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 .
Claim Status
The status of claims 1-42 is:
Claims 1-42 were pending as of the Non-Final Rejection mailed 01/12/2026.
Claims 1, 6, 10-14, 32-33, 35, 37-38, 40, and 42 are amended as of the amendments and remarks received 06/01/2026.
Claims 2-5, 7-9, 15-31, 34, 36, 39, and 41 remain as originally presented as of the amendments and remarks received 06/01/2026.
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.
Claim(s) 1-3, 7-8, 14-17, 24-27, and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (U.S. Patent Publication No 2021/0350147, hereinafter “Yuan”) in view of Bandara et al. (Bandara, W. G. C., Valanarasu, J. M. J., & Patel, V. M. (2022, May). Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 343-350). IEEE., hereinafter “Bandara”).
Regarding claim 1, Yuan discloses a system for predicting one or more drivable paths relative to at least one road segment (Yuan [0074]: “Routing module 307 is configured to provide one or more routes or paths from a starting point to a destination point”), the system comprising:
at least one processor (Yuan [0061]: “Perception and planning system 110 includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs) to receive information from sensor system 115, control system 111, wireless communication system 112, and/or user interface system 113, process the received information, plan a route or path from a starting point to a destination point, and then drive vehicle 101 based on the planning and control information”) programmed to:
access topographical information associated with the at least one road segment (Yuan [0074]: “Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line”, the creation of the topographic map implies the use of topographical information);
generate a topographical representation of the at least one road segment based on the topographical information (Yuan [0074]: “Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line”);
predict at least one drivable path relative to the at least one road segment based on the topographical representation of the at least one road segment (Yuan [0075]: “Based on a decision for each of the objects perceived, planning module 305 plans a path or route for the autonomous vehicle, as well as driving parameters (e.g., distance, speed, and/or turning angle), using a reference line provided by routing module 307 as a basis. That is, for a given object, decision module 304 decides what to do with the object, while planning module 305 determines how to do it”);
receive information identifying the at least one drivable path (Yuan [0076]: “Based on the planning and control data, control module 306 controls and drives the autonomous vehicle, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data”);
store the information identifying the at least one drivable path in at least one map (Yuan [0076]: “Based on the planning and control data, control module 306 controls and drives the autonomous vehicle, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data”, for the path to be utilized in this manner it must be stored so it can be used by the processor).
Yuan does not explicitly disclose the system wherein the processor is programmed to:
input at least the topographical representation of the at least one road segment to at least one trained model, wherein the at least one trained model includes a graph neural network and is configured to predict at least one drivable path along a lane of travel of the at least one road segment based on the topographical representation of the at least one road segment.
However, Bandara teaches the system wherein the processor is programmed to:
input at least the topographical representation of the at least one road segment to at least one trained model (Bandara Fig. 1: input aerial images are the topographical representations), wherein the at least one trained model includes a graph neural network and is configured to predict at least one drivable path along a lane of travel of the at least one road segment based on the topographical representation of the at least one road segment (Bandara Fig. 1 description: “We build graphs in two spaces: (a) spatial space and (b) a projected latent interaction space from feature maps. Graph reasoning in spatial space extracts connectivity between the road segments, whereas reasoning over interaction space delineates roads from other topographies. Nodes connected with lines in (a) denote how road segments are modeled to understand connectivity in the spatial space. Regions marked with different colors in (b) denote how different semantics are segregated for better road delineation in the interaction space”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the graph neural network as taught by Bandara with the system of Yuan because it would allow for highly accurate and computationally efficient processing that can be applied to large-scale image (Bandara Abstract). This motivation for the combination of Yuan and Bandara is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 32, it is rejected under the same analysis as claim 1 above.
Regarding claim 33, it is rejected under the same analysis as claim 1 above along with Yuan’s disclosure of a non-transitory computer-readable medium storing instructions executable by at least one processor (Yuan [0008]: “In another aspect of the disclosure, a non-transitory machine-readable medium having instructions stored therein is provided”).
Regarding claim 2, Yuan discloses the system, wherein the at least one processor is further programmed to distribute the at least one map to at least one vehicle (Yuan [0076]: “Based on the planning and control data, control module 306 controls and drives the autonomous vehicle”).
Regarding claim 3, Yuan discloses the system, wherein the at least one vehicle is configured to navigate autonomously or semi-autonomously based on the at least one map (Yuan [0076]: “Based on the planning and control data, control module 306 controls and drives the autonomous vehicle, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data. The planning and control data include sufficient information to drive the vehicle from a first point to a second point of a route or path using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands) at different points in time along the path or route”).
Regarding claim 7, Yuan discloses the system, wherein the topographical information includes LIDAR output provided by one or more LIDAR devices included in one or more vehicles that traversed the at least one road segment (Yuan [0071]: “Perception module 302 may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of autonomous vehicle. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module 302 can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR”), and wherein the topographical representation of the at least one road segment is generated based on the LIDAR output (Yuan [0071]: “Perception module 302 may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of autonomous vehicle. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module 302 can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR”).
