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 .
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 12399508. Although the claims at issue are not identical, they are not patentably distinct from each other because independent claims 1, 18 and 20 of the claimed invention are not patentably distinct from independent claims 1, 21 and 22 of U.S. Patent No. 12399508 regarding navigating an uncrewed aerial vehicle (UAV) to a delivery location based on determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location.
Claim Rejections - 35 USC § 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, 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 1-7 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schubert et al. (US 20180107211 A1; hereinafter Schubert) in view of Moster et al. (US 20180204469 A1; hereinafter Moster).
Regarding claim 1, Schubert teaches a method comprising: navigating, by an uncrewed aerial vehicle (UAV), to a delivery location in an environment (see at least, 0071] The navigation module 214 may provide functionality that allows the UDV 200 to move about its environment and reach a desired location (e.g., a delivery destination)…in an aerial UDV, navigation module 214 may control the altitude and/or direction of flight); capturing, by at least one sensor on the UAV, sensor data representative of the delivery location (see at least, [0140] FIG. 7A illustrates aerial UDV 180
capturing sensor data representing a portion of delivery destination 602…may hover about, pan the camera on the UDV….to sweep the sensor field of view 702 over the delivery destination to acquire
sensor data representing a region of the delivery destination); determining, based on the sensor data, a segmented point cloud of the delivery location (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery
destination represented by the images. The model may be three dimensional (3D) such as, for example, a point cloud model); wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications (see at least, Fig 6; [0133] The first virtual model
600 may include virtual representations of a plurality of physical features within the first region of the delivery destination (e.g., delivery destination house 602, neighboring houses 601 and 604, windows
606, 608, 610, 614, 626, 628, and 632, doors 612 and 630, vegetation 620 and 622, gate 618, stairs
616 and 634, walkway 624, and sidewalk 636) …may represent the physical features as point clouds),
based on determining that the pre-selected delivery point satisfies the condition (see at least, [0031] Upon marker placement, a validation may be performed to determine whether the delivery vehicle is capable of navigating to the target drop-off spot and whether the package, based on anticipated size and shape of the package, is expected to fit and/or rest securely in the target drop-off spot), initiating, by the UAV, a payload delivery operation towards the pre-selected delivery point (see at least, Fig 8B; [0152] the delivery vehicle may navigate to the target drop-off spot to place the object at the target drop-off spot based on the determined spatial relationship between the delivery vehicle and the target drop-off location…FIG. 8B illustrates aerial UDV 180 hovering above target
drop-off spot 818 before placing package 820 down at the target drop-off spot 820…package 820 is placed in the target drop-off spot specified by the package recipient).
Schubert does not explicitly teach determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location. However, Moster teaches this limitation.
Moster teaches determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder (see at least, Fig 13-D; [0163] As the UAV descends, the vehicle flight buffer 1026 is compared to the point
cloud buffer. The UAV system obtains a geospatial position of the UAV, and determines an x, y, z position in the same 3-D coordinate space as the point cloud. A vehicle flight buffer is computed resulting in a volumetric perimeter around the UAV), the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance (see at least, [0164] The UAV system obtains a geospatial position of the UAV…a radius of based on the certainty of the GPS signal can be used…FIG. D illustrates a cylinder), does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location (see at least, [0164] The vehicle flight buffer may be compared against the point cloud for a given position around the point cloud by the UAV. Any intersection with actual points of the point cloud, or of the point cloud buffer indicates a possible incursion by the UAV into the physical structure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Regarding claim 2, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein determining the segmented point cloud (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery destination represented by the images. The model may be three dimensional (3D) such as, for example, a point cloud model) is based on applying at least one pre-trained machine learning model to the sensor data (see at least, [0127] machine learning algorithms may be utilized to plan the path through which to navigate the vehicle to the target drop-off spot to avoid obstacles, hazards, and/or other environmental features within the delivery destination).
