DETAILED CORRESPONDENCE
This Office action is in response to the application filed 9/17/2024, with claims 1-18 pending.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/18/24 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: the word “and” in line five should be -- an -- . Appropriate correction is required.
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 1-4 and 8-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipate by Xu, Shengdong, et al. “Real-time 3D navigation for autonomous vision-guided MAVs.” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015 hereinafter “Shengdong”.
Claims 1 and 15. Shengdong teaches a method performed by a computing system for identifying a travel path based on locations for a vehicle to travel from a current location to a target location, the method comprising:
accessing indications of object locations associated with objects that the vehicle is to avoid and empty locations not associated with objects that the vehicle is to avoid (figs. 2 and figs. 3 illustrates this element in which the vehicle is avoiding is avoiding both empty and object indicated location. While fig. 6 illustrates detection of objects. );
for each empty location and each empty neighbor location, calculating a cost of traveling from that empty location to that empty neighbor location (pg. 52, sec. A discusses a movement cost and reads on this element as such—“The cost of a primitive motion is proportional to its path length except for the turning movement which has a cost corresponding to a 25cm path length. Backward movements are penalized with a weighted factor greater than 1 as obstacles at the rear cannot be observed with a forward-facing camera, and thus, we want to avoid backward movements if possible.”); and
applying a minimal cost path algorithm to a graph to determine a travel path between the current location and the target location, where vertices of the graph represent locations, edges of the graph connect empty neighbor voxels, and the costs are associated with the edges (pg. 55, sec. C, col. 2 reads on this element as such—“After creating both the local regular state lattice and the global octree-based state lattice, the next step is to perform graph search to find an optimal path. Any edge in the octree-based state lattice can be decomposed into a series of high-resolution primitive motions. The edge corresponds to a feasible path whose costs and decompositions have been pre-computed and stored in the multi-resolution path lookup table described in the previous section. The octree-based state lattice is dynamically changing with constant 3D map updates and as the robot moves. 1) Optimal Path Finding: To show the efficiency of our octree-based state lattice, we use a simple A* graph search algorithm to generate optimal paths. Of course, it is possible to implement AD* or any other variant of the A* graph search algorithm for the octree-based state lattice. The edge costs between octree node states can be obtained from the multi-resolution path lookup table. The graph search algorithm finds an optimal trajectory that consists of octree node states.”).
Claim 2. Shengdong teaches the method of claim 1 further comprising specifying a travel direction for the vehicle as the direction of the neighbor location of the vehicle location that is along the travel path (pg. 54, sec. A reads on this element as such—“The pre-computed canonical set of maximum-resolution motion primitives consists of turning on the spot in both the left and right directions, moving up and down vertically, and moving forward and backward in several directions. The cost of a primitive motion is proportional to its path length except for the turning movement which has a cost corresponding to a 25cm path length”).
Claim 3. Shengdong teaches the method of claim 2 further comprising directing the vehicle to travel in the travel direction (pg. 54, sec. A, reads on this element as such—“The primitive motions can be seen as a canonical set of short feasible control samples that satisfy the differential constraints of the system.”).
Claims 4 and 16. Shengdong teaches the method of claim 1 wherein the costs are based on a distance transform that identifies the distance from an empty location to the nearest object location (pg. 56, sec. C. col. 2, para. 4 reads on this element as such—“The octree-based path planner is constrained to plan trajectories through the centers of octree node states and states in the local regular 3D state lattice. By using an octree based state lattice, we greatly reduce the number of candidate states for the A* algorithm to expand. As a result, the A* algorithm is able to find the best path in a short time.”).
Claim 8. Shengdong teaches the method of claim 1 wherein the minimal cost path algorithm is a Dijkstra-based algorithm (on pg. 55, col. 1, last para. reads on this element as such—“The precomputation step shown in Algorithm 2 can be performed via the Dijkstra's algorithm”).
Claim 9. Shengdong teaches the method of claim 6 wherein the minimal cost path algorithm employs a Fibonacci heap (pg. 55 teaches algorithm 1 and 2. While optimal path finding is taught on pg. 56, col. 2 – pg. 57, col. 1).
Claim 10. Shengdong teaches the method of claim 1 further comprising, for each of a plurality of time intervals:
calculating current costs based on a current vehicle location, a current target location, and current object locations (pg. 55, sec. C, col. 2 reads on this element as such—“After creating both the local regular state lattice and the global octree-based state lattice, the next step is to perform graph search to find an optimal path. Any edge in the octree-based state lattice can be decomposed into a series of high-resolution primitive motions. The edge corresponds to a feasible path whose costs and decompositions have been pre-computed and stored in the multi-resolution path lookup table described in the previous section. The octree-based state lattice is dynamically changing with constant 3D map updates and as the robot moves.); and
applying the minimal cost path algorithm based on the current costs to identify a current travel path (pg. 55, sec. C, col. 2 Optimal Path Finding: To show the efficiency of our octree-based state lattice, we use a simple A* graph search algorithm to generate optimal paths).
