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 first non-final rejection is in response to Applicant’s original filing of 06/04/2025.
Claims 1-20 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-12, and 14-17 of U.S. Patent No. 12333756 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because both inventions are directed toward controlling a plurality of drones through generating and updating flight plans for surveying a location based on a 3D model, the model updated based on received survey data. Dependent claims of each invention further limit the independent claims by defining the survey data collected and how it affects updates to the 3D model and flight plans (see table below).
19/227,872
US 12333756 B2
1. A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system:generating flight plans for a plurality of drones to survey the location based on a 3D model, wherein the flight plans are generated based on a desired level of image resolution of one or more objects in the location;controlling the plurality of drones to survey the location based on the flight plans;receiving survey data from the plurality of drones;updating the 3D model based on the survey data received from the plurality of drones;updating the flight plans based on the updated 3D model; and controlling the plurality of drones to survey the location based on the updated flight plans.6. …the flight plans are generated using a machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
1. A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system:automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the preliminary flight plans based on the updated 3D model, wherein the preliminary flight plans are generated using a single machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
2. …the survey data comprises images of the location.
2. …the survey data comprises images of the location.10. …the survey data comprises images of the location.
3. …the images capture survey markers that comprise encoded location information.
6. …the survey data comprises images capturing survey markers that comprises encoded location information and the 3D model is updated based on the images and associated location information.14. …the survey data comprises images capturing survey markers that comprises encoded location information and the 3D model is updated based on the images and associated location information.
4. …the survey data comprises positional data for the drones.
3. …the survey data comprises positional data for the drones.
11. …the survey data comprises positional data for the drones.
5. …the flight plans comprise a set of spline a set of waypoints, or a combination thereof.
4. …the preliminary flight plans comprise a set of splines or a set of waypoints.
12. …the preliminary flight plans comprise a set of splines or a set of waypoints.
7. …receiving an indication from at least one drone of a detection of at least one person;
and updating a flight plan of the at least one drone to avoid flying over the at least one person.
7. …receiving an indication from at least one drone of a detection of at least one person, and updating a preliminary flight plan of the at least one drone to avoid flying over the at least one person.
15. …receiving an indication from at least one drone of a detection of at least one person, and updating a preliminary flight plan of the at least one drone to avoid flying over the at least one person.
8. …predicting illumination of at least one object based on the 3D model;
and updating the flight plans for the plurality of drones surveying the at least one object based on the predicted illumination.
8. …automatically predicting illumination of at least one object based on the 3D model and automatically updating the preliminary flight plans for the plurality of drones surveying the at least one object based on the predicted illumination.
16. …automatically predicting illumination of at least one object based on the 3D model and automatically updating the preliminary flight plans for the plurality of drones surveying the at least one object based on the predicted illumination.
19. A system comprising a base station communicatively coupled to a plurality of drones, the base station comprising one or more processors, memory, and one or more programs stored in the memory for execution by the one or more processors for:generating flight plans for a plurality of drones to survey the location based on a 3D model, wherein the flight plans are generated based on a desired level of image resolution of one or more objects in the location;controlling the plurality of drones to survey the location based on the flight plans;receiving survey data from the plurality of drones;updating the 3D model based on the survey data received from the plurality of drones;updating the flight plans based on the updated 3D model;and controlling the plurality of drones to survey the location based on the updated flight plans.
6. …the flight plans are generated using a machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
9. A system comprising a base station communicatively coupled to a plurality of drones, the base station comprising one or more processors, memory, and one or more programs stored in the memory for execution by the one or more processors for:automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the preliminary flight plans based on the updated 3D model, wherein the preliminary flight plans are generated using a single machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
20. A non-transitory compute readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to:generate flight plans for a plurality of drones to survey the location based on a 3D model, wherein the flight plans are generated based on a desired level of image resolution of one or more objects in the location;control the plurality of drones to survey the location based on the flight plans;receive survey data from the plurality of drones;update the 3D model based on the survey data received from the plurality of drones;update the flight plans based on the updated 3D model;and control the plurality of drones to survey the location based on the updated flight plans.
