DETAILED ACTION
This is a response to the Amendment to Application # 18/969,790 filed on May 26, 2026 in which claims 1, 3, 8, 12, 14, and 20 were amended.
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
Claims 1-20 are pending, of which claims 1, 2, 4-13, and 15-20 are rejected under 35 U.S.C. § 101; claims 1-8 and 20 are rejected under 35 U.S.C. § 102(a)(2); and claims 9-19 are rejected under 35 U.S.C. § 103.
Examiner's Note on the Completeness of the Reply
The Non-Final Office Action dated February 23, 2026 contained an objection to the specification. Applicant failed to address this objection in either the presently filed Remarks or by amending the specification. If Applicant fails to address this objection in the future, those actions shall be deemed non-compliant.
Specification
The use of the terms ArcGIS, FlightAware, and The Weather Company, which are trademarks used in commerce, have been noted in this application. The terms should be fully capitalized wherever they appear or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claims 1, 3, 8, 12, 14, and 20 objected to for the following reasons: The claim amendments fail to comply with 37 C.F.R. § § 1.52(a)(iv), which requires all papers submitted to the United States Patent and Trademark Office be submitted in “dark ink or its equivalent.” Instead, the markup appears to be in colored ink that is not fully legible. Although the claims have been accepted for entry on this occasions, future submissions that are not in the required dark ink shall be deemed non-compliant. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 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, 2, 4-13, and 15-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claims 1, 2, 4-13, and 15-20, these claims are directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims recite, when considered individually or as a whole, a method and a system for generating a map. Therefore, claims 1, 2, 4-13, and 15-20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1, 12, and 20 include limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the § 101 rejection. Representative claim 1 recites:
1. A computer-implemented method for generating spatially-indexed scores suitable for Unmanned Aerial Vehicle (UAV) applications, the method comprising:
at a server, receiving geospatial data associated with a selectable location;
defining a plurality of spatial indices, wherein each spatial index is associated with a respective geometric boundary based on the received geospatial data;
retrieving location-based data from a plurality of third-party databases remote from the server, wherein the location-based data includes disparate data types, and wherein the location-based data is associated with the location and includes one or more of aviation data, population data, land usage data, zoning data, facility data, infrastructure data, natural data, and building data associated with at least a portion of the location;
at the server, normalizing the retrieved location-based data into a common data type as a uniform representation;
at the server, processing the normalized location-based data to assign a score to each spatial index, wherein each score reflects a weighted risk factor associated with directing a UAV through that spatial index; and
at the server, generating surface data configured to be processed for UAV operations, wherein the surface data includes the scores associated with each spatial index.
Independent claims 12 and 20 additionally recite the presence of a user interface, while independent claim 20 further includes sending the data to a third party.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, the “defining,” “normalizing” and “processing” steps, in the context of these claims, encompass steps that can be performed in the mind. Further, the two “receiving” steps and the “generating” step, in the context of these claims, encompass steps that can be performed via pen and paper. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claims, as a whole, integrate the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
1. A computer-implemented method for generating spatially-indexed scores suitable for Unmanned Aerial Vehicle (UAV) applications, the method comprising:
at a server, receiving geospatial data associated with a selectable location;
defining a plurality of spatial indices, wherein each spatial index is associated with a respective geometric boundary based on the received geospatial data;
retrieving location-based data from a plurality of third-party databases remote from the server, wherein the location-based data includes disparate data types, and wherein the location-based data is associated with the location and includes one or more of aviation data, population data, land usage data, zoning data, facility data, infrastructure data, natural data, and building data associated with at least a portion of the location;
at the server, normalizing the retrieved location-based data into a common data type as a uniform representation;
at the server, processing the normalized location-based data to assign a score to each spatial index, wherein each score reflects a weighted risk factor associated with directing a UAV through that spatial index; and
at the server, generating surface data configured to be processed for UAV operations, wherein the surface data includes the scores associated with each spatial index.
For the following reasons, the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of the “server” and the “user interface” the examiner submits that these limitations are instructions to “apply it.” See MPEP § 2106.05(f).
