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 the Claims
This first non-final action is in response to applicant's amendments/arguments on Feb. 25, 2026. Claims 1-20 are pending and have been considered as follows.
Examiner's response
Applicant’s amendments/arguments with respect to claim(s) 1-20 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 1-5, 7-16, 18-20 are rejected under 35 U.S.C. 103 as being obvious over by Kolski (Autonomous Driving in Structured and Unstructured Environments, Intelligent Vehicles Symposium 2006, June 13-15, 2006, Tokyo, Japan ) in view of Shalev-shwartz (US 20240085922 A1) in view of Cairano (US 20160375901 A1)
Regarding claim 1, Kolski teaches determining a first path for controlling a vehicle (Fig. 1, autonomous Smart car) based at least in part on a pathway indicator (Abstract, Figs. 2-3, and corresponding paragraphs including P560, B. Lane Detection, from several lane detection algorithms, each designed to detect different types of lanes, such as the closest lane to the vehicle, straight lanes, or curved or symmetric lanes. These algorithms rely mainly on the spatial gradient of the image to extract their hypotheses. The results of the individual algorithms are then combined to determine the most probable lane. P559, We use the information from our laser range finder to construct a local grid-based cost map specifying the nearby obstacles and difficult areas to traverse for the vehicle; p 558, When driving on detectable roads, the system uses visual lane detection and laser range data to generate a local map, which is processed by a local planner to guide the vehicle down the lane while avoiding obstacles);
determining that the first path for controlling the vehicle is one or more of unable to be generated or unable to be executed by the vehicle ( abstract, exploits structure in the environment in the form of driving lanes (determining that the first path). when attempting to generate a trajectory base on lanes is unsuccessful or the trajectory generated was not able to be executed, ignore any structure that may exist (abstract, such structure exists but unable to generate a trajectory, ignoring…). In addition, it is well known that when lane markings fade or are missing, vehicles often fail to generate a trajectory because their primary input—camera-based lane detection—is unavailable);
determining, at exclusion of the pathway indicator and at least in part on an inability to execute or generate the first path, a tree structure including successive sets of nodes generated from a current position of the vehicle, a node of the successive sets of nodes being associated with at least one of a heading or a velocity of the vehicle (Abstract, exploits structure in the environment in the form of driving lanes, yet also navigates successfully when no such information; ignore any structure that may exist, Fig. 4 and corresponding paragraphs, P560, no lane information (indicator) to guide or constrain the actions of the vehicle, the vehicle must use a more general approach for navigation, When vehicle in unstructed environments, there is no lane information to guide to constrain the actions of the vehicle (inability to execute or generate the first path). The system employs a global map and planner to generate an efficient trajectory (second path) to desired goal (Figs. 4-6, P561-p562)); and
causing the vehicle to navigate along the second path (Fig. 4 and corresponding paragraphs, When driving in unstructured environments, the system employs a global map and planner to generate an efficient trajectory to a desired goal).
Kolski does not explicitly teach but Shalev-shwartz teaches the specific limitations of the pathway indicator comprising one or more of a marking in an environment, a roadway, a sidewalk, a railing, or boundary identification ([0154] determine visual indicators (e.g., lane markings, a detected vehicle and its location and/or path, a detected traffic light, etc.) that are consistent across the images captured from each of image capture devices 122, 124, and 126).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, autonomous driving in structured and unstructured environments, as taught by Kolski, pathway indicator comprising one or more of a marking in an environment, a roadway, a sidewalk, a railing, or boundary identification, as taught by Shalev-shwartz , as Kolski and Shalev-shwartz are directed to vehicle control (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using pathway indicator comprising one or more of a marking in an environment, a roadway, a sidewalk, a railing, or boundary identification (abstract, Shalev-shwartz ) and predictably applied it to autonomous driving in structured and unstructured environments as taught by Kolski to identify vehicle’s location.