Regarding claim 3, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the condition is one of a plurality of conditions, each of which is associated with a different semantic classification indicative of an obstacle (see at least, [0119] The first virtual model may represent first physical features of the first region of the delivery destination. Within examples, the physical features may include topological features, vegetation, buildings, architectural and/or structural features of buildings, and other objects contained in, adjacent to, or observable from a region of the delivery destination).
Regarding claim 4, the combination of Schubert and Moster teaches the method of claim 3. Moster further teaches wherein each of the plurality of conditions is further associated with a different particular lateral distance away from point cloud areas with a respective semantic classification (see at least, [0031] the structure may be entirely shifted along a lateral direction…An example buffer can be 1 meter, 2 meters, 5 meters, from a surface of the structure and objects as indicated in the point cloud).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include each of the plurality of conditions is further associated with a different particular lateral distance away from point cloud areas with a respective semantic classification as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Regarding claim 5, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein a semantic classification indicative of an obstacle comprises a semantic classification selected from the group consisting of: a tree, a power line, or a body of water (see at least, 0160] The delivery vehicle may be localized with respect to the delivery destination 602 based on distinct patterns of physical features within master model 900. For example, house 901 may include trees 914 and 916 planted to the left).
Regarding claim 6, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the condition further indicates that a semantic classification of the pre-selected delivery point is indicative of a suitable landing or delivery surface (see at least, [0031] Upon marker placement, a validation may be performed to determine whether the delivery vehicle is capable of navigating to the target drop-off spot and whether the package, based on anticipated size and shape of the package, is expected to fit and/or rest securely in the target drop-off spot).
Regarding claim 7, the combination of Schubert and Moster teaches the method of claim 6. Schubert further teaches wherein a semantic classification indicative of a suitable landing or delivery surface comprises a semantic classification selected from the group consisting of: a patio, a lawn, a sidewalk, or a driveway (see at least, [0133] the first virtual model 600 may include virtual representations of a plurality of physical features within the first region of the delivery destination..e.g.,…walkway 624, and sidewalk 636).
Regarding claim 10, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the at least one sensor comprises a camera or a LiDAR sensor (see at least, ([0034] the delivery vehicle may begin capturing sensor data from sensors on the delivery vehicle...
e.g., cameras, depth sensors, light detection and ranging (LIDAR) device).
Regarding claim 11, the combination of Schubert and Moster teaches the method of claim 1.
Schubert further teaches the payload delivery operation comprises delivery with the tether (see at least, [0091] In order to deliver the payload, the aerial UDV may include a retractable delivery system that lowers the payload to the ground while the UDV hovers above…The release mechanism can be configured to secure the payload while being lowered from the UDV by the tether and release the payload upon reaching ground level).
Regarding claim 12, the combination of Schubert and Moster teaches the method of claim 1.
Moster further teaches wherein the at least one delivery point in the delivery location satisfies an additional condition indicating that the descent path above the at least one delivery point represented in the point cloud is at least an additional particular lateral distance away from point cloud areas with corresponding semantic classifications indicative of an obstacle at the delivery location (see at least, [0036] the UAV can utilize the point cloud to determine objects, structures, in a path to the landing area, and may be able to simply move laterally and descend to the landing area), wherein the additional particular lateral distance ([0032] UAVs may be able to modify their courses (e.g., ascend, move laterally)….move a second threshold distance (e.g., 50 centimeters, 1 meter) is greater than the particular lateral distance ([0032] a first threshold distance (e.g., 10 centimeters, 20 centimeters) and enables landing of the UAV at the delivery location (see at least, [0036] the UAV can utilize the point cloud to determine objects, structures, in a path to the landing area, and may be able to simply move laterally and descend to the landing area).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include the at least one delivery point in the delivery location satisfies an additional condition indicating that the descent path above the at least one delivery point represented in the point cloud is at least an additional particular lateral distance away from point cloud areas with corresponding semantic classifications indicative of an obstacle at the delivery location, wherein the additional particular lateral distance is greater than the particular lateral distance and enables landing of the UAV at the delivery location as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Regarding claim 13, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the descent path is from a ground surface at the pre-selected delivery point to a predetermined altitude above the pre-selected delivery point, wherein the predetermined altitude is associated with where the UAV captured the sensor data (see at least, [0140] Upon arriving within a threshold distance of the delivery destination (e.g., within a horizontal and/or vertical accuracy limit of the GPS system), the control system may begin capturing data from sensors on the delivery vehicle for localization and navigation of the vehicle to the target drop-off spot for the object, as illustrated in FIG. 7A).
