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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 13, 2026, has been entered.
Status of Claims
This Office Action is in response to the Request for Continued Examination filed July 13, 2026. Claims 1-4, 6-8, 10-14, 16-18, and 20, are presently pending and presented for examination.
Response to Amendment
The examiner recognizes that all original rejections made under 35 U.S.C. § 101 previously stated for the original claims 1-4, 6-14, and 16-20, are overcome by the amendments made by the applicant unless stated otherwise below.
Response to Arguments
Applicant argues that the amendments made to the claims integrate the invention to a practical application and overcome the 35 U.S.C. § 101 rejection of record.
Applicant’s arguments, see Applicant’s Remarks, filed July 13, 2026, with respect to the section titled Patentability of Claims 1-4, 6-8, 10-14, 16-18, and 20, have been fully considered and are persuasive. The 35 U.S.C. 101 Rejection of the Final Rejection filed January 15, 2026, has been withdrawn.
Applicant’s remaining arguments with respect to claims 1-4, 6-8, 10-14, 16-18, and 20, 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.
An updated and detailed rejection follows below.
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 1-4, 6-8, 10-14, 16-18, and 20, are rejected under 35 U.S.C. 103 as being unpatentable over Ravenscroft (US 20120158280, already of record), in view of White et al. (US 20230408288; hereinafter White, already of record), and further in view of Ali et al. (US 20240133693; hereinafter Ali).
Regarding Claim 11,
Ravenscroft teaches
A non-transitory computer-readable medium having computer-executable instructions stored (Ravenscroft: Paragraph [0028]) thereon that, in response to execution by one or more processors of a computing system, (Ravenscroft: Paragraph [0027]) cause the computing system to perform actions for planning a navigation route for an autonomous vehicle, (Ravenscroft: Abstract and Paragraph [0029]) the actions comprising:
receiving, by the computing system, mission information including a start location and a goal location; (Ravenscroft: Paragraph [0100])
generating, by the computing system, a representation of an operation area that includes the start location and the goal location; (Ravenscroft: Paragraph [0100], FIG. 5)
updating, by the computing system, the representation of the operation area based on one or more temporary obstacles; (Ravenscroft: Paragraph [0047], FIG. 5 (Elements 508 and 510))
...
determining, by the computing system, the navigation route using the cost-to-go map of the operation area. (Ravenscroft: Paragraph [0036]-[0038], [0046], [0049], [0103])
Ravenscroft does not teach
...
providing, by the computing system, the representation of the operation area, the start location, and the goal location as input to a machine-learning model to generate a cost-to-go map of the operation area; and
...
However in the same field of endeavor, White teaches
...
providing, by the computing system, the representation of the operation area, the start location, and the goal location as input to a machine-learning model to generate a cost-to-go map of the operation area... (White: Paragraph [0054], [0086], Machine Learning Model 211) and
...
It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the navigation route planning system of Ravenscroft, with the machine learning model of White, for the benefit of assisting in assessing the risk to the drone and/or its cargo within a geographic and/or three-dimensional region. (White: Paragraph [0107])
Ravenscroft, in view of White, does not teach
...
...wherein the cost-to-go map generated by the machine learning model includes a direction of steepest decent for a plurality of points in the operation area; and
determining, by the computing system, the navigation route using the cost-to-go map of the operation area by following a path of steepest descent from the start location to the goal location as indicated by the directions of steepest descent in the cost-to-go map without comparing a cost of traveling from any point in the cost-to-go map to any other point in the cost-to-go map; and
controlling, by the computing system, the autonomous vehicle by transmitting instructions to the autonomous vehicle to cause the autonomous vehicle to autonomously navigate the navigation route from the start location to the goal location.
However in the same field of endeavor, Ali teaches
...
...wherein the cost-to-go map generated by the machine learning model includes a direction of steepest decent for a plurality of points in the operation area; (Ali: FIG. 4 and Paragraph [0059]-[0061], [0063]; “The method of FIG. 4 further comprises selecting (450), in dependence upon the total cost of each flight path, an optimal flight path from the plurality of flight paths. Selecting (450), in dependence upon the total cost of each flight path, an optimal flight path from the plurality of flight paths may be carried out by the route instructions (148) selecting the flight path with the lowest cost.”; The flight plan with the lowest cost is made up of various cells that the UAV would need to pass through and therefore includes the direction of steepest descent.) and
determining, by the computing system, the navigation route using the cost-to-go map of the operation area by following a path of steepest descent from the start location to the goal location as indicated by the directions of steepest descent in the cost-to-go map without comparing a cost of traveling from any point in the cost-to-go map to any other point in the cost-to-go map; (Ali: Paragraph [0063]; “The optimal flight path selected by the route instructions (148) in the server (140) may be then provided to the UAV via wireless transmission. Alternatively, the route instructions may be executed by the UAV (102) or the control device (120).” The flight plan is made up of the various cells that the UAV must pass through on the way from the start location to the destination location which make up the path of steepest descent.) and
controlling, by the computing system, the autonomous vehicle by transmitting instructions to the autonomous vehicle to cause the autonomous vehicle to autonomously navigate the navigation route from the start location to the goal location. (Ali: Paragraph [0034])
It would be obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to modify the navigation route planning system of Ravenscroft, in view of White, with the cost-to-go map analysis of Ali, for the benefit of regulating air traffic and ensure the safe navigation of the UAVs. (Ali: Paragraph [0002])
Regarding Claim 12,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein the temporary obstacles are represented by one or more Volume4 shapes that represent a three-dimensional volume, a start time, and an end time. (Ravenscroft: Paragraph [0013], [0042]-[0046])
Regarding Claim 13,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 12, wherein the obstacles include one or more of a temporary flight restriction or a timed space reservation for another autonomous vehicle. (Ravenscroft: Paragraph [0047]; “Obstruction or obstacle detection algorithms described herein may also aggregate flight paths passing to or from a given airport into aircraft approach patterns or segments for the airport. In addition, these algorithms may model "keep out" zones as obstacles or obstructions. Generally, the various algorithms described herein may be applied within any convenient altitude within the atmosphere that is accessible to any type of UAV.” The model accounts for aircraft approach patterns (i.e. timed space reservations for aircrafts) and/or “keep out” zones which are areas with restricted airspace access (i.e. flight restricted areas).)
