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 Claims
This Office Action is in response to the aforementioned Application filed October 02, 2025. Claims 1-4, 6-14, 16-20, are presently pending and presented for examination.
Response to Amendment
The examiner recognizes that all original objections previously stated for the original claims 2 10, 12, and 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.
Examiner respectfully disagrees. The amended limitations merely serve to further specify the mental process of providing a start and end location for a route and creating navigation instructions for the route based on a cost analysis which may be reasonably performed by a person in the mind or with pen and paper as will be further described below.
Applicant argues that Ravenscroft does not teach the limitations of claim 6 and 16, specifically that there is no teaching or suggestion for a goal location being used as a start node and a start location being used as a goal node for route planning.
Examiner respectfully disagrees. The cited paragraph of Ravenscroft for said limitations teaches a process for generating an initial flight plan which begins at a first destination D1, travels to three other destinations D2, D3, and D4, and returns to the first destination D1. The flight plan begins and ends at the same point, as it is a round trip, and because of this fact the start node is the same as the goal location and may be used interchangeably to satisfy the requirements for the limitations present for claims 6 and 16.
Applicant’s remaining arguments with respect to claims 1-4, 6-14, 16-20, 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-14, and 16-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-4, 6-14, and 16-20, are directed to a method and medium for planning the most cost-efficient route for a UAV. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 11 recites abstract limitations displayed in bold below:
A non-transitory computer-readable medium having computer-executable instructions stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for planning a navigation route for an autonomous vehicle, the actions comprising:
receiving, by the computing system, mission information including a start location and a goal location;
generating, by the computing system, a representation of an operation area that includes the start location and the goal location;
updating, by the computing system, the representation of the operation area based on one or more temporary obstacles;
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 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.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. For example, a human is able to mentally plan various routes for a UAV based on the various factors described (start and goal locations, temporary obstacles present in the operation area, and the cost-to-go to the goal through various routes) and can compare the cost for each route in order to decide which route would be the most cost effective. Once the aforementioned determinations are complete, the person may create a map showing the route based on the previously discussed factors. Thus, the claim recites an abstract idea.
Additionally, these limitations, as drafted, are a process that, under its broadest reasonable interpretation, additionally and/or alternatively represent mathematical relationships (i.e. configuring models and performing calculations) and are therefore mathematical concepts. The mere recitation of a generic computer or computing element does not take the claim out of the mathematical concepts grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One, the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
A non-transitory computer-readable medium having computer-executable instructions stored thereon that, in response to execution by one or more processors of a computing system, cause the computing system to perform actions for planning a navigation route for an autonomous vehicle, the actions comprising:
receiving, by the computing system, mission information including a start location and a goal location;
generating, by the computing system, a representation of an operation area that includes the start location and the goal location;
updating, by the computing system, the representation of the operation area based on one or more temporary obstacles;
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 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.
The functions of the non-transitory computer-readable medium having computer-executable instructions, the one or more processors of the computing system, and the machine-learning model are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components.
The characterization and functions of the computing system and the machine-learning models amount to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the additional elements of non-transitory computer-readable medium having computer-executable instructions, the one or more processors of the computing system, and the machine-learning model, amount to mere instructions to apply the exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As discussed above, the characterization and functions of the computing system and the machine-learning model amounts to merely indicating a field of use or technological environment in which to apply a judicial exception, which does not amount to significantly more than the exception itself (see MPEP 2106.05(h)).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
The various metrics/variables/limitations of claims 12-14 and 16-18 merely narrow the previously recited abstract idea limitations without recitation of any further additional elements. Therefore, for the reasons described above with respect to claim 11, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 19 recites transmitting a navigation route to the autonomous vehicle to be used for autonomous navigation. These limitations amount to the extra-solution activity of sending and receiving data. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claim 20 recites that the autonomous vehicle is an unmanned aerial vehicle. The characterization of the autonomous vehicle represents a technical environment of field of use, which cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself.
The limitations of claims 1 are comparable to the limitations of claim 11 and are therefore rejected under the same rationale.
The various metrics/variables/limitations of claims 2-4 and 6-9 merely narrow the previously recited abstract idea limitations without recitation of any further additional elements. Therefore, tor the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim 9 recites transmitting a navigation route to the autonomous vehicle to be used for autonomous navigation. These limitations amount to the extra-solution activity of sending and receiving data. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claim 10 recites that the autonomous vehicle is an unmanned aerial vehicle. The characterization of the autonomous vehicle represents a technical environment of field of use, which cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself.
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, 6-14, and 16-20 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 Jiang (US 20230192130).
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.
However in the same field of endeavor, Jiang 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; (Jiang: Paragraph [0041], [0044]) 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. (Jiang: Paragraph [0041], [0043], [0045])
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 White, for the benefit of generating a route for an autonomous vehicle more efficiently. (Jiang: Abstract and Paragraph [0001])
Regarding Claim 12,
Ravenscroft, in view of White, and further in view of Jiang, 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 Jiang, 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 Jiang, 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 Jiang, 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 Jiang, is the same as stated for Claim 11 above.
Regarding Claim 17,
Ravenscroft, in view of White, and further in view of Jiang, 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 Jiang, 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 19,
Ravenscroft, in view of White, and further in view of Jiang, teaches
The computer-readable medium of claim 11, wherein the actions further comprise transmitting the navigation route to the autonomous vehicle to be used for autonomous navigation from the start location to the goal location. (Ravenscroft: Paragraph [0025]; “Pre-flight planning”, [0031]-[0032])
Regarding Claim 20,
Ravenscroft, in view of White, and further in view of Jiang, 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 9, the claim is analogous to Claim 19 limitations and is therefore rejected under the same premise as Claim 19.
Regarding Claim 10, the claim is analogous to Claim 20 limitations and is therefore rejected under the same premise as Claim 20.
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at 571-272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/P.R.L./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663