Regarding the limitations of receiving the data “from a plurality of third-party databases remote from the server” and “sending the surface data to a third party to enable the third party to control a UAV based on the surface data,” these limitations are insignificant extra-solution activities. See MPEP § 2106.05(g). Further, the portion of the limitation “to enable the third party to control a UAV based on the surface data” of claim 20 appears to be a recitation of intended use and, therefore, receives no patentable weight, meaning it cannot satisfy Prong II of Step 2A or Step 2B.
In particular, the limitations are recited at a high level of generality (i.e. as a general means of generating a map).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. See MPEP § 2106.05.
Accordingly, the additional limitations does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the server and user interface amount to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
And as discussed above, the additional limitations of “retrieving … from a plurality of third party databases” and the “sending,” the examiner submits that these limitations are insignificant extra-solution activities that are well understood, routine, and conventional. See MPEP § 2106.05(d) and Lopez et al., US Patent 11,868,145
Hence, the claim is not patent eligible.
Dependent claims 2, 4-11, 13, and 15-19 do not recite any further limitations that cause the claims to be directed towards statutory subject matter. The claims merely recite: the abstract idea discussed above. Each of the further limitations expound upon the abstract idea and do not recite additional elements integrating the abstract idea into a practical application or additional elements that are not well-understood, routine or conventional. Therefore, dependent claims 2, 4-11, 13, and 15-19 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claims 1, 2, 4-13, and 15-20 are ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 U.S.C. § 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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-8 and 20 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Lopez et al., US Patent 11,868,145 (hereinafter Lopez).
Regarding claim 1, Lopez discloses a computer-implemented method for generating spatially-indexed scores suitable for Unmanned Aerial Vehicle (UAV) applications, the method comprising “at a server, receiving geospatial data associated with a selectable location” (Lopez col. 10, ll. 4-26, col. 15, ll. 4-18) where the one or more physical computer servers 282 receive geospatial data (Lopez col. 10, ll. 4-26) and those areas may be “selected as a function of operating characteristics of the aerial vehicle. (Lopez col. 15, ll. 4-18). Additionally, Lopez discloses “defining a plurality of spatial indices, wherein each spatial index is associated with a respective geometric boundary based on the received geospatial data” (Lopez, col. 14, l. 56-col. 15, l. 3) by applying a matrix of cells over the geographic map. The present specification gives an example of spatial indices including “grid cells.” (Spec. ¶ 12). Further, Lopez discloses “retrieving location-based data from a plurality of third-party databases remote from the server, wherein the location-based data includes disparate data types, and wherein the location-based data is associated with the location and includes one or more of aviation data, population data, land usage data, zoning data, facility data, infrastructure data, natural data, and building data associated with at least a portion of the location” (Lopez col. 1, ll. 56-67) where population data may be retrieved from “any public or private source” (i.e., a plurality of third-party databases). Lopez gives additional examples of the population coming from LandScan and WorldPop databases, which are known, third party remote databases. (Lopez col. 4, l. 63-col. 5, l. 28). Lopez continues by disclosing that other types of data may be used, such as “data relating to operations of the aerial vehicle 210” (i.e., aviation data, Lopez, col. 10, ll. 21-26), various land use and facility classifications (Lopez, col. 21, ll. 13-16), “critical infrastructure” data (i.e., infrastructure data, Lopez, col. 4, ll. 42-52), “ground conditions” (i.e., natural data, Lopez, col. 3, ll. 7-14), and “buildings” (i.e., building data, Lopez, col. 3, ll. 43-51). Thus, Lopez discloses at least six disparate data types.
