DETAILED ACTION
This Non-Final action is responsive to the continuation and IDS filed 3/11/2026. (note the previous notice of allowability has been withdrawn in light of the new grounds of rejection).
In the continuation Claims 1-4, 6-13, 15-19 and 21-25 are pending. Claims 5, 14 and 20 remain canceled. Claims 1, 10 and 19 are the independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 2-4, 6-9, 11-13, 15-18 and 21-25 are 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. Please note allowability status of claims are subject to change should relevant prior art be discovered anytime during prosecution.
Information Disclosure Statement
5. The information disclosure statement (IDS) submitted on 3/11/2026 has been entered, and considered by the examiner.
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.
6. Claims 1, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Florent Altche herein Florent (NPL-Partitioning of the Free Space-Time for On-Road Navigation of Autonomous Ground Vehicles, Dec. 2017, IEEE, pgs. 2126-2133) in view of Wilkinson (U.S. Pub 2021/0080955, filed Dec. 13, 2019) further in view of Sheckells (U.S. 10,379,538, filed Dec. 15, 2017).
Regarding Independent claim 1, 10 and 19, Florent discloses system comprising:
a memory device; and a processing device, operatively coupled to the memory device, to: receive a set of input data including a roadgraph, the roadgraph comprising an autonomous vehicle driving path and one or more synthetic objects (see pgs. 2126, section 1 & pg. 2127, section 2, discloses a transition graph built from a road environment that contains ego-vehicle path and obstacles. The obstacles are modeled as rectangles in a Frenet coordinate space wherein the graph encodes all spatial relationships between the driving path and those objects. The synthetic objects is the set of modeled rectangular obstacle vehicles whose future trajectories are assumed known);
determine that the autonomous vehicle driving path is affected by one or more obstacles (see pgs. 2126, section 1 & pg. 2127, section 2, discloses “free space-time” as the complement of the obstacle space-time in which the entire framework is triggered by the presence of obstacles affecting the vehicles driving path. The partition algorithm identifies which portions of the driving space are blocked by each obstacle);
select, from the set of candidate paths, a coarse-optimized path associated with an optimal cost value (see paragraphs 1-2, pg. 2129, discloses that the graph exploration phase selects an optimal path. The graph-selected path is explicitly a coarse solution that identifies which cells/maneuver variant to follow before the fine optimization step);
modify the coarse-optimized path using continuous path optimization to obtain a fine-optimized path (see pgs. 2126, section 1, discloses a two-stage architecture where the graph selection (course) is followed by continuous trajectory optimization (fine). Further disclosing a divide-and-conquer decomposition); Florent teaches identification of multiple candidate collision-free paths with cost values through transition graphs. In addition, teaching the geometric alignment of scene features to path geometry through its Frenet coordinate framework. Every obstacle region, road boundary and partition cell Is expressed relative to the reference path’s geometry. However, the cost metric does not include curvature of the path nor that the features are aligned to a fine-optimized path.
Wilkinson discloses:
identify a set of candidate paths that avoid the one or more obstacles, each candidate path of the set of candidate paths being associated with a cost value based on a curvature of a respective candidate path (see abstract & Fig. 8 & paragraphs 5, 7 and 171, discloses identifying candidate paths with determined costs by using branching policies to avoid objects. Furthermore, supporting comparison of determined curvature of a path in paragraph 54); Wilkinson discloses in paragraph 68 that in some implementations the optimization planner can be or includes an iterative solver comprising an iterative linear quadratic regulator. Wilkinson however fails to explicitly describe modifying the coarse-optimized path using continuous path optimization and instead uses an iterative algorithm.
Sheckells discloses:
generate a synthetic scene based on the fine-optimized path that is obtained by modifying the coarse-optimized path using the continuous path optimization, wherein placement of synthetic scene features is aligned to geometry of the fine-optimized path (see col. 2, lines 55-58, which teaches use of the methods described above with simulated data);
train a machine learning model to navigate an autonomous vehicle based on the synthetic scene generated based on the fine-optimized path obtained by modifying the coarse-optimized path (see cols. 13-14, discloses neural networks training and AV receiving an output trajectory).
cause an autonomous vehicle control system to modify the autonomous driving path in view of one or more outputs of the trained machine learning model (see col. 13, lines 34-55, discloses using machine learning algorithms that produce output. Further in col. 18, lines 7-25, discloses outputting the segment/trajectory data to a vehicle controller to control the autonomous vehicle). It would have been obvious for one of ordinary skill in the art before the effective filing date of the application to have applied a trajectory smoother to an optimized trajectory based on cost to generate a smooth and continuous path. One motivation is to further “optimize” the cost-effective trajectory has described by Sheckells in col. 12, lines 25-35.
It is noted that any citation [[s]] 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. [[See, MPEP 2123]]
Response to Arguments
7. No arguments have been filed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANGLESH M PATEL whose telephone number is (571)272-5937. The examiner can normally be reached on M-F from 11 am to 7 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop, can be reached at telephone number 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manglesh M Patel/
Primary Examiner, Art Unit 3665
4/17/2026
Prosecution Insights