6. …the flight plans are generated using a machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
17. A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to: automatically generate preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receive survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; update the 3D model based on the survey data received from the plurality of drones; and automatically update at least a portion of the preliminary flight plans based on the updated 3D model, wherein the preliminary flight plans are generated using a single machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
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 1-5, 9-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. (US 20180321692 A1) in view of Ljubuncic et al. (US 20170247108 A1) and Henry et al. (US 20210263515 A1).
Regarding claims 1, 19, and 20, Castillo-Effen discloses a system comprising a base station communicatively coupled to a plurality of drones (claim 19; see at least ¶ [0028] and [0041] describing base station data management and multiple-drone operation), the base station comprising a non-transitory compute readable medium comprising instructions (claim 20; see at least ¶ [0056]) that, when executed by one or more processors of a computing system, cause the computing system to perform a method for controlling a plurality of drones to survey a location (claim 1; see at least abstract and ¶ [0041]), the method comprising, at the computing system:
controlling the plurality of drones to survey the location based on the flight plans (see at least ¶ [0028], [0038], and [0041] describing base station data management and multiple-drone operation);
and receiving survey data from the plurality of drones (see at least ¶ [0028] disclosing the base station receiving data from the inspection robot).
While Castillo-Effen discloses using information for updating, maintaining, and generating new inspection plans for a given asset (see at least ¶ [0032]), it does not explicitly disclose updating the 3D model based on the survey data received from the plurality of drones;
updating the flight plans based on the updated 3D model;
and controlling the plurality of drones to survey the location based on the updated flight plans.
However, Ljubuncic suggests updating the 3D model based on the survey data received from the plurality of drones (see at least ¶ [0111-0113] describing examples where the unmanned aerial vehicle (UAV) generates the 3D model and updates it as more mapping information is collected);
updating the flight plans based on the updated 3D model (see at least ¶ [0111-0113] describing examples where the UAV generates and updates flight paths based on the updated 3D model);
and controlling the plurality of drones to survey the location based on the updated flight plans (see at least ¶ [0072-0073], [0111-0113], and [0120] describing examples where the UAV generates and updates flight paths based on the updated 3D model according to flight control instructions provided by a flight control management module).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the dynamic model and path updating of Ljubuncic into the inspection system of Castillo-Effen with a reasonable expectation of success because both inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system update the information in real-time and be more useful for present and future inspections.
While Castillo-Effen discloses generating flight plans for a plurality of drones to survey the location based on a 3D model (see at least ¶ [0022-0023] disclosing an autonomously generated flight inspection plan that follows a 3D model of the travel path used to inspect an asset), the combination of Castillo-Effen and Ljubuncic does not explicitly disclose the flight plans are generated based on a desired level of image resolution of one or more objects in the location.
However, Henry suggests the flight plans are generated based on a desired level of image resolution of one or more objects in the location (see at least ¶ [0028] and [0035-0037] describing a UAV following a scan plan wherein a lower-resolution 3D model is generated and subsequently improved with a higher-resolution scan in real-time).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the multiple resolution scanning of Henry into the combination of Castillo-Effen and Ljubuncic with a reasonable expectation of success because all inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system update and maintain an accurate model in real-time and be more useful for present and future inspections.
Regarding claim 2, Castillo-Effen discloses the survey data comprises images of the location (see at least ¶ [0035] describing the robot taking still, video, and thermal images of an asset).
Regarding claim 3, Castillo-Effen does not explicitly disclose the images capture survey markers that comprise encoded location information.
However, Ljubuncic suggests the images capture survey markers that comprise encoded location information (see at least ¶ [0091] describing server information being provided by scanning an RFID tag, an NFC tag, a barcode, a QR code, etc.).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the information tags of Ljubuncic into the inspection system of Castillo-Effen with a reasonable expectation of success because both inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system collect more information about an aspect during an inspection.
Regarding claim 4, Castillo-Effen discloses the survey data comprises positional data for the drones (see at least ¶ [0040] disclosing the inspection process monitoring sensor data, drone location, and position with respect to the asset).