Moreover, Lopez discloses “at the server, normalizing the retrieved location-based data into a common data type as a uniform representation” (Lopez col. 1, ll. 56-67) where the population density data and the critical infrastructure data (i.e., disparate types of location-based data) are normalized into a grid data type on the map. Likewise, Lopez discloses “at the server, processing the normalized location-based data to assign a score to each spatial index, wherein each score reflects a weighted risk factor associated with directing a UAV through that spatial index” (Lopez col. 15, l. 42-col. 16, l. 12) by assigning reliability scores, which reflect values such as safety (i.e., risk) of directing the UAV through that cell. These scores are weighted by the various intrinsic data applied to the values to calculate the scores. Finally, Lopez discloses “at the server, generating surface data configured to be processed for UAV operations, wherein the surface data includes the scores associated with each spatial index” (Lopez col. 16, ll. 49-65) where the mission data (i.e., surface data configured to be processed for UAV operations, and includes the reliability scores.
Regarding claim 2, Lopez discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Lopez discloses “generating a planned route for a UAV based on the surface data and the scores associated with each spatial index” (Lopez col. 16, ll. 49-65) where the mission data (i.e., surface data configured to be processed for UAV operations, and includes the reliability scores.
Regarding claim 3, Lopez discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, Lopez discloses “sending instructions to the UAV, wherein the instructions cause the UAV to travel along the planned route” (Lopez col. 11, ll. 37-41) where the route includes instructions to travel along the planned route.
Regarding claim 4, Lopez discloses the limitations contained in parent claim 2 for the reasons discussed above. In addition, Lopez discloses “classifying each spatial index according to its respective score, wherein the planned route is generated based on the classification of each spatial index” (Lopez col. 15, l. 42-col. 16, l. 12) where cells may be classified with labels such as “critical infrastructure” that affect the reliability of the score of the cell that is used to plan the route.
Regarding claim 5, Lopez discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Lopez discloses “filtering which of the location-based data is normalized and processed to assign the scores of the spatial indices” (Lopez, col. 16, ll. 21-28) where less land than what is covered in the geographic map is assigned reliability scores, meaning the unused land was filtered out within the broadest reasonable interpretation of the term.
Regarding claim 6, Lopez discloses the limitations contained in parent claim 5 for the reasons discussed above. In addition, Lopez discloses “adjusting the scores based on which of the location-based data is selected via the filtering, and adjusting a planned route of a UAV based on the adjusted scores” (Lopez col. 4, ll. 4-16) where the selected paths may change (i.e., be adjusted) based on changes to the data over time.
Regarding claim 7, Lopez discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Lopez discloses “dynamically adjusting a resolution of the spatial indices; and assigning the scores based on the adjusted resolution of the spatial indices” (Lopez col. 15,ll. 4-18, col. 15, l. 42-col. 16, l. 12) where the size of the cells may be based on data such as temperature (Lopez, col. 15, ll. 4-18), which is subject to change, that may be generated “dynamically” during flight of the vehicle. (Lopez, col. 15, l. 42-col. 16, l. 12).
Regarding claim 8, Lopez discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Lopez discloses “wherein the score is on a scale in which a first score is associated with a higher risk of travel for the UAV, and a second score is associated with a lower risk of travel for the UAV” (Lopez col. 21, ll. 17-52 and Fig. 6B) by giving an example of “elevated” scores being correlated to higher risk.
Regarding claim 20, Lopez discloses a computer-implemented method for controlling Unmanned Aerial Vehicles (UAVs), the method comprising “receiving geospatial data associated with a selectable location” (Lopez col. 10, ll. 4-26, col. 15, ll. 4-18) where the one or more physical computer servers 282 receive geospatial data (Lopez col. 10, ll. 4-26) and those areas may be “selected as a function of operating characteristics of the aerial vehicle. (Lopez col. 15, ll. 4-18). Additionally, Lopez discloses “defining a plurality of spatial indices, wherein each spatial index is associated with a respective geometric boundary based on the received geospatial data” (Lopez, col. 14, l. 56-col. 15, l. 3) by applying a matrix of cells over the geographic map. The present specification gives an example of spatial indices including “grid cells.” (Spec. ¶ 12). Further, Lopez discloses “retrieving location-based data from a plurality of third-party databases remote from the server, wherein the location-based data includes disparate data types, and wherein the location-based data is associated with the location and includes three or more of aviation data, population data, land usage data, zoning data, facility data, infrastructure data, natural data, and building data associated with at least a portion of the location” (Lopez col. 1, ll. 56-67) where population data may be retrieved from “any public or private source” (i.e., a plurality of third-party databases). Lopez gives additional examples of the population coming from LandScan and WorldPop databases, which are known, remote third-party databases. (Lopez col. 4, l. 63-col. 5, l. 28). Lopez continues by disclosing that other types of data may be used, such as “data relating to operations of the aerial vehicle 210” (i.e., aviation data, Lopez, col. 10, ll. 21-26), various land use and facility classifications (Lopez, col. 21, ll. 13-16), “critical infrastructure” data (i.e., infrastructure data, Lopez, col. 4, ll. 42-52), “ground conditions” (i.e., natural data, Lopez, col. 3, ll. 7-14), and “buildings” (i.e., building data, Lopez, col. 3, ll. 43-51). Thus, Lopez discloses at least six disparate data types.