Kolski in view of Shalev-shwartz does not explicitly teach but Cairano teaches that determining, …, a tree structure including successive sets of nodes generated from a current position of the vehicle, a node of the successive sets of nodes being associated with at least one of a heading or a velocity of the vehicle; determining, based on the tree structure including the successive sets of nodes, a second path ([0042] FIG. 4B shows a schematic illustrating determining 401 of the coarse path according to one embodiment. The embodiment generates points of the coarse path as nodes on a tree and updates the routs forming the set of paths on that tree. The current tree in the drivable space 430 is shown with root node 420 representing the current position of the vehicle and includes nodes such as a node 410 connected by links such as a link 411. The tree can also include a target position 421 that the tree has to eventually reach. [0043] In one embodiment, a new point 422 in the drivable space is randomly selected, and the closest node 426 in the tree according to a distance measure is selected. The new node 423 of the tree is obtained from point 422 and node 426 for instance as the point in the line between 426 and 422 within a given distance from 426. Such distance is large for coarse paths and small for fine paths. The new node 423 is connected to the tree by adding the link 429 from new node 423 to a node 425 on the tree in the neighborhood of new node 422 such that the path connecting the nodes 420 and 423 is more optimal according to the cost function than other possible paths. Next, existing links on the tree are updated. For example, the nodes in a neighborhood of new node 423, are evaluated to determine a better path from the root 420 to the node that goes through the new node 423. For instance for node 426 the older link 427 can be dropped and the new link 424 can be added).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, autonomous driving in structured and unstructured environments, as taught by Kolski as modified by Shalev-shwartz, determining a second path based on a tree structure including successive sets of nodes generated from a current position of the vehicle, a well-known technology, as taught by Cairano, as Kolski, Cairano and Shalev-shwartz are directed to vehicle control (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using determining a second path based on successive sets of nodes generated from a current position of the vehicle and predictably applied it to autonomous driving in structured and unstructured environments as taught by Kolski as modified by Shalev-shwartz to optimize vehicle’s route.
Regarding claims 10 and 19, please see the rejection above with respect to claim 1, which is commensurate in scope to claims 10 and 19, with claim 1 being drawn to a method and 10 claims 10 and 19 being drawn to a corresponding computer-readable media and a system respectively.
Please note, Shalev-shwartz taches additional limitation such as computer-readable media storing instructions ([0021], [0052] These processor designs each include multiple processing units with local memory and instruction sets). Cairano teaches
1)the newly added limitation pruning the tree structure including the successive sets of nodes ([0038]A subsequent stage involves removing redundant points in the fine path to obtain a pruned path. A final stage involves smoothing the pruned path. In such a manner, the total computational complexity of the method for determining the modified path is reduced. [0040]) in claims 10 and 19 and
2) the limitation determining, based at least in part on remaining nodes of the successive sets of nodes, a second path; and causing the vehicle to navigate along the second path (claim 8, determining a coarse path defined by a set of points with coarse separation connecting the current position with the target position; refining the coarse path to produce a refined path formed by a set of points with fine separation and close to the selected coarse path; removing redundant point of the refined path that increase the value of the cost function without being useful for avoiding obstacles to produce a pruned path; and smoothing a trajectory connecting the points of the pruned path to produce the modified path.)
Regarding claim 2, Kolski teaches wherein determining that the first path is one or more of unable to be generated or unable to be executed by the vehicle comprises determining an absence of the pathway indicator in the environment (In unstructured environments, where there is no lane Information to guide or constrain the actions of the vehicle, P560).
Regarding claim 11, please see the rejection above with respect to claim 2, which is commensurate in scope to claim 11, with claim 2 being drawn to a method and claim 11 being drawn to a corresponding computer-readable media.
Regarding claim 3, Kolski teaches wherein the first path is determined by a planning system of the vehicle and is based at least in part on the pathway indicator and the second path is determined by a guidance system of the vehicle which is separate from the planning system (Abstract, Local Planning (grid-based, Lane Detection) v Global Planning in Unstructured Environments (a guidance system)).
