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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/29/2026 has been entered.
Status of Claims
This non-final rejection is in response to Applicant’s amended filing of 07/29/2026.
Claims 1-9 and 13-23 are currently pending and have been examined. Applicant has amended claims 1, 13, and 20; and added new claims 21-23.
Response to Arguments
Applicant's arguments with respect to claims 1-9 and 13-20 rejected under 35 USC § 103 have been fully considered but they are not persuasive.
The Applicant argues that Neubauer does not disclose the amended limitations of “a resource expenditure parameter associated with data transmission by the vehicle” and fails to disclose, teach, or suggest “additional entity parameter data.” The Examiner respectfully disagrees. Regarding “additional entity parameter data” in accordance with Applicant’s specification, Neubauer explicitly discloses current network-specific coverage data includes antenna type, antenna gain, energy per resource element (EPRE) per antenna port, etc. (see at least ¶ [0132-0137]) that reads directly on the limitation. Furthermore, energy per resource element (EPRE) per antenna port, in conjunction with ¶ [0129] reciting “The provision of the change probability … of a currently connected network node may facilitate optimization of flight paths by UAV service providers in view of a stable connection and or in view of saving energy and processing power otherwise needed for monitoring the signal strengths of network nodes or switching operations” at least suggests the “resource expenditure parameter associated with data transmission by the vehicle” as claimed in the amended limitation.
Additionally, the Applicant argues that there is no basis to combine Murphy and Neubauer as Murphy is directed toward rerouting flight paths according to observed coverage gaps while Neubauer is directed toward a multi-operator network handover that does not “[correspond] to a second signal type that is different from the at least one signal type”. The Examiner respectfully disagrees. Neubauer ¶ [0063] makes more than a passing reference satellite radio systems and states “The plurality of communication networks may respectively be wireless communication networks or radio communication networks like cellular networks such as UMTS, LTE or New Radio, 4G, 5G, WiMAX or any other network.” Each of these networks provide their own signals for connectivity and are presented as part of the plurality available to perform handovers within. The ability to shift between available plurality networks is advantageous to the flight operations of Murphy because it allows an area with a coverage gap over one network to be accessible over another available network, thereby allowing Murphy to maintain a predetermined flight path before committing to rerouting the path. Therefore Murphy and Neubauer are reasonable to combine because both inventions are directed toward determining signal strength of areas that provide connectivity to UAVs that would facilitate the UAV operating in the coverage area by ensuring the strongest connectivity throughout its flight.
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.
Claims 1-8 and 13-23 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy (US 20180293897 A1; reference provided in European search opinion filed 07/20/2025) in view of Neubauer et al. (US 20200394929 A1; reference provided in European search opinion filed 07/20/2025).
Regarding claims 1, 13, and 20, Murphy discloses an apparatus comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to perform operations (claim 13; see abstract and ¶ [0020-0022]) and computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon (claim 20; see abstract and ¶ [0020-0022]) that, in execution with at least one processor, configures a computer-implemented method (claim 1; see abstract and ¶ [0020-0022]) comprising:
receiving entity parameter data corresponding to at least one signal handling entity associated with an environment, wherein the at least one signal handling entity supports communication of at least one signal type (see at least ¶ [0009] and [0017] disclosing using base station configuration data to develop a coverage model, the data including the band and power output of network radio frequency signals);
receiving dynamic environment data associated with an impact of a signal strength of the signal type in the environment (see at least ¶ [0009] and [0017] disclosing using environmental data to develop a coverage model, the data including geography and structures);
generating a model of signal strength in an environment for the at least one signal type (see at least ¶ [0015-0019] and [0026-0027] disclosing a coverage forecast engine and modeling module for producing a coverage model) by at least:
modeling a signal propagation for each signal handling entity of the at least one signal handling entity based at least in part on the entity parameter data and the dynamic environment data (see at least ¶ [0015-0019] and [0026-0027] disclosing a coverage forecast engine utilizing base station configuration data and environmental data to develop the coverage model);
determining the signal strength for the at least one signal type in the environment based at least in part on a combination of the signal propagations for the at least one signal handling entity (see at least ¶ [0018-0019], [0026-0027], and [0030-0031] disclosing the coverage forecast engine measuring network signal robustness values in developing a coverage model of a network);
identifying a set of coverage areas within the environment that are associated with different signal strengths based at least in part on the model (see at least ¶ [0015-0019] and [0026-0027] disclosing a coverage forecast engine utilizing base station configuration data and environmental data to develop the coverage model);
outputting a top-down coverage map of the set of coverage areas at least within proximity of a vehicle (see at least ¶ [0040-0042] and Fig. 3 depicting a UAV traveling through a network area where it may have to modify its travel path to avoid a coverage gap, represented in an overhead view);
outputting a vertical profile coverage map of the set of coverage areas at least within the proximity of the vehicle (see at least ¶ [0040-0042] and Fig. 3 depicting a UAV traveling through a network area where it may have to modify its travel path to avoid a coverage gap, represented in a profile view of vertical coverage layers);
and determining that a portion of a travel path of the vehicle is within a dead zone in the set of coverage areas (see at least ¶ [0040-0042] and Fig. 3 depicting a UAV traveling through a network area where it may have to modify its travel path to avoid a coverage gap, represented in an overhead and/or a profile view of vertical coverage layers).