Claim 11. Shengdong teaches the method of claim 10 further comprising, for each of the plurality of time intervals, specifying a current travel direction as the direction of the empty neighbor location of the current vehicle location that is along the current travel path (p. 55, col. 1, last two para. reads on this element as such—“However, it is possible to represent the path between the two states as a sequence of motion primitives which can be precomputed beforehand for every possible pair of octree levels. We precompute a lookup table; given a small fixed-size state lattice, for a state located at the center of the lattice and with a given heading direction, we store the cost and decomposition of a path from that state to all other states in the state lattice. The precomputation step shown in Algorithm 2 can be performed via the Dijkstra's algorithm. The lookup table can be used to inform the path planner how two octree node states connect to each other and the cost of a feasible path between the two states.
Claim 12. Shengdong teaches the method of claim 10 wherein a time interval is adjusted based on risk tolerance of the vehicle colliding with an object (pg. 58, col. 2, second from last para. describes this element as such—“The MAY was able to perceive the environment and incrementally build a 3D map in real-time. At the same time, the octree-state-lattice-based path planner was constantly able to provide near-optimal trajectories which were collision-free and led the MAY to the goal state. Throughout the whole process, our path planner took less than one second to find
a near-optimal path.”).
Claim 13. Shengdong teaches the method of claim 1 wherein the objects exclude objects that are more than a remote distance from the current vehicle location (pg. 57, col. 1, para. 2 describes a scenario that reads on this element as such—“In the above example, there are only four tree node states along the path but there are many more waypoints along the full path. This is because the actual path from one tree node
state to another is decomposed into a series of high-resolution primitive motions.” ).
Claim 14. Shengdong teaches the method of claim 13 wherein the remote distance is adjusted based on risk tolerance of the vehicle colliding with the object (pg. 58, col. 2, second para. from bottom reads on this element as such—“At the same time, the
octree-state-lattice-based path planner was constantly able to provide near-optimal trajectories which were collision-free and led the MAY to the goal state.”).
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 (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 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 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shengdong in view of Chan et al., US 2017/0015415 hereinafter “Chan”.
Claims 5 and 17. Shengdong teaches the method of claim 4; however, Shengdong is silent on the term stationary. Yet, Chan teaches
further comprising prior to travel, calculating stationary costs based on stationary objects; and during travel, calculating moving costs based on moving objects; wherein the calculating calculates the cost for traveling from an empty location to an empty neighbor location to be a minimum of the stationary cost and the moving cost for the empty neighbor location (The following cited section from Chan read on stationary cost calculating cost while enroute to the stationary charging station and the cost of moving from one location to another as such—“ The system may report or verify the location to the UAV and the UAV may accept and select a charge location and transaction terms (e.g. contract terms under an existing short-term or long-term contract or spot contracting/agreement at the power source at the time of repowering). According to an exemplary embodiment, an operator of a fleet of UAVs may contract with the system to
repower multiple UAVs, see for example FIGS. 3A-3B , 20 and 39-4 1 . As indicated, transaction terms may include costs for energy, time of energy/power transfer, location, time of day/day of week, priority, etc . The UAV may roost or park at the charge location; according to an exemplary embodiment the system may allow a UAV to repower/charge while roosted (parked) at a charging station or other location); according to an exemplary embodiment, the UAV may roost or park and wait for a charge station (e.g. location along a power line/wire) for repowering to become available. Upon completion of repowering, the UAV may execute with the system a transaction including billing and payment”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Chan with the invention of Shengdong because such combination would provide a set of location for the UAV to land (see [0107], Chan).
Claims 6, 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shengdong in view of Rathbun, David, et al. “An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments.” Proceedings. The 21st digital avionics systems conference. Vol. 2. IEEE, 2002, hereinafter “Rathbun”.
Claim 6. Shengdong teaches the method of claim 1; however, Shengdong is silent on the term density. Yet, Rathbun teaches wherein the costs are calculated based on environmental measures that include a transform distance measure, an object density measure, and a zone permeability measure (pg. 8.D.2-5, col. 2 teaches on probability density as it relates to objects).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Rathbun with the invention of Shengdong because such combination would provide safe route through a field of obstacles at uncertain locations (see Abstract, Rathbun).
Claims 7 and 18. Shengdong teaches the method of claim 6; however, Shengdong is silent on the term density. Yet, Rathbun teaches wherein the calculating calculates the cost of traveling from an empty location to an empty neighbor location to be a minimum of a cost associated with the transform distance measure, a cost associated with the object density measure, and a cost associated with the zone permeability measure (pg. 8.D.2-5, col. 2 teaches on probability density as it relates to objects. While pg. 8.D.2-5, col. 1 teaches that “the EA-based path planner attempt to find a path that minimize [the] cost function”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Rathbun with the invention of Shengdong because such combination would provide safe route through a field of obstacles at uncertain locations (see Abstract, Rathbun).
Conclusion
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/A.D.T/Examiner, Art Unit 3661
/RUSSELL FREJD/Primary Examiner, Art Unit 3661