DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 1, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This first final action is in response to applicant's amendment on April 16, 2026. Claims 1-28 are pending and have been considered as follows.
Response to Arguments/Amendments
Applicant’s amendments/arguments with respect to the rejections to claims under 35 U.S.C 101 have been fully considered and are persuasive. Therefore, the rejections to claims under 35 U.S.C 101 have been withdrawn.
Applicant’s amendments/arguments with respect to claim(s) under 35 U.S.C 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-18 and 21-28 are rejected under 35 U.S.C. 103 as being obvious over Kabirzadeh (US11150660B) in view of Zhou (CN 118296862 A1 machine translation)
Regarding claim 1, Kabirzadeh teaches a method for updating a planning system of an autonomous vehicle (abstract), the method comprising:
obtaining, at a simulator, an input comprising scene data used for a single planning cycle of the planning system (Figs. 1-2, 7, and corresponding paragraphs, col. 7, line 15-Col 8, line 45, Fig. 7, receive log data of an autonomous vehicle traversing an environment, step 702-718, a single planning cycle; filtering at least a portion of the data, selecting a subset of the at least a portion of the data used for a minimum time period);
using the simulator, applying a simulation for the single planning cycle of the planning system based on the scene data, without simulating additional planning cycles, to output a plurality of autonomous vehicle metrics (Fig. 3 and corresponding paragraphs, m Based on the log data 108, the SES can generate a 55 simulated scenario that includes a simulated environment 114);
updating one or more parameters of the simulation based on the plurality of autonomous vehicle metrics (The scenario editor component 250 can allow a user to modify the scenario generated by the scenario component 246; updating a state of the simulated object in the 30 simulated scenario, wherein updating the position and velocity of the simulated object is based at least in part on the simulation cost);
outputting a plurality of updated autonomous vehicle metrics based on the one or more updated parameters (Figs. 5-6 and corresponding paragraphs);
determining that a condition is satisfied based on the plurality of updated autonomous vehicle metrics (Col. 19, lines 11- 30, simulation cost; The simulation component 252 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; determining that a rule was not broken or an assertion was not triggered, the simulation component 252 can determine that the autonomous controller succeeded. Based at least in part on determining that the autonomous controller performance was inconsistent with 25 the predetermined outcome (that is, the autonomous con-troller 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); and
updating, based on the one or more updated parameters, the planning system of the autonomous vehicle (simulations can be useful to inform the hardware design of autonomous vehicles, such as optimizing placement of sensors on an autonomous vehicle. Successful validation of a proposed controller system may subsequently be down-loaded by (or otherwise transferred to) a vehicle for further vehicle control and operation ).
Kabirzadeh does not explicitly teach a single trajectory planning cycle. under reasonable interpretation, in Fig. 7, from step 702-718 is a single planning cycle. In addition, Zhou teaches a single planning cycle and without simulating additional planning cycles (Step 101, obtaining log data corresponding to a driving failure event occurring in automatic driving simulation of a vehicle; determine one driving failure event (single cycle) occurring in the vehicle autopilot simulation; acquiring log data corresponding to each driving failure event respectively).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, a scenario editor and simulator, as taught by Kabirzadeh, using a single trajectory planning cycle, as taught by Zhou, as Kabirzadeh and Zhou are directed to vehicle trajectory simulation (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using a single trajectory planning cycle and predictably applied scenario editor and simulator it to minimize tracking error with respect to the reference trajectory.
Please see more references regarding a single planning cycle and without simulating additional planning cycles in Prior Art section.
Regarding claims 25 and 26, please see the rejection above with respect to claim 1.
Regarding claim 2, Kabirzadeh teaches the limitation of wherein updating the planning system comprises updating a trajectory planner of the planning system and the input comprises scene data used for a single trajectory planning cycle of the trajectory planner (Fig. 5 and corresponding paragraphs, The log data can include an event of interest. an event of interest can include detecting a collision with another object, a violation of a traffic rule (e.g., a speed limit), and/or a disengagement where a human driver takes control of the operation of the vehicle from an autonomous controller. In some instances, an event of interest can include an error condition and/or a warning such as a low battery power event. Further, an event of interest can include a lane change, a change of direction (of object(s) in the environment), and/or a change in velocity (of object(s))).
Regarding claim 3, Kabirzadeh teaches the limitation of wherein the simulation of the planning system is based only on the scene data used for the single planning cycle (log data in Col. 3, lines 10-50).
Regarding claim 4, while Kabirzadeh as modified by Zhou teaches the design should be efficient enough to avoid spending unnecessary milliseconds performing calculations to determine a new decision, Kabirzadeh as modified by Zhou discloses the claimed invention (e.g. complete one or more cycle) except complete ten cycles per second. It would have been an obvious matter of design choice to use ten cycles per second, since applicant has not disclosed that using ten cycles per second solves any stated problem or is for any particular purpose.
Regarding claim 5, Kabirzadeh teaches wherein the scene data used for the single planning cycle is extracted from one or more event logs of the planning system (Col. 15, lines 1- 15).
Regarding claim 6, Kabirzadeh teaches wherein the scene data is extracted from the one or more event logs based on at least one of a user input comprising an indication of an event or a detection by the planning system of an event (Fig. 6 and corresponding paragraphs, including at least The scenario editor component 250 can allow a user to modify the scenario generated by the scenario component 246. 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 ).
Regarding claim 7, Kabirzadeh teaches wherein the scene data comprises input data used for the event and used by the planning system during the single planning cycle (Cols 3-4).
Regarding claim 8, Kabirzadeh teaches wherein the input data comprises at least one of perception data, localization data, or routing data (the vehicle(s) 104 can transmit log data 108 that includes time data, perception data, and/or map data to the computing device(s) 110 to store the log data 108).
Regarding claim 9, Kabirzadeh teaches wherein the perception data is detected by one or more perception sensors positioned on the autonomous vehicle (perception data that identifies objects (e.g., roads, sidewalks, road markers, signage, traffic lights, other vehicles, pedestrians, cyclists, 50 animals, etc.), using sensor system(s) 206 can capture one or more images of an environment. The sensor system(s) 206 can capture images of an environment that includes an object).
Regarding claim 10, Kabirzadeh teaches wherein the scene data used for the single planning cycle is extracted from one or more event logs of a multi-cycle planning system simulation (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. As discussed above, a database can store one or more log data. In some instances, the computing device(s) 232 can act as the database to store the log data. In some instances, the computing device(s) 232 can connect to a different computing device to access the log data database. 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. the log data can include an event marker that indicates that an event of interest is used for 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 ).
Regarding claim 11, Kabirzadeh teaches wherein the scene data used for the single planning cycle is generated by a scene data simulation ( Fig. 7, single loop from steps 702-718).
Regarding claim 12, Kabirzadeh teaches wherein generating the scene data comprises generating a plurality of variations of a scene, wherein each variation is generated based on a unique set of scene parameters (Col. 16, lines 50- 67, the weight(s) used for a waypoint can influence a cost used for the simulated object deviating from or adhering to the waypoint behavior, a weight used for a waypoint of the simulated object 418 (e.g., used for a location of the simulated object 418) may be lower to 55 allow the simulated object 418 to react to motion of the simulated vehicle 416 that deviates from the motion of the vehicle 408 in the log data 402).
Regarding claim 13, Kabirzadeh teaches wherein the portion of the plurality of autonomous vehicle metrics comprise at least one of:
one or more action inconsistency metrics,
one or more actor controllability metrics,
one or more lane preference metrics,
one or more right-of-way assertion metrics,
one or more route progress metrics (Fig. 6 and corresponding paragraphs), or
one or more spatial lane boundary metrics (Fig. 6 and corresponding paragraphs).
Regarding claim 14, Kabirzadeh teaches wherein determining that the condition is satisfied comprises at least one of:
determining, based on the plurality of updated autonomous vehicle metrics, that a failure condition has been corrected; or
determining that at least one of the plurality of updated autonomous vehicle metrics meets a threshold value (an event of interest can include detecting a collision with another object, a violation of a traffic rule (e.g., a speed limit), and/or a disengagement where a human driver takes control of the operation of the vehicle from an autonomous controller. In some instances, an event of interest can include an error condition and/or a warning such as a low battery power event. Further, an event of interest can include a lane change, a change of direction (of object(s) in the environment), and/or a change in velocity (of object(s) in the environment).