Regarding Claim 14,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein the representation of the operation area includes a raster representation, a digital surface model representation, or a representation generated by a function approximation technique. (Ravenscroft: Paragraph [0105]-[0109], FIG. 6)
Regarding Claim 16,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein the machine-learning model is trained by:
executing a route planning technique using the goal location as a start node and the start location as a goal node to generate cost-to-go values for nodes of the operation area; (Ravenscroft: Paragraph [0100], FIG. 5) and
using the cost-to-go values as labels for a set of training data that includes the terrain map and the goal location for training the machine-learning model. (White: Paragraph [0031], [0034], [0036]-[0037], [0105]-[0106])
The motivation to combine Ravenscroft, White, and Ali, is the same as stated for Claim 11 above.
Regarding Claim 17,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein determining the navigation route using the cost-to-go map of the operation area includes selecting nodes based on a direction of steepest descent of the cost values of the cost-to-go map. (Ravenscroft: Paragraph [0083], [0101]-[0103], FIG. 5; Examiner notes that paragraph [0066] of the instant application’s specification states “If the cost-to-go map includes indications of the direction of steepest descent, these indications can be used directly to determine the route without significant further computation. Even if the cost-to-go map does not include such indications, the direction of steepest descent may be determined by sampling several neighboring points and choosing the neighboring point having the lowest cost-to-go value.” The functions of Ravenscroft teach calculating a cost for flight plan based on distance, fuel, obstacles, etc. The system sets an acceptable upper bound and recalculates the flight plan if the total cost of the flight plan exceeds the upper bound in an attempt to reduce said total cost. Once a flight path is made with a cost lower than the upper bound, and it is determined to be satisfactory, no more computation is conducted and the flight path is accepted. By editing the plan based on the cost and changing how the UAV is routed to the several nodes based on the total cost of the flight plan and the cost to fly in between nodes, the system of Ravenscroft is searching for the path of steepest decent in an iterative manner to achieve the most efficient flight path for the UAV.)
Regarding Claim 18,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein cost values of the cost-to-go map include vectors representing one or more of energy usage to reach the goal location, (Ravenscroft: Paragraph [0057]) a time to reach the goal location, (Ravenscroft: Paragraph [0022]) a cumulative expected noise impact, a control effort, (Ravenscroft: Paragraph [0061]-[0063]) or a cumulative proximity to obstacles. (Ravenscroft: Paragraph [0046]; “...if a given area is highly congested, the algorithms may assign a correspondingly high cost to this area, thereby reducing the possibility of a flight solution passing through this congested area. In another example, the algorithms may model this highly congested area as an obstruction.” If an area is congested then the cumulative proximity to obstacles (i.e. other UAVs) is high.)
Regarding Claim 20,
Ravenscroft, in view of White, and further in view of Ali, teaches
The computer-readable medium of claim 11, wherein the autonomous vehicle is an unmanned aerial vehicle (UAV). (Ravenscroft: Abstract and Paragraph [0013])
Regarding Claim 1, the claim is analogous to Claim 11 limitations and is therefore rejected under the same premise as Claim 11.
Regarding Claim 2, the claim is analogous to Claim 12 limitations and is therefore rejected under the same premise as Claim 12.
Regarding Claim 3, the claim is analogous to Claim 13 limitations and is therefore rejected under the same premise as Claim 13.
Regarding Claim 4, the claim is analogous to Claim 14 limitations and is therefore rejected under the same premise as Claim 14.
Regarding Claim 6, the claim is analogous to Claim 16 limitations and is therefore rejected under the same premise as Claim 16.
Regarding Claim 7, the claim is analogous to Claim 17 limitations and is therefore rejected under the same premise as Claim 17.
Regarding Claim 8, the claim is analogous to Claim 18 limitations and is therefore rejected under the same premise as Claim 18.
Regarding Claim 10, the claim is analogous to Claim 20 limitations and is therefore rejected under the same premise as Claim 20.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULO ROBERTO GONZALEZ LEITE whose telephone number is (571)272-5877. The examiner can normally be reached Mon-Fri: 8:00 am - 4:30 pm.
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/P.R.L./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
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