Moreover, Lopez discloses “normalizing the retrieved location-based data as a uniform representation” (Lopez col. 1, ll. 56-67) where the population density data and the critical infrastructure data (i.e., disparate types of location-based data) are normalized into a grid data type on the map. Likewise, Lopez discloses “assigning a score to each spatial index based on the normalized location-based data, wherein each score reflects a weighted risk factor associated with directing a UAV through that spatial index” (Lopez col. 15, l. 42-col. 16, l. 12) by assigning reliability scores, which reflect values such as safety (i.e., risk) of directing the UAV through that cell. These scores are weighted by the various intrinsic data applied to the values to calculate the scores. Lopez also discloses “generating surface data configured to be processed for UAV operations, wherein the surface data includes the scores associated with each spatial index” (Lopez col. 16, ll. 49-65) where the mission data (i.e., surface data configured to be processed for UAV operations, and includes the reliability scores. Finally, Lopez discloses “sending the surface data to a third party to enable the third party to control a UAV based on the surface data” (Lopez col. 5, l. 57-col. 6, l. 6) where any party over a network may be enabled to control the UAV based on the data.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
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. Applicants are advised of the obligation under 37 C.F.R. § 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 9-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Lopez in view of Pestun et al., US Publication 2018/0260626 (hereinafter Pestun).
Regarding claim 9, Lopez discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Lopez discloses “generating … a planned route for the UAV based on the surface data and the scores associated with each spatial index” (Lopez Fig. 1F) by generating a map of the planned route of the UAV based on the surface data and reliability scores.
Lopez does not appear to explicitly disclose that the generated map is “for display on a user interface.”
However, Pestun discloses a method for controlling a drone including the step of “generating, for display on a user interface, a planned route for the UAV based on the surface data and the scores associated with each spatial index” (Pestun ¶¶ 334-335 and Fig. 31) where client user interface 140 displays the UAV’s flight path based on infrastructure objects and risks associated with the areas of the map.
Lopez and Pestun are analogous art because they are from the “same field of endeavor,” namely that of UAV mapping methods.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Lopez and Pestun before him or her to modify the map of Lopez to include the user interface that displays a map of Pestun.
The motivation for doing so would have been that a person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that display of such a map would allow a human user to verify the flight data to reduce the likelihood of computer error.
Regarding claim 10, the combination of Lopez and Pestun discloses the limitations contained in parent claim 9 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “generating, on a user interface, a contiguous surface map associated with the scores of each spatial index, wherein the surface map includes a visual representation of the score of each spatial index” (Pestun ¶ 323) by displaying the map with a risk mask that sets the value of each pixel based on the risk of the area covered.
Regarding claim 11, the combination of Lopez and Pestun discloses the limitations contained in parent claim 10 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “generating a map for display on the user interface based on the received map data; and overlaying the surface map and the planned route onto the generated map for display on the user interface” (Pestun ¶¶ 331, 334-335) by generating the map (Pestun ¶¶ 334-335) and indicating that the data may be displayed over the map background. (Pestun ¶ 331).
Regarding claim 12, Lopez discloses a system for generating spatially-indexed scores suitable for Unmanned Aerial Vehicle (UAV) applications, the system comprising “a local server … configured to communicate with third-party servers via a communication network, wherein the third-party servers are remote from the local server and store location-based data” (Lopez col. 1, ll. 56-67) where population data may be retrieved from “any public or private source” (i.e., a plurality of third-party databases). Lopez gives additional examples of the population coming from LandScan and WorldPop databases, which are known, remote third-party databases. (Lopez col. 4, l. 63-col. 5, l. 28). Lopez continues by disclosing that other types of data may be used, such as “data relating to operations of the aerial vehicle 210” (i.e., aviation data, Lopez, col. 10, ll. 21-26), various land use and facility classifications (Lopez, col. 21, ll. 13-16), “critical infrastructure” data (i.e., infrastructure data, Lopez, col. 4, ll. 42-52), “ground conditions” (i.e., natural data, Lopez, col. 3, ll. 7-14), and “buildings” (i.e., building data, Lopez, col. 3, ll. 43-51). Additionally, Lopez discloses “wherein the local server includes a processor and memory storing instructions.” (Lopez col. 10, ll. 4-26). Further, Lopez discloses “that cause the processor to: receive a selection of a location …; obtain geospatial data associated with the location” (Lopez col. 10, ll. 4-26, col. 15, ll. 4-18) where the one or more physical computer servers 282 receive geospatial data (Lopez col. 10, ll. 4-26) and those areas may be “selected as a function of operating characteristics of the aerial vehicle. (Lopez col. 15, ll. 4-18). Further, Lopez discloses “define a plurality of spatial indices, wherein each spatial index is associated with a respective geometric boundary based on the received geospatial data” (Lopez, col. 14, l. 56-col. 15, l. 3) by applying a matrix of cells over the geographic map. The present specification gives an example of spatial indices including “grid cells.” (Spec. ¶ 12).
Moreover, Lopez discloses “retrieve the location-based data from the third-party servers, wherein the location-based data includes disparate data types, wherein the location-based data is associated with the location and includes two or more of aviation data, population data, land usage data, zoning data, facility data, infrastructure data, natural data, and building data associated with at least a portion of the location” (Lopez col. 1, ll. 56-67) where population data may be retrieved from “any public or private source” (i.e., a plurality of third-party databases). Lopez gives additional examples of the population coming from LandScan and WorldPop databases. (Lopez col. 4, l. 63-col. 5, l. 28). Lopez continues by disclosing that other types of data may be used, such as “data relating to operations of the aerial vehicle 210” (i.e., aviation data, Lopez, col. 10, ll. 21-26), various land use and facility classifications (Lopez, col. 21, ll. 13-16), “critical infrastructure” data (i.e., infrastructure data, Lopez, col. 4, ll. 42-52), “ground conditions” (i.e., natural data, Lopez, col. 3, ll. 7-14), and “buildings” (i.e., building data, Lopez, col. 3, ll. 43-51). Thus, Lopez discloses at least six disparate data types. Likewise, Lopez discloses “normalize the retrieved location-based data into a common data type as a uniform representation” (Lopez col. 1, ll. 56-67) where the population density data and the critical infrastructure data (i.e., disparate types of location-based data) are normalized into a grid data type on the map. Lopez also discloses “assign a score to each spatial index based on the normalized location-based data, wherein each score reflects a weighted risk factor associated with directing a UAV through that spatial index based on the normalized location-based data” (Lopez col. 15, l. 42-col. 16, l. 12) by assigning reliability scores, which reflect values such as safety (i.e., risk) of directing the UAV through that cell. These scores are weighted by the various intrinsic data applied to the values to calculate the scores. Finally, Lopez discloses “generate surface data configured to be processed for UAV operations, wherein the surface data includes the scores associated with each spatial index” (Lopez col. 16, ll. 49-65) where the mission data (i.e., surface data configured to be processed for UAV operations, and includes the reliability scores.