Regarding claim 14, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the sensor data comprises two-dimensional representations of the delivery location (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery destination represented by the images…the model may be two dimensional (2D) and may include a depth map), wherein the point cloud is a three-dimensional representation of the delivery location (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery destination represented by the images. The model may be three dimensional (3D) such as, for example, a point cloud model).
Regarding claim 15, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein determining the pre-selected delivery point is based on determining that the pre-selected delivery point is at a particular location relative to a building (see at least, [0124] a second virtual model of the second region of the delivery destination may be determined based on the sensor data. The second virtual model may represent second physical features of the second region of the delivery destination…the physical features represented by the second virtual model may include… buildings…adjacent to, or observable from the second region of the delivery destination).
Regarding claim 16, the combination of Schubert and Moster teaches the method of claim 1. Schubert further teaches wherein the method further comprises: capturing, by the UAV, one or more additional images of the delivery location (see at least, [0130] As successive images are captured, either discretely or from a video stream, the images may be used to construct the first virtual model of the first region of the delivery destination represented by the images); verifying, based on the one or more additional images of the delivery location, whether the pre-selected delivery point satisfies the condition (see at least, [0041] a validation may be performed to determine whether the delivery vehicle is capable of navigating to the target drop-off spot and whether the package, based on anticipated size and shape of the package, is expected to fit and/or rest securely in the target drop-off spot); and based on verifying that the pre-selected delivery point does satisfy the condition, descending to a particular altitude above the pre-selected delivery point (see at least, [0140] Aerial UDV 180 may hover about,
pan the camera on the UDV, or a combination thereof to sweep the sensor field of view 702 over
the delivery destination to acquire sensor data representing a region of the delivery destination).
Regarding claim 17, the combination of Schubert and Moster teaches the method of claim 1. Moster further teaches wherein the semantic classifications indicative of an obstacle comprise semantic classifications corresponding to an unacceptable delivery surface and semantic classifications corresponding to an object exceeding a threshold height (see at least, [0035] a buffer is determined for the structure, objects, UAV, and so on, the buffer can be more complex and be associated with a statistical confidence at distances from the structure, objects, or UAV…the system can determine a flight plan as being unfeasible if a likelihood of a collision is greater than a threshold percent).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include the semantic classifications indicative of an obstacle comprise semantic classifications corresponding to an unacceptable delivery surface and semantic classifications corresponding to an object exceeding a threshold height as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Regarding claim 18, Schubert teaches an uncrewed aerial vehicle (UAV) (see at least, [0010] FIG. 1A
illustrates an unmanned aerial vehicle), comprising: at least one sensor; and a control system (see at least, [0006] one or more sensors connected to the delivery vehicle, and a control system) configured to: navigate, by the UAV, to a delivery location in an environment (see at least, 0071] The navigation
module 214 may provide functionality that allows the UDV 200 to move about its environment and reach a desired location (e.g., a delivery destination)…in an aerial UDV, navigation module 214 may control the altitude and/or direction of flight); capture, by at least one sensor on the UAV, sensor data representative of the delivery location (see at least, [0140] FIG. 7A illustrates aerial UDV 180
capturing sensor data representing a portion of delivery destination 602…may hover about, pan the camera on the UDV….to sweep the sensor field of view 702 over the delivery destination to acquire sensor data representing a region of the delivery destination); determine, based on the sensor data, a segmented point cloud of the delivery location (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery destination represented by the images. The model may be three dimensional (3D) such as, for example, a point cloud model), wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications (see at least, Fig 6; [0133] The first virtual model
600 may include virtual representations of a plurality of physical features within the first region of the delivery destination (e.g., delivery destination house 602, neighboring houses 601 and 604, windows
606, 608, 610, 614, 626, 628, and 632, doors 612 and 630, vegetation 620 and 622, gate 618, stairs
616 and 634, walkway 624, and sidewalk 636) …may represent the physical features as point clouds).