Regarding claim 8, Yuan discloses the system, wherein the topographical information includes a map retrieved from a database (Yuan [0114]: “Referring to FIG. 11B, when a loop closure is detected 1111, target map or segments map generation process 1112 can generate a target map based on previously extract segments for the frames of the loop. Segments map or target map can be a database, a struct or, a class object storing a list of segments of the frames”), and wherein the topographical representation of the at least one road segment is generated based on the map (Yuan [0114]: “Referring to FIG. 11B, when a loop closure is detected 1111, target map or segments map generation process 1112 can generate a target map based on previously extract segments for the frames of the loop. Segments map or target map can be a database, a struct or, a class object storing a list of segments of the frames”).
Regarding claim 14, Yuan dose not explicitly disclose the system, wherein the at least one trained model is further configured to associate a plurality of nodes with the topographical representation and predict the at least one drivable path along the lane of travel of the at least one road segment by predicting at least one connection between at least two of the plurality of nodes.
However, Bandara teaches the system, wherein the at least one trained model is further configured to associate a plurality of nodes with the topographical representation (Bandara Fig. 1 description: “We build graphs in two spaces: (a) spatial space and (b) a projected latent interaction space from feature maps. Graph reasoning in spatial space extracts connectivity between the road segments, whereas reasoning over interaction space delineates roads from other topographies. Nodes connected with lines in (a) denote how road segments are modeled to understand connectivity in the spatial space. Regions marked with different colors in (b) denote how different semantics are segregated for better road delineation in the interaction space”) and predict the at least one drivable path along the lane of travel of the at least one road segment by predicting at least one connection between at least two of the plurality of nodes (Bandara Fig. 1 description: “We build graphs in two spaces: (a) spatial space and (b) a projected latent interaction space from feature maps. Graph reasoning in spatial space extracts connectivity between the road segments, whereas reasoning over interaction space delineates roads from other topographies. Nodes connected with lines in (a) denote how road segments are modeled to understand connectivity in the spatial space. Regions marked with different colors in (b) denote how different semantics are segregated for better road delineation in the interaction space”).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Regarding claim 15, Yuan does not explicitly disclose the system, wherein the plurality of nodes are associated with a graph generated by the graph neural network.
However, Bandara teaches the system, wherein the plurality of nodes are associated with a graph generated by the graph neural network (Bandara Fig. 1 description: “We build graphs in two spaces: (a) spatial space and (b) a projected latent interaction space from feature maps. Graph reasoning in spatial space extracts connectivity between the road segments, whereas reasoning over interaction space delineates roads from other topographies. Nodes connected with lines in (a) denote how road segments are modeled to understand connectivity in the spatial space. Regions marked with different colors in (b) denote how different semantics are segregated for better road delineation in the interaction space”).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Regarding claim 16, Yuan does not explicitly disclose the system, wherein the graph incudes edges representing relationships between the plurality of nodes.
However, Bandara teaches the system, wherein the graph incudes edges representing relationships between the plurality of nodes (Bandara Fig. 1 description: “We build graphs in two spaces: (a) spatial space and (b) a projected latent interaction space from feature maps. Graph reasoning in spatial space extracts connectivity between the road segments, whereas reasoning over interaction space delineates roads from other topographies. Nodes connected with lines in (a) denote how road segments are modeled to understand connectivity in the spatial space. Regions marked with different colors in (b) denote how different semantics are segregated for better road delineation in the interaction space”).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Regarding claim 17, Yuan does not explicitly disclose the system, wherein the at least one trained model is further configured to associate at least one attribute with at least one of the plurality of nodes.
However, Bandara teaches the system, wherein the at least one trained model is further configured to associate at least one attribute with at least one of the plurality of nodes (Bandara Fig. 1: interaction space graph reasoning assigns attributes to nodes).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Regarding claim 24, Yuan discloses the system, wherein the at least one road segment includes a divided road segment (Yuan [0070]: “The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.”).
Regarding claim 25, Yuan discloses the system, wherein the at least one road segment includes a plurality of travel lanes (Yuan [0070]: “The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.”).
Regarding claim 26, Yuan discloses the system, wherein the at least one road segment includes at least one of a roundabout, lane split, or lane merge (Yuan [0070]: “The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.”).
Regarding claim 27, Yuan does not explicitly disclose the system, wherein the at least one trained model is trained based on a plurality of images.
However, Bandara teaches the system, wherein the at least one trained model is trained based on a plurality of images (Bandara Page 5: “The Massachusetts Roads dataset [22] consists of train, validation and test sets with 1108, 14 and 49 images, respectively, each with a size of 1, 500 × 1,500 pixels. Following [40], we fill the training images into size of 1536 × 1536 and then we crop each image into 512 × 512 patches with overlapping window of 256 pixels to make the training set”).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Regarding claim 31, Yuan does not explicitly disclose the system, wherein the at least one trained model is trained based on map information.
However, Bandara teaches the system, wherein the at least one trained model is trained based on map information (Bandara Abstract: “To this end, we propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps”).
It would have been obvious to combine the neural network of Bandara with the system of Yuan for the same reasons used for claim 1 above.
Allowable Subject Matter
Claims 34-42 are allowed.
Claims 4-6, 9-13, 18-23, and 28-30 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.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 32-33 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/AIDAN KEUP/ Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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