Regarding claims 12 and 20, please see the rejection above with respect to claim 3, which is commensurate in scope to claims 12 and 20, with claim 3 being drawn to a method and claims 12 and 20 being drawn to a corresponding computer-readable media and a system respectively.
Regarding claim 4, Kolski teaches wherein determining that the vehicle is one or more of unable to be generated or unable to be executed by the vehicle comprises determining that an output of the first path causes the vehicle to at least one of:
Stop (P561, However, such an approach is susceptible to local minima, meaning that it can cause the vehicle to get 'stuck' behind obstacles that reside between its initial position and the goal); call a remote operator for assistance; fail to navigate through the environment; or collide with an object in the environment.
Regarding claim 13, please see the rejection above with respect to claim 4, which is commensurate in scope to claim 13, with claim 4 being drawn to a method and claim 13 being drawn to a corresponding computer-readable media.
Regarding claim 5, Kolski teaches wherein determining, based on (the tree structure including) the successive sets of nodes, the second path comprises:
determining based on first nodes based at least in part on the current position of the vehicle in the environment (Fig.4);
determining a subset of the first nodes (The vehicle projects a set of feasible arcs through the local map from its current position and orientation (arcs for a single speed are shown in red/gray));
determining first costs associated with the subset of the first nodes (a discrete set of variable-length arcs for our vehicle actions, corresponding to different steering angles and vehicle speeds [4]. Each of these arcs represents an action that is feasible from the current vehicle position, orientation, and velocity. We then choose the best of these arcs according to their costs and perhaps also some general objective, such as the amount of distance the arc takes us in our desired direction of travel (e.g. down the road). This arc can then be directly executed by the vehicle.);
determining, based at least in part on the first costs, two or more nodes of the subset of the first nodes having lowest costs among the first costs associated with the subset of the first nodes (Fig. 4);
determining second nodes based at least in part on the two or more nodes (every planning cycle, the vehicle projects out its set of available arcs into this map and computes the cost of each arc based on its distance and the cost of the cells it travels through); and
determining the second path based at least in part on second costs associated with the second nodes and the first costs associated with the subset of the first nodes (the planner selects arcs that minimize the distance between the vehicle and its goal location (the desired parking spot) The set of available arcs are shown in red/gray, with the best arc shown in blue/black. Here, the best arc was selected based on a combination of the cost of the arc itself and the cost of a global path from the end of the arc to the goal (the goal is shown as a filled circle at the right of the figure). The global path from the end of the best arc to the goal is also shown in blue/black. In this example, a purely local planner would have selected the straight arc leading directly to the right, as this brings it closest to the goal in terms of straight-line distance. However, such an arc could cause it to get stuck behind the clump of obstacles in the middle of the map).
Kolski in view of Shalev-shwartz does not explicitly teach but Cairano teaches that determining, based on the tree structure including the successive sets of nodes, a second path.
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, autonomous driving in structured and unstructured environments, as taught by Kolski as modified by Shalev-shwartz, determining a second path based on successive sets of nodes generated from a current position of the vehicle, a well-known technology, as taught by Cairano, as Kolski, Cairano and Shalev-shwartz are directed to vehicle control (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using determining a second path based on successive sets of nodes generated from a current position of the vehicle and predictably applied it to autonomous driving in structured and unstructured environments as taught by Kolski as modified by Shalev-shwartz to optimize vehicle’s route.
Regarding claim 14, please see the rejection above with respect to claim 5, which is commensurate in scope to claim 14, with claim 5 being drawn to a method and claim 14 being drawn to a corresponding computer-readable media.
Regarding claim 7, Kolski teaches wherein determining the second path is based at least in part on: determining, based at least in part on determining the first nodes, multiple contiguous paths through the first nodes(Fig. 4 corresponding paragraphs); and
determining a lowest-cost path from the multiple contiguous paths(Fig. 4 corresponding paragraphs).
Regarding claim 16, please see the rejection above with respect to claim 7, which is commensurate in scope to claim 16, with claim 7 being drawn to a method and claim 16 being drawn to a corresponding computer-readable media.