based on determining that the pre-selected delivery point satisfies the condition (see at least, [0031] Upon marker placement, a validation may be performed to determine whether the delivery vehicle is capable of navigating to the target drop-off spot and whether the package, based on anticipated size and shape of the package, is expected to fit and/or rest securely in the target drop-off spot), initiate, by the UAV, a payload delivery operation towards the pre-selected delivery point (see at least, Fig 8B; [0152] the delivery vehicle may navigate to the target drop-off spot to place the object at the target
drop-off spot based on the determined spatial relationship between the delivery vehicle and the
target drop-off location…FIG. 8B illustrates aerial UDV 180 hovering above target drop-off spot 818
before placing package 820 down at the target drop-off spot 820…package 820 is placed in the target
drop-off spot specified by the package recipient).
Schubert does not explicitly teach determine, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location. However, Moster teaches this limitation.
Moster teaches determine, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder (see at least, Fig 13-D; [0163] As the UAV descends, the vehicle flight buffer 1026 is compared to the point
cloud buffer. The UAV system obtains a geospatial position of the UAV, and determines an x, y, z position in the same 3-D coordinate space as the point cloud. A vehicle flight buffer is computed resulting in a volumetric perimeter around the UAV), the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance (see at least, [0164] The UAV system obtains a geospatial position of the UAV…a radius of based on the certainty of the GPS signal can be used…FIG. D illustrates a cylinder), does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location (see at least, [0164] The vehicle flight buffer may be compared against the point cloud for a given position around the point cloud by the UAV. Any intersection with actual points of the point cloud, or of the point cloud buffer indicates a possible incursion by the UAV into the physical structure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include determine, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Regarding claim 19, the combination of Schubert and Moster teaches the UAV of claim 18. Schubert further teaches comprising a tether, where the payload delivery operation comprises delivery with the tether (see at least, [0091] In order to deliver the payload, the aerial UDV may include a retractable delivery system that lowers the payload to the ground while the UDV hovers above…The release mechanism can be configured to secure the payload while being lowered from the UDV by the
tether and release the payload upon reaching ground level).
Regarding claim 20, Schubert teaches a non-transitory computer readable medium comprising program instructions executable by one or more processors to perform operations (see at least, [007] a non-transitory computer-readable storage medium is provided having stored thereon instructions that, when executed by a computing device, cause the computing device to perform operations), the operations comprising: navigating, by an uncrewed aerial vehicle (UAV), to a delivery location in an environment (see at least, [0071] The navigation module 214 may provide functionality that allows the UDV 200 to move about its environment and reach a desired location (e.g., a delivery destination)…in an aerial UDV, navigation module 214 may control the altitude and/or direction of flight); capturing, by at least one sensor on the UAV, sensor data representative of the delivery location (see at least, [0140] FIG. 7A illustrates aerial UDV 180 capturing sensor data representing a portion of delivery destination 602…may hover about, pan the camera on the UDV….to sweep the sensor field of view 702 over the delivery destination to acquire sensor data representing a region of the delivery destination); determining, based on the sensor data, a segmented point cloud of the delivery location (see at least, [0131] The images, along with the corresponding additional sensor data, may be used to generate a model of the region of the delivery destination represented by the images. The model may be three dimensional (3D) such as, for example, a point cloud model); wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications (see at least, Fig 6; [0133] The first virtual model 600 may include virtual representations of a plurality of physical features within the first region of the delivery destination (e.g., delivery destination house 602, neighboring houses 601 and 604, windows 606, 608, 610, 614, 626, 628, and 632, doors 612 and 630, vegetation 620 and 622, gate 618, stairs 616 and 634, walkway 624, and sidewalk 636)…may represent the physical features as point clouds), based on determining that the pre-selