DETAILED ACTION
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 .
Status of Claims
Claims 1, 2, and 4-21 of US Application No. 18/886,057 are currently pending and have been examined. Applicant amended claims 6, 8, 10, and 15, canceled claim 3, and added claim 21.
Response to Arguments/Amendments
Applicant’s arguments regarding the rejections of claims 1, 2, 4, and 7 under 35 U.S.C. 102, see REMARKS, filed 24 August 2026, have been fully considered but are not persuasive.
Applicant first argues that Jain does not teach “obtaining, based on the updating the at least one of parameters, test data comprising information about a second drivable area for testing”. The Examiner respectfully disagrees.
Even if Applicant’s assertions in support of their argument are true, Jain still teaches the instant limitation. In this regard, Jain first discloses a framework 200A for training a machine-learning-based trajectory generator, e.g., an ML-based trajectory generator. See Fig. 2A and ¶ [0037]. The training framework includes updating and optimizing the generator, such as by adjusting trajectory parameters or parameter distributions. See Fig. 2A and ¶ [0054]. In other words, Jain teaches updating parameters of trajectory generator. Jain further discloses that a process 400B for generating and selecting appropriate trajectories to navigate an autonomous vehicle is executed after trajectory generator has been trained to meet pre-determined criteria. See Fig. 4B and ¶ [0061]. In process 400B, perception data of the surrounding environment is captured at 402. See at least Fig. 4B and ¶ [0061]). Since process 400B, including capturing perception data at 402, is performed after the generator is optimized, the perception data is obtained based on updating the parameters. Therefore, Jain teaches obtaining, based on the updating the at least one of parameters, test data comprising information about a second drivable area for testing (i.e., perception data is obtained after the trajectory generator has been trained to meet pre-determined criteria). Therefore, Applicant’s arguments are not persuasive.
Applicant then argues that Jain fails to teach “controlling, based on the second driving path for testing, autonomous driving of the vehicle”. The Examiner respectfully disagrees.
Even if Applicant’s assertions in support of their argument are true, Jain still teaches the instant limitation. In this regard, Jain discloses a process 400B for generating and selecting appropriate trajectories to navigate an autonomous vehicle. See Fig. 4B and ¶ [0061]. During process 400B, perception data of the surrounding environment is captured at step 402. See Fig. 4B and ¶ [0061]. At step 405, a human-like trajectory T’ is generated based on a heuristic-based trajectory T and features/objects extracted from the perception data. Finally, at step 408, the AV may determine one or more vehicle operations to navigate based on the human-like trajectory. See at least Fig. 4B and ¶ [0062]). Therefore, Jain teaches controlling, based on the second driving path for testing, autonomous driving of the vehicle (i.e., AV is operated based on the human-like trajectory). Therefore, Applicant’s arguments are not persuasive.
The previous rejections of claims 3, 8, 10-13, and 16-19 under § 102 are withdrawn. Applicant canceled claim 3, thereby rendering the previous rejection of claim 3 moot. Applicant amended claim 8 and independent claim 10 to include limitations not taught by the prior art of record. Claims 13 and 16-19 depend from claim 10. Therefore, the rejections of claims 8, 10-13, and 16-19 under § 102 are withdrawn.
The previous rejections of claims 5, 6, and 9 under 35 U.S.C. 103 are maintained for the same reasons as claim 1.
The previous rejections of claims 14, 15, and 20 under § 103 are withdrawn because they depend from an allowed claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 7, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jain et al. (US 2022/0066459 A1, “Jain”).
Regarding claims 1 and 21, Jain discloses using machine learning models for generating human-like trajectories and teaches:
obtaining training data comprising information about a first drivable area (sensors, e.g., camera, to capture perception data, e.g., road lines – see at least ¶ [0025]; human-driven trajectories – see at least ¶ [0024]);
outputting, based on the information about the first drivable area, a first driving path in the first drivable area (computer algorithm may generate a human-like trajectory based on the perception data, e.g., road boundaries – see at least Fig. 2A and ¶ [0054]);
determining, based on the first driving path and the training data, a driving path loss (loss function 224 may determine a loss value based on comparison of human-like trajectory 203 and human-driven trajectory – see at least Fig. 2B and ¶ [0054]);
updating, based on the driving path loss, at least one of parameters associated with the outputting the first driving path (the loss valued determined by loss function 224 may be fed to generator 202 to cause the generator to be updated and optimized – see at least ¶ [0054]; adjusting one or more trajectory parameters or parameter distributions to generate the human-like trajectory 203 – see at least ¶ [0054]);
obtaining, based on the updating the at least one of parameters, test data comprising information about a second drivable area for testing (process 400B is executed after trajectory generator has been trained to meet pre-determined criteria – see at least Fig. 4B and ¶ [0061]; at 402, perception data of the surrounding environment is captured – see at least Fig. 4B and ¶ [0061]);
outputting, based on the information about the second drivable area for testing, a second driving path for testing in the second drivable area for testing (at 405, a human-like trajectory T’ is generated based on a heuristic-based trajectory T and features/objects extracted from the perception data – see at least Fig. 4B and ¶ [0061]-[0062]); and
controlling, based on the second driving path for testing, autonomous driving of the vehicle (at 408, AV may determine one or more vehicle operations to navigate based on the human-like trajectory 406 – see at least Fig. 4B and ¶ [0062]).