Regarding claim 15, Kabirzadeh teaches wherein the single planning cycle comprises:
a trajectory generation step comprising generating one or more candidate trajectories based on one or more candidate actions (the first waypoint 324 can be used for a threshold such that if determined cost(s) meet or exceed a threshold, the candidate trajectory can be rejected or modified to determine a trajectory with cost(s) that do not exceed the threshold); and
a trajectory selection step comprising selecting a candidate trajectory from the one or more candidate trajectories (the first waypoint 324 can be used for a threshold such that if determined cost(s) meet or exceed a threshold, the candidate trajectory can be rejected or modified to determine a trajectory with cost(s) that do not exceed the threshold; generating a candidate trajectory of the object; wherein the candidate trajectory comprises one or more of a simulated object position, a simulated object velocity, a simulated object acceleration, or a simulated object time).
Regarding claim 16, Kabirzadeh teaches wherein the single planning cycle further comprises, prior to the trajectory generation step:
an action generation step comprising generating the one or more candidate actions for execution by the autonomous vehicle (Fig. 5 and correspond paragraphs); and
an interaction generation step comprising validating the one or more candidate actions based on a model of one or more interactions between the autonomous vehicle and a vehicle nearby to the autonomous vehicle ( Abstract, The scenarios can be used for testing and validating interactions and responses of a vehicle controller within a simulated environment).
Regarding claim 17, Kabirzadeh teaches further comprising displaying a graphical user interface, wherein the graphical user interface comprises a visualization of at least a portion of the plurality of autonomous vehicle metrics (Fig. 6 and correspond paragraphs, via a user interface that can also allow the user to view a graphical representation of the scenario and edit the scenario).
Regarding claim 18, Kabirzadeh teaches further comprising: detecting, at the graphical user interface, a user input indicating a selection of a selectable trajectory selection affordance; and causing, in response to detecting the user input, the graphical user interface to display at least a portion of the plurality of autonomous vehicle metrics for one or more candidate trajectories (Fig. 6 and correspond paragraphs, 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. For example, as discussed above, the user can edit events of interest used for the simulated object 624. This can force the simulated object 624 to perform an action in the simulated scenario. In some instances, the user can adjust a waypoint associated with a trajectory of the simulated object 624. The user can move a position of the waypoint and/or attributes of the waypoint such as a cost threshold).
Regarding claim 21, Kabirzadeh teaches wherein the graphical user interface comprises a batch view comprising a selectable first affordance, the method further comprising: detecting, at the graphical user interface, a user input indicating a selection of the selectable first affordance; and causing, in response to detecting the user input, the graphical user interface to display at least a portion of the plurality of autonomous vehicle metrics associated with a plurality of variations of a scene generated by a scene data simulation (Fig. 6 and correspond paragraphs, The begin button 608 can allow a user to begin the simulation and the end button 610 can allow the user to end the simulation. As the simulation executes, the user can view the behavior of the objects as the actions occur; the user can view the list of events of interest associated with the simulated scenario. In some instances, a timeline view of all events of interest can be displayed (e.g., a lane change at time x, a sudden stop at time y, etc.).).
Regarding claim 22, Kabirzadeh teaches wherein the portion of the plurality of autonomous vehicle metrics comprise an indication of whether each variation of the plurality of variations passed or failed a selected constraint set ( based at least in part on executing the simulated scenario, simulation data can indicate how the autonomous controller responds to each simulated scenario, as described above and determine a successful outcome or an unsuccessful outcome based at least in part on the simulation data. Successful validation of a proposed controller system may subsequently be down-loaded by (or otherwise transferred to) a vehicle for further vehicle control and operation ).