Murphy does not explicitly disclose determining an optimal network corresponding to the dead zone based on additional entity parameter data and a resource expenditure parameter associated with data transmission by the vehicle, wherein the optimal network corresponds to a second signal type that is different than the at least one signal type;
and causing the vehicle to connect to the optimal network when the vehicle is within the dead zone.
However, Neubauer suggests determining an optimal network corresponding to the dead zone based on additional entity parameter data (see at least ¶ [0132-0137] disclosing network-specific coverage data includes antenna type, antenna gain, energy per resource element (EPRE) per antenna port) and a resource expenditure parameter associated with data transmission by the vehicle (see at least ¶ [0129] and [0136-0137] disclosing handover operations and coverage data providing for energy resources available to the UAV and energy per resource element (EPRE) per antenna port), wherein the optimal network corresponds to a second signal type that is different than the at least one signal type (see at least ¶ [0053-0058], [0063], and [0113-0114] disclosing monitoring network coverage areas for the strongest connection signals to a UAV to optimize the flight path for coverage, wherein network handover includes wireless, radio, cellular, and satellite networks);
and causing the vehicle to connect to the optimal network when the vehicle is within the dead zone (see at least ¶ [0053-0058], [0063], and [0113-0114] disclosing monitoring network coverage areas for the strongest connection signals to a UAV to optimize the flight path for coverage).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the network and flight path optimization of Neubauer into the coverage modeling of Murphy with a reasonable expectation of success because both inventions are directed toward determining signal strength of areas that provide connectivity to UAVs. This would facilitate the UAV operating in the coverage area by ensuring the strongest connectivity throughout its flight.
Regarding claims 2 and 14, Murphy discloses the model of signal strengths comprises a modeled connectivity signal strength and a modeled position accuracy signal strength (see at least ¶ [0036-0038] disclosing the coverage model includes a query module and path analysis module that associates signal strength to coordinates in the coverage area and directing UAV flight paths away from coverage gaps with low robustness).
Regarding claims 3 and 15, Murphy discloses receiving an updated proposed travel plan that navigates throughout the environment (see at least ¶ [0038-0040] disclosing modifying UAV flight paths to avoid areas containing signal robustness gaps according to the coverage model);
and updating at least one user interface comprising the top-down coverage map and the vertical profile coverage map to depict at least one updated signal strength associated with the vehicle along the updated proposed travel plan (see at least ¶ [0038-0040] and Fig. 3 disclosing modifying UAV flight paths to avoid areas containing signal robustness gaps according to the coverage model).
Regarding claims 4 and 16, Murphy discloses receiving at least one signal interface parameter indicating a particular signal type, a provider identifier, or another parameter associated with the signal strengths associated with the set of coverage areas (see at least ¶ [0019] disclosing updating and associating signal robustness with 3-dimensional sections of airspace that the UAV travels through along their flight path);
determining an updated set of coverage areas based at least in part on the at least one signal interface parameter (see at least ¶ [0056-0058] and Fig. 6 disclosing updating the coverage model based on new signal robustness values detected along the flight path of the UAV);
and outputting a coverage map to a display, wherein the coverage map depicts the updated set of coverage areas (see at least ¶ [0001], [0021], [0023], [0043], and [0056-0058] disclosing the computing devices include UAV control devices and coverage model data can provide updated coverage models on computing device displays).