Regarding claim 2, Jain further teaches:
wherein the information about the first drivable area comprises information about a boundary line of the first drivable area (sensors, e.g., camera, to capture perception data, e.g., road lines – see at least ¶ [0025]; human-driven trajectories may generate a human-like trajectory based on the perception data, e.g., road boundaries – see at least Fig. 2A and ¶ [0054]).
Regarding claim 4, Jain further teaches:
generating, based on a training data generation model and the information about the first drivable area, the training data, wherein the training data comprises an improved driving path corresponding to the first drivable area (the training process is repeated until generator 202 generates human-like trajectories that meet pre-determined criteria – see at least ¶ [0054]; another trajectory pair T-H – see at least ¶ [0054]; perception data of the surrounding environment associated with trajectory 201 – see at least ¶ [0054]; updated human-like trajectory 203 based on updated model 202 – see at least Fig. 2B and ¶ [0054), and wherein the information about the first drivable area is input before obtaining the training data (initial heuristic-based trajectory 201 based on perception data – see at least Fig. 2A and ¶ [0041]).
Regarding claim 7, Jain further teaches:
wherein the obtaining the test data comprises generating, based on at least a portion of a driving image obtained from an image sensor and high definition map information, the test data, wherein the at least the portion of the driving image is obtained before obtaining the test data (human-like trajectory is determined based on the heuristic-based trajectory 201, which is generated by the motion planning algorithm 212 based on the perception data 211 – see at least ¶ [0041]; perception data may include camera data – see at least ¶ [0025]-[0026]; perception data may include map data generated based on previously collected perception data – see at least ¶ [0026]).
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Nilsson et al. (US 2015/0073663 A1, “Nilsson”).
Regarding claim 5, Jain fails to teach but Nilsson discloses maneuver generation for automated driving and teaches:
wherein the generating the training data comprises generating, based on a quadratic programming scheme, the improved driving path (model predictive control problem formulated as a quadratic program – see at least ¶ [0050]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning models for generating human-like trajectories of Jain to provide for generating paths based on a quadratic programming scheme, as taught by Nilsson, with a reasonable expectation of success, because model predictive control formulated as a quadratic equation allows for trajectories to be determined while accounting for system constraints (Nilsson at ¶ [0049]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Yaghoubi et al. (US 2024/0059302 A1, “Yaghoubi”).
Regarding claim 6, Jain further teaches:
wherein the information about the first drivable area is obtained based on at least a portion of: first driving data generated as the vehicle travels on a road (AVs use sensors, e.g., camera, to capture perception data, e.g., road lines – see at least ¶ [0025]).
Jain fails to teach wherein the information about the first drivable area is obtained based on at least a portion of second driving data generated based on a driving simulation process.
However, Yaghoubi discloses a control system utilizing rulebook scenario generation and teaches:
wherein the information about the first drivable area is obtained based on at least a portion of second driving data generated based on a driving simulation process (simulated scenario may be used to generated a simulated trajectory to test the control system – see at least ¶ [0106]-[0108]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning models for generating human-like trajectories of Jain to provide for generating data based on a driving simulation process, as taught by Lee, with a reasonable expectation of success, because the simulation may be used to test the control system (Yaghoubi at ¶ [0109]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jain in view of Lee et al. (US 2024/0400095 A1, “Lee”).
Regarding claim 9, Jain fails to teach but Lee discloses learning vehicle trajectories and teaches:
outputting coordinates of prediction points on the first driving path, wherein the driving path loss is associated with a value representing how far the prediction points are from target values (ground truth data may be compared to predicted trajectories of the model to calculate a loss value, such as, for example, via least absolute deviations, least square errors – see at least ¶ [0016]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the machine learning models for generating human-like trajectories of Jain to provide for determining the path loss, as taught by Jain, with a reasonable expectation of success, because it would reduce the number of heuristic-based trajectories necessary to determine a control trajectory for the vehicle to follow (Lee at ¶ [0012]).
Allowable Subject Matter
Claims 10-20 are allowed.
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AARON L TROOST whose telephone number is (571)270-5779. The examiner can normally be reached Mon-Fri 7:30am-4pm.
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/AARON L TROOST/Primary Examiner, Art Unit 3666