Regarding claim 23, Kabirzadeh teaches wherein the portion of the plurality of autonomous vehicle metrics comprise an indication of a selected action type for each variation of the plurality of variations (the user can create a new waypoint that allows a simulated object to deviate from the trajectory represented in the log data).
Regarding claim 24, Kabirzadeh teaches wherein the batch view comprises a selectable second affordance associated with the plurality of variations, the method further comprising: detecting, at the graphical user interface, a user input indicating a selection of a variation of the plurality of variations using the selectable second affordance; and causing, in response to detecting the user input, the graphical user interface to display at least a portion of the plurality of autonomous vehicle metrics associated with the selected variation (the first waypoint 324 can be associated with a threshold such that if determined cost(s) meet or exceed a threshold, the candidate trajectory can be rejected or modified to determine a trajectory with cost(s) that do not exceed the threshold. Similarly, when the simulated object 318 reaches the second waypoint 326, the behavior of the simulated object 318 has parameters substantially similar (at least in part and within some threshold) to the attributes associated with the object attributes of the second waypoint 326).
Regarding claim 27, Kabirzadeh as modified by Zhou teaches teaches wherein the input comprises only scene data corresponding to an error associated wiparentth the planning system (Zhou, acquiring log data corresponding to a driving failure event occurring in automatic driving simulation of a vehicle).
The same motivation to combine as the parent claim applies here.
Regarding claim 28, Kabirzadeh as modified by Zhou teaches wherein the input comprises only scene data corresponding to a sub-system of the planning system associated with an error ( Step 101, obtaining log data corresponding to a driving failure event occurring in automatic driving simulation of a vehicle, wherein the vehicle is provided with at least one function module related to automatic driving. function module related to automatic driving (a sub-system of the planning system associated with an error)).
The same motivation to combine as the parent claim applies here.
Claims 19-20 are rejected under 35 U.S.C. 103 as being obvious over by over Kabirzadeh (US 11150660 B) in view of Zhou (CN 118296862 A1 machine translation) in view of Ricci (US 20140310075 A1)
Regarding claim 19, Kabirzadeh as modified by Zhou does not explicitly teach but Ricci teaches further comprising causing, in response to detecting the user input, the graphical user interface to display a visualization of a cost associated with the one or more candidate trajectories (claim 19, determine a cost of using each of the one or more alternate routes to the destination; display the cost of using each of the one or more alternate routes to the destination ).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, a scenario editor and simulator, as taught by Kabirzadeh as modified by Zhou, a visualization of a cost associated with the one or more candidate trajectories, as taught by Ricci, as Kabirzadeh, Ricci and Zhou are directed to vehicle trajectory simulation (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using the visualization of a cost associated with the one or more candidate trajectories and predictably applied the scenario editor and simulator to find an optimal control solution.
Regarding claim 20, Kabirzadeh teaches wherein the cost is determined based on a difference between the autonomous vehicle metrics associated with a candidate trajectory and expected autonomous vehicle metrics (cost analysis. For example, the simulation component 314 can determine a cost associated with the simulated vehicle 416 applying a maximum braking (e.g., an emergency stop) which may result in excessive or uncomfortable accelerations for passengers represented in the simulated object 418. Addition-ally, the simulation component 314 can determine a cost associated with the simulated vehicle 416 applying a mini-mum braking force, which may result in a collision or near-collision with the simulated object 418. 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).
Prior Art
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275,277 (CCPA 1968)).
For example, Wang (US20230237212 A1) teaches driving log file extraction in a single planning cycle (Fig. 3, At 328, the system 128 determines a result of the executed simulation of the ADAS/autonomous driving feature simulation scenario is verified, the method 300 ends without simulating additional planning cycles).
Further, Nygaard (US 12030509 B1) teaches simulations in order to evaluate software used to control vehicles in an autonomous driving mode. The simulations are a log-based simulations which are run using log data collected by a vehicle over some brief period of time such as 1 minute or more or less. A search of log data may be conducted in order to identify one or more similar situations based on characteristics of the initial situation. identify bugs or other issues with the software and direct efforts to improve the software. Claim 1, a search of the log data in order to identify log data for one or more similar situations based on characteristics of the initial situation;
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.W./ Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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