Lopez does not appear to explicitly disclose “a user interface” and, therefore, also does not appear to explicitly disclose “a local server commutatively connected to the user interface and configured to communicate with third-party servers via a communication network, wherein the third-party servers are remote from the local server and store location-based data” or “that cause the processor to: receive a selection of a location from a user via the user interface; obtain geospatial data associated with the location.”
However, Pestun discloses a method for controlling a drone including “a user interface.” (Pestun ¶ 60). Additionally, Pestun discloses “a local server commutatively connected to the user interface” (Pestun ¶ 362 and Fig. 34) where the computer system 3400 may be a server and is shown to be connected to I/O device 3410, which may be a display including the user interface. Finally, Pestun discloses “that cause the processor to: receive a selection of a location from a user via the user interface; obtain geospatial data associated with the location” (Pestun ¶ 335) where the user selects a location on the interface to obtain more images of the area.
Further, a person of ordinary skill in the art prior to the effective filing date would have recognized that when Pestun was combined with Lopez, the local server configured to communicate with third-party servers of Lopez would further be communicatively connected to the interface of Pestun. Therefore the combination of Lopez and Pestun at least teaches and/or suggests the claimed limitation “a local server commutatively connected to the user interface and configured to communicate with third-party servers via a communication network, wherein the third-party servers are remote from the local server and store location-based data,” rendering it obvious.
Lopez and Pestun are analogous art because they are from the “same field of endeavor,” namely that of UAV mapping methods.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Lopez and Pestun before him or her to modify the map of Lopez to include the user interface that displays a map of Pestun.
The motivation for doing so would have been that a person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that display of such a map would allow a human user to verify the flight data to reduce the likelihood of computer error.
Regarding claim 13, the combination of Lopez and Pestun discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “generate a planned route for a UAV based on the surface data and the scores associated with each spatial index” (Lopez col. 16, ll. 49-65) where the mission data (i.e., surface data configured to be processed for UAV operations, and includes the reliability scores.
Regarding claim 14, the combination of Lopez and Pestun discloses the limitations contained in parent claim 13 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “send instructions to the UAV to cause the UAV to travel along the planned route” (Lopez col. 11, ll. 37-41) where the route includes instructions to travel along the planned route.
Regarding claim 15, the combination of Lopez and Pestun discloses the limitations contained in parent claim 13 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “classify each spatial index according to its respective score, wherein the planned route is generated based on the classification of each spatial index” (Lopez col. 15, l. 42-col. 16, l. 12) where cells may be classified with labels such as “critical infrastructure” that affect the reliability of the score of the cell that is used to plan the route.
Regarding claim 16, the combination of Lopez and Pestun discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “receive filtering instructions from the user interface” (Pestun ¶ 335) where the user’s selection of the particular location is a form of filtering within the plain and ordinary meaning of the term. Further, the combination of Lopez and Pestun discloses “based on the filtering instructions, filter which of the location-based data is normalized and processed to assign the scores of the spatial indices” (Lopez, col. 16, ll. 21-28) where less land than what is covered in the geographic map is assigned reliability scores, meaning the unused land was filtered out within the broadest reasonable interpretation of the term.
Regarding claim 17, the combination of Lopez and Pestun discloses the limitations contained in parent claim 16 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “adjust the scores based on which of the location-based data is selected via the filtering, and adjust a planned route of the UAV based on the adjusted scores” (Lopez col. 4, ll. 4-16) where the selected paths may change (i.e., be adjusted) based on changes to the data over time.
Regarding claim 18, the combination of Lopez and Pestun discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “receive a request from the user interface to adjust a resolution of the spatial indices” (Pestun ¶ 107 and Fig. 7) where a request to set the spatial resolution may flow to the entity recognizer (Pestun ¶ 107), which Fig. 7 shows may be based on input from the client systems 134 and its associated user interface. Further, the combination of Lopez and Pestun discloses “dynamically adjust the resolution in response to the request; and assign the scores based on the adjusted resolution” (Lopez col. 15,ll. 4-18, col. 15, l. 42-col. 16, l. 12) where the size of the cells may be based on data such as temperature (Lopez, col. 15, ll. 4-18), which is subject to change, that may be generated “dynamically” during flight of the vehicle. (Lopez, col. 15, l. 42-col. 16, l. 12).
Regarding claim 19, the combination of Lopez and Pestun discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Lopez and Pestun discloses “wherein each spatial index is hexagonal in shape.” (Lopez col. 3, ll. 7-25).
Response to Arguments
Applicant’s arguments filed May 26, 2026, with respect to the rejection of claims 3, 8, and 14 under 35 U.S.C. § 112(b) (Remarks 8) have been fully considered and are persuasive. The rejection of claims 3, 8, and 14 under 35 U.S.C. § 112(b) have been withdrawn.
Applicant's remaining arguments filed May 26, 2026 have been fully considered but they are not persuasive.
Regarding the rejection of claims 1, 2, 4-13, and 15-20 under 35 U.S.C. § 101, Applicant first argues that several elements require computer components and thus, “does not account for the scale and technical context of the claimed process.” (Remarks 8-9). The examiner disagrees.
As indicated in the rejection, these elements were addressed under Step 2B of the Alice/Mayo framework. Therefore, Applicant’s argument is unpersuasive.
Applicant next argues that under Step 2A, prong one, the present invention cannot be performed in the mind because “the present claims require machine-performed collection and analysis of large, distributed datasets that a human mind is not equipped to perform as claimed. (Remarks 9-10, emphasis added). The examiner disagrees.
In Berkheimer v. HP INC., 881 F. 3d 1360 (Fed. Cir. 2018), the federal circuit held that improvements are only considered “to the extent they are captured in the claims.” Berkheimer at 1369. Here, the claims do not, in fact require a “large, distributed dataset,” as argued. Instead, the claims only require two (or three, in the case of claim 20) pieces of data. This amount of data can be easily handled in the human mind. Therefore, Applicant’s argument is not persuasive.
Applicant next argues that under Step 2A, prong two, the claimed invention amounts to something more because “[t]he claimed invention addresses that problem [of not managing risks] by generating spatially indexed risk scores and surface data that are specifically configured for UAV operations and route planning.” (Remarks 10-11). The examiner disagrees.
The arguments of counsel cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965); In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997). Here, Applicant presents no evidence as to how these steps overcome the cited problem. Instead, Applicant merely points to claimed functionality and declares it to solve a problem. Therefore, Applicant’s argument is unpersuasive.
Finally, Applicant argues that under Step 2B, “[t]he ordered combination recited in the claims is not a generic instruction to apply an abstract idea on a computer” but instead “require[s] a particular sequence of operations in a UAV-navigation environment.” (Remarks 11-12).
First, Applicant has again failed to present any evidence and, instead, merely provides arguments. Additionally, Applicant’s claims are recited at a high level of generality and do not incorporate the argued “concrete technological arrangement,” as argued. Therefore, Applicant’s argument is unpersuasive.
Regarding the rejection of claim 1 under 35 U.S.C. § 102, Applicant argues that Lopez fails to disclose the newly amended limitation and although “Lopez describes more than one kind of information,” but “does not disclose normalizing any such disparate data into a common data type as a uniform representation, and then assigning a score based on the normalized representation” and that the rejection “does no account for this concrete technological arrangement and its role in producing an operation UAV routing dataset.” (Remarks 12-13). The examiner disagrees.
As an initial matter, although the claim requires that the location-based data include disparate data types, it does not require “normalizing any such disparate data into a common data type as a uniform representation, and then assigning a score based on the normalized representation.” Instead, it merely requires that some of the location-based data is normalized and assigned a score.
Further, as discussed above, Lopez does disclose these features. Therefore, Applicant’s argument is unpersuasive.
Regarding the rejection of claims 2-20 under 35 U.S.C. §§ 102 and 103, respectively, Applicant argues that these claims are allowable for the same reasons as claim 1 or for depending on such a claim. (Remarks 14). Applicant’s arguments are unpersuasive for the reasons discussed above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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 ANDREW R DYER whose telephone number is (571)270-3790. The examiner can normally be reached Monday-Thursday 7:30-4:30.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662
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