaches: based on the target sampling rate (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the cost minimization algorithms of costs based on object parameters of Kabirzadeh, to include the probability indicator as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: vehicle simulations utilizing cost minimization algorithms of costs based on object parameters, including probability indicators.
While Kabirzadeh and Mittal et al. teach the limitations as stated above, it does not expressly disclose:
and wherein applying the user-specified configuration to the set of run segments comprises generating a normalized sampling distribution for the set of run segments
However, Kobilarov teaches: and wherein identifying the one or more target run segments from the plurality of run segments comprises generating a normalized sampling distribution for the plurality of run segments (Paragraph [57], “random values may be sampled from a normal distribution (or other distribution) so that the sampled values are weighted and/or clustered toward more the vehicle state parameters having higher likelihoods of resulting in feasible, lowest-cost and/or optimal trajectories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing costs, including probability indicators, and minimization algorithms, of Kabirzadeh and Mittal et al., to include sampling from a normal distribution as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling from normal distributions of costs, including probability indicators, and using minimization algorithms.
Regarding claim 6, Kabirzadeh teaches: The method of claim 5, wherein the one or more target run segments are identified by selecting run segments from the plurality of run segments ([Col 14, lines 65-67], “a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 23, lines 24-44], “allow a user to adjust parameters associated with the simulated environment and/or simulated objects. For example, a user can use a cursor 622 to select a simulated object 624. After selecting the simulation object 624, the sub menu 620 can allow the user to adjust a behavior of the simulated object 624, edit characteristics of the simulated object 624, and/or remove the simulated object 624”)
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
in accordance with the normalized sampling distribution
However, Kobilarov teaches: in accordance with the normalized sampling distribution (Paragraph [57], “random values may be sampled from a normal distribution (or other distribution) so that the sampled values are weighted and/or clustered toward more the vehicle state parameters having higher likelihoods of resulting in feasible, lowest-cost and/or optimal trajectories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing costs, including probability indicators, and minimization algorithms, of Kabirzadeh and Mittal et al., to include sampling selection from a normal distribution as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling selections from normal distributions of costs, including probability indicators and using minimization algorithms.
Regarding claim 12, Kabirzadeh teaches: The system of claim 8, …by applying a minimization algorithm to the plurality of run segments for each respective parameter ([Col 21, lines 42-45], "The simulation component 314 can, using cost minimization algorithms (e.g., gradient descent), determine a trajectory to perform the stop in a safe manner while minimizing or optimizing costs""; [Col 20, lines 47-54], “Additionally, cost(s) that indicates a simulation cost (e.g., a reference, cost, an obstacle cost, an acceleration cost, and/or a steering cost) can be determined for the first waypoint 324. For example, cost(s) can be determined based at least in part on object attributes (e.g., a speed, a steering angle, yaw rate, acceleration, classification, predicted trajectory, uncertainty, and/or other object parameters) associated with the trajectory 320”).
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
based on the target sampling rate
However, Mittal et al. teaches: based on the target sampling rate (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the cost minimization algorithms of costs based on object parameters of Kabirzadeh, to include the probability indicator as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: vehicle simulations utilizing cost minimization algorithms of costs based on object parameters, including probability indicators.
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
and wherein applying the user-specified configuration to the plurality of run segments comprises generating a normalized sampling distribution for the set of run segments
However, Kobilarov teaches: wherein identifying the one or more target run segments from the plurality of run segments comprises generating a normalized sampling distribution for the plurality of run segments (Paragraph [57], “random values may be sampled from a normal distribution (or other distribution) so that the sampled values are weighted and/or clustered toward more the vehicle state parameters having higher likelihoods of resulting in feasible, lowest-cost and/or optimal trajectories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing costs including probability indicators and minimization algorithms, of Kabirzadeh and Mittal et al., to include sampling from a normal distribution as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling from normal distributions of costs including probability indicators and using minimization algorithms.