Regarding claim 5, Castillo-Effen discloses the flight plans comprise a set of spline a set of waypoints, or a combination thereof (see at least ¶ [0034] and Fig. 4 disclosing the 3D travel path being constructed from a beginning point, a sequence of intermediary way points, and an end point).
Regarding claim 9, Castillo-Effen discloses prior to generating the flight plans for the plurality of drones, selecting the plurality of drones from a set of available drones based on known features of the location (see at least ¶ [0028] and [0041] disclosing using unmanned robots for inspection tasks that may be embodied as unmanned aerial vehicles (UAVs) and/or crawling robots and may be deployed individually or simultaneously to complete an inspection task).
Regarding claim 10, the combination of Castillo-Effen and Ljubuncic does not explicitly disclose a first object in the location is imaged at a higher resolution than a second object in the location.
However, Henry suggests a first object in the location is imaged at a higher resolution than a second object in the location (see at least ¶ [0099] disclosing a user interface allowing a user to specify through semantic segmentation different parts and/or subjects of an image the UAV captures to have higher or lower resolution).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the multiple resolution scanning of Henry into the combination of Castillo-Effen and Ljubuncic with a reasonable expectation of success because all inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system update and maintain an accurate model in real-time and be more useful for present and future inspections.
Regarding claim 11, the combination of Castillo-Effen and Ljubuncic does not explicitly disclose the desired level of image resolution of the one or more objects in the location is determined based on semantic segmentation of an image of the location.
However, Henry suggests the desired level of image resolution of the one or more objects in the location is determined based on semantic segmentation of an image of the location (see at least ¶ [0099] disclosing a user interface allowing a user to specify through semantic segmentation different parts and/or subjects of an image the UAV captures to have higher or lower resolution).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the multiple resolution scanning of Henry into the combination of Castillo-Effen and Ljubuncic with a reasonable expectation of success because all inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system update and maintain an accurate model in real-time and be more useful for present and future inspections.
Regarding claim 12, Castillo-Effen discloses controlling the plurality of drones to detect anomalies in the location (see at least ¶ [0032] disclosing scanned asset model information includes component labels and semantic information identifying anomalies like cracks).
Regarding claim 13, Castillo-Effen discloses annotating the 3D model with the detected anomalies (see at least ¶ [0032] disclosing scanned asset model information includes component labels and semantic information identifying anomalies like cracks).
Regarding claim 14, Castillo-Effen discloses the detected anomalies comprise rust, cracks, or exposed metal (see at least ¶ [0032] disclosing scanned asset model information includes component labels and semantic information identifying anomalies like cracks).
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. in view of Ljubuncic et al. and Henry et al., as applied to claim 1 above, and in further view of Tran (US 20210089134 A1).
Regarding claim 6, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose the flight plans are generated using a machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use.
However, Tran suggests the flight plans are generated using a machine learning model that comprises an objective function that comprises a weighting of drone flight time, drone wireless signal strength, and drone battery use (see at least ¶ [0144] and [0480] where a neural network governs flight management through determining a cost function of time in air and fuel consumption and managing handovers over networks based on signal strength).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the neural network of Tran into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward operating autonomous unmanned aerial vehicles. While Tran is directed to an autonomous air transportation vehicle, one of ordinary skill in the art would recognize that the factors the neural network of Tran considers would be readily applicable to an inspection system described in the combination of Castillo-Effen, Ljubuncic, and Henry. This would help the manage the plurality of UAVs and their respective resources while performing their inspection tasks.
Regarding claim 15, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose controlling the plurality of drones to scan the radio frequency signal strength of the location.
However, Tran suggests controlling the plurality of drones to scan the radio frequency signal strength of the location (see at least ¶ [0108] and [0480] where a neural network governs flight management through determining and managing handovers over networks based on signal strength).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the neural network of Tran into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward operating autonomous unmanned aerial vehicles. While Tran is directed to an autonomous air transportation vehicle, one of ordinary skill in the art would recognize that the factors the neural network of Tran considers would be readily applicable to an inspection system described in the combination of Castillo-Effen, Ljubuncic, and Henry. This would help the manage the plurality of UAVs and their respective resources while performing their inspection tasks.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. in view of Ljubuncic et al. and Henry et al., as applied to claim 1 above, and in further view of Abhyanker (US 20140180914 A1).
Regarding claim 7, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose receiving an indication from at least one drone of a detection of at least one person;
and updating a flight plan of the at least one drone to avoid flying over the at least one person.
However, Abhyanker suggests receiving an indication from at least one drone of a detection of at least one person (see at least ¶ [0197] and Fig. 9B depicting a multi-copter adjusting speed and course to avoid detected pedestrians);
and updating a flight plan of the at least one drone to avoid flying over the at least one person (see at least ¶ [0197] and Fig. 9B depicting a multi-copter adjusting speed and course to avoid detected pedestrians).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the pedestrian avoidance of Abhyanker into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward operating autonomous unmanned aerial vehicles. This would help the vehicles avoid colliding and causing harm to a person.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. in view of Ljubuncic et al. and Henry et al., as applied to claim 1 above, and in further view of Abhyanker (US 20140180914 A1).
Regarding claim 8, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose predicting illumination of at least one object based on the 3D model;
and updating the flight plans for the plurality of drones surveying the at least one object based on the predicted illumination.
However, Harvey suggests predicting illumination of at least one object based on the 3D model (see at least ¶ [0063-0065] disclosing a quality module that detects glare from comparing image data and ambient light data and assesses the quality of inspection information therefrom);
and updating the flight plans for the plurality of drones surveying the at least one object based on the predicted illumination (see at least ¶ [0063-0065] disclosing a quality module that detects glare from comparing image data and ambient light data and assesses the quality of inspection information therefrom).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the glare detection of Harvey into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward asset inspection systems using drones to gather information for a 3D model. This would help the system mitigate any faulty information in the image data caused by glare.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. in view of Ljubuncic et al. and Henry et al., as applied to claim 1 above, and in further view of Burch et al. (US 20210142271 A1).
Regarding claim 16, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose the survey data is received from the plurality of drones continuously as the plurality of drones are surveying the location.
However, Burch suggests the survey data is received from the plurality of drones continuously as the plurality of drones are surveying the location (see at least ¶ [0269-0270] disclosing sensor-based inspection information of an inspection point is transmitted periodically or streamed).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the transmission timing methods of Burch into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward using drones to perform inspection tasks of a location, object, or asset. This would allow monitors to view inspection subjects either in real-time or in regular intervals.
Regarding claim 17, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose the survey data is received from the plurality of drones periodically.
However, Burch suggests the survey data is received from the plurality of drones periodically (see at least ¶ [0269-0270] disclosing sensor-based inspection information of an inspection point is transmitted periodically or streamed).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the transmission timing methods of Burch into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward using drones to perform inspection tasks of a location, object, or asset. This would allow monitors to view inspection subjects either in real-time or in regular intervals.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Castillo-Effen et al. in view of Ljubuncic et al. and Henry et al., as applied to claim 1 above, and in further view of Dowlatkhah et al. (US 20170242431 A1).
Regarding claim 18, the combination of Castillo-Effen, Ljubuncic, and Henry does not explicitly disclose updating the flight plans based on weather conditions.
However, Dowlatkhah suggests updating the flight plans based on weather conditions (see at least ¶ [0059] disclosing a command center recomputing and updating flight paths for drones based on inclement weather).
It would be obvious to one of ordinary skill in the art before the effective filing date of the present invention to incorporate the weather-based flight path updates of Dowlatkhah into the combination of Castillo-Effen, Ljubuncic, and Henry with a reasonable expectation of success because all inventions are directed toward operating drones in an environment using predetermined flight paths. This would help prevent the drones from being disrupted by weather conditions and help them avoid hazardous situations caused by weather.
Conclusion
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/J.C.B./Examiner, Art Unit 3669
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
7/24/26
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