Regarding claims 5 and 17, Murphy discloses the model of a first signal type is based at least in part on a first set of type-specific modeling parameters and the model of a second signal type is based at least in part on a second set of type-specific modeling parameters (see at least ¶ [0026-0028] disclosing the coverage area modeling signal robustness in correlation to other modeling factors, including the coordinates of the measurement, RF band, and date and time the measurement takes place).
Regarding claims 6 and 18, Murphy discloses generating a travel plan based at least in part on the set of coverage areas (see at least ¶ [0038-0040] disclosing modifying UAV flight paths to avoid areas containing signal robustness gaps according to the coverage model).
Regarding claims 7 and 19, Murphy discloses the top-down coverage map and the vertical profile coverage map are outputted to a display of an external vehicle control system associated with the vehicle (see at least ¶ [0001], [0021], [0023], and [0043] disclosing the computing devices include UAV control devices and coverage model data can be provided on computing device displays).
Regarding claim 8, Murphy discloses the entity parameter data comprises one or more from a set of entity location data, entity coverage data, entity system data, environment map data, environment structure data, and obstacle data (see at least ¶ [0009] and [0017] disclosing using base station configuration data to develop a coverage model, the data including site location, antenna height, antenna type, antenna orientation, antenna down tilt angle, radio frequency (RF) band, RF power output, and more).
Regarding claim 21, the combination of Murphy and Neubauer does not explicitly disclose the model of signal strength is further generated for the second signal type, and wherein the optimal network is determined based at least in part on coverage areas associated with the second signal type within the model.
However, the means Murphy recites for generating a model of signal strength in an environment for the at least one signal type (see at least ¶ [0015-0019] and [0026-0027] disclosing a coverage forecast engine utilizing base station configuration data and environmental data to develop the coverage model) can be directly implemented to a different signal type according to corresponding base station and/or antenna configuration data that produces a different signal. Therefore one of ordinary skill in the art would recognize the means that Murphy uses to generate a model of signal strength for the at least one signal type would essentially be repeated to generate model of signal strength for second signal type different from the at least one signal type, as merely repeating the model generation process with a different signal would take only routine skill in the art.
Regarding claim 22, Murphy does not explicitly disclose the resource expenditure parameter comprises at least one of an energy consumption value or a computing power value associated with data transmission via the optimal network.
However, Neubauer suggests the resource expenditure parameter comprises at least one of an energy consumption value or a computing power value associated with data transmission via the optimal network (see at least ¶ [0129] and [0136-0137] disclosing handover operations and coverage data providing for energy resources available to the UAV and energy per resource element (EPRE) per antenna port).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the network and flight path optimization of Neubauer into the coverage modeling of Murphy with a reasonable expectation of success because both inventions are directed toward determining signal strength of areas that provide connectivity to UAVs. This would facilitate the UAV operating in the coverage area by ensuring the strongest connectivity throughout its flight.
Regarding claim 23, Murphy discloses the resource expenditure parameter comprises at least one of a maximum bandwidth or a transmission speed associated with data transmission via the optimal network (see at least ¶ [0017] disclosing generating the coverage model using network configuration data in consideration of a data transmission rate).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Neubauer, as applied to claim 1 above, and in further view of Malviya et al. (US 20180158343 A1).
Regarding claim 9, the combination of Murphy and Neubauer does not explicitly disclose the top-down coverage map and the vertical profile coverage map are outputted to a display onboard the vehicle.
However, Malviya suggests disclose the top-down coverage map and the vertical profile coverage map are outputted to a display onboard the vehicle (see at least abstract and ¶ [0019-0020] disclosing coverage map information being displaying in the aircraft).
While Malviya is directed toward an aircraft instead of a UAV, it is directed toward operating the aircraft routed through a coverage map. Therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the in-vehicle display of Malviya into the combination of Murphy and Neubauer with a reasonable expectation of success because all inventions are directed toward determining signal strength of areas that provide connectivity to flying vehicles. One of ordinary skill would recognize that selecting the place the coverage model is displayed only involves routine skill.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED C BEAN whose telephone number is (571)272-5255. The examiner can normally be reached 7:30AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached at (571) 272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.C.B./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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