Regarding claim 13, Kabirzadeh teaches: The system of claim 12, wherein the one or more target run segments are identified by selecting run segments from the plurality of run segments ([Col 14, lines 65-67], “a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 23, lines 24-44], “allow a user to adjust parameters associated with the simulated environment and/or simulated objects. For example, a user can use a cursor 622 to select a simulated object 624. After selecting the simulation object 624, the sub menu 620 can allow the user to adjust a behavior of the simulated object 624, edit characteristics of the simulated object 624, and/or remove the simulated object 624”)
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
in accordance with the normalized sampling distribution
However, Kobilarov teaches: in accordance with the normalized sampling distribution (Paragraph [57], “random values may be sampled from a normal distribution (or other distribution) so that the sampled values are weighted and/or clustered toward more the vehicle state parameters having higher likelihoods of resulting in feasible, lowest-cost and/or optimal trajectories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing costs including probability indicators and minimization algorithms, of Kabirzadeh and Mittal et al., to include sampling selection from a normal distribution as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling selections from normal distributions of costs including probability indicators using minimization algorithms.
Regarding claim 19, Kabirzadeh teaches: The non-transitory computer-readable medium of claim 15, … by applying a minimization algorithm to the plurality of run segments for each respective parameter ([Col 21, lines 42-45], "The simulation component 314 can, using cost minimization algorithms (e.g., gradient descent), determine a trajectory to perform the stop in a safe manner while minimizing or optimizing costs"), and wherein the one or more target run segments are identified by selecting run segments from the plurality of run segments ([Col 14, lines 65-67], “a user can select a log data of a set of log data to be used to generate a simulated scenario”; [Col 23, lines 24-44], “allow a user to adjust parameters associated with the simulated environment and/or simulated objects. For example, a user can use a cursor 622 to select a simulated object 624. After selecting the simulation object 624, the sub menu 620 can allow the user to adjust a behavior of the simulated object 624, edit characteristics of the simulated object 624, and/or remove the simulated object 624”)
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
based on the target sampling rate
However, Mittal et al. teaches: based on the target sampling rate (Paragraph [82], “generating a model for the vehicle agent driving action based on the probability indicator”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the cost minimization algorithms of costs based on object parameters of Kabirzadeh, to include the probability indicator as taught by Mittal et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: vehicle simulations utilizing cost minimization algorithms of costs based on object parameters, including probability indicators.
While Kabirzadeh teaches the limitations as stated above, it does not expressly disclose:
wherein identifying the one or more target run segments from the plurality of run segments comprises generating a normalized sampling distribution for the plurality of run segments… in accordance with the normalized sampling distribution
However, Kobilarov teaches: wherein identifying the one or more target run segments from the plurality of run segments comprises generating a normalized sampling distribution for the plurality of run segments… in accordance with the normalized sampling distribution (Paragraph [57], “random values may be sampled from a normal distribution (or other distribution) so that the sampled values are weighted and/or clustered toward more the vehicle state parameters having higher likelihoods of resulting in feasible, lowest-cost and/or optimal trajectories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing costs including probability indicators and minimization algorithms, of Kabirzadeh and Mittal et al., to include sampling selection from a normal distribution as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling selections from normal distributions of costs including probability indicators and using minimization algorithms.
Regarding claim 21, while Kabirzadeh and Mittel et al. teach the limitations as stated above in claim 5, it does not expressly disclose:
wherein the minimization algorithm comprises one or more least squares functions
However, Kobilarov teaches: The method of claim 5, wherein the minimization algorithm comprises one or more least squares functions (Paragraph [42], “However, the optimization component 202 also may use a derivative-free least squares (DF-LS) technique using least-squares to exploit structure, and/or a derivative-free constrained optimization (DF-COPT) technique using local linear-quadratic approximation”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the user edited autonomous vehicle simulations through user configurable log data utilizing sampling from normal distributions of costs, including probability indicators, and using minimization algorithms, of Kabirzadeh, Mittal et al., and Kobilarov to include the optimization including a least squares technique as taught by Kobilarov. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: user edited autonomous vehicle simulations through user configurable log data utilizing sampling from normal distributions of costs, including probability indicators, and using least squares techniques and minimization algorithms in order to optimize operation.
Conclusion
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/A.T.T./Examiner, Art Unit 3656
/KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656