Regarding claim 8, Kolski teaches wherein determining the second path is based at least in part on: determining a contiguous set of connections between the first nodes and the second nodes (Fig. 4 corresponding paragraphs);
determining a boundary between the current position of the vehicle and an end location (Fig. 4 corresponding paragraphs); and
determining a lowest-cost path from the contiguous set of connections (Fig. 4 corresponding paragraphs).
Regarding claim 17, please see the rejection above with respect to claim 8, which is commensurate in scope to claim 17, with claim 8 being drawn to a method and claim 17 being drawn to a corresponding computer-readable media.
Regarding claim 9, Kolski teaches wherein determining, based on the tree structure including successive sets of nodes, the second path comprises:
determining multiple routes between the current position of the vehicle in the environment and an end location (Fig. 4 corresponding paragraphs); and
determining, as the second path, a route of the multiple routes associated with a lowest cost (Fig. 4, The cost of each of these arcs is computed, based on the cost of the cells the arc travels through (darker areas are more expensive, with black cells representing obstacles). A global path is planned from the end of each arc to the goal (shown as a filled circle on the right side of the map) and the cost of this path is added to the cost of the arc, P561, This algorithm provides very low-cost paths through grid-based representations of an environment).
Regarding claim 18, please see the rejection above with respect to claim 9, which is commensurate in scope to claim 18, with claim 9 being drawn to a method and claim 18 being drawn to a corresponding computer-readable media.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being obvious over by Kolski (Autonomous Driving in Structured and Unstructured Environments, Intelligent Vehicles Symposium 2006, June 13-15, 2006, Tokyo, Japan ) in view of Shalev-shwartz (US 20240085922 A1) in view of Cairano (US2016375901A1) in view of Whittaker (US 20080059015 A1).
Regarding claim 6, Kolski as modified by Shalev-shwartz as modified by Cairano does not explicitly teach but Whittaker teaches the specific limitations of determining hashes associated with the first nodes; determining that a first node of the first nodes and a second node of the first nodes are associated with redundant hashes ([0056]-[0057] Comparison of full rate LIDAR data is computationally expensive and is unattractive for high-speed navigation. To make comparison rates reasonable, points are preferably binned into 2D (x,y) cells and hashed by 2D cell location. Each hash location contains a list of all points within a cell. When a new point hashes to a hash location containing a list of points far from the new point, the list of points is preferably cleared and the new point is inserted. These data structure allow constant time comparison of nearby points by doing a hash lookup in the region of a point of interest) and
discarding the first node based at least in part on the redundant hashes ([0057] With long range sensors, small errors in attitude of the sensor cause large errors in point registration. Comparison of two measurements of the same terrain patch from two different viewpoints can falsely generate an obstacle if the vehicle pose is erroneously pitched or elevated. Because pose errors accumulate over time; it is important to delete points that are old and possibly inaccurate. To accommodate fast deletion, points are inserted into a ring buffer in the order that they are received. Once the ring buffer is full each new point overwrites the current oldest point in the buffer and the hash table).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, autonomous driving in structured and unstructured environments as taught by Kolski as modified by Shalev-shwartz as modified by Cairano, determining hashes associated with the first nodes, as taught by Whittaker, as Kolski, Cairano, Whittaker and Shalev-shwartz are directed to vehicle control (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using determining hashes associated with the first nodes (abstract, Shalev-shwartz) and predictably applied it to autonomous driving in structured and unstructured environments as taught by Kolski as modified by Shalev-shwartzto allow constant time comparison of nearby points by doing a hash lookup in the region of a point of interest.
Regarding claim 15, please see the rejection above with respect to claim 6, which is commensurate in scope to claim 15, with claim 6 being drawn to a method and claim 15 being drawn to a corresponding computer-readable media.
Conclusion
Please refer to form 892 for cited references.
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)).
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/J.W./ Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/ Supervisory Patent Examiner, Art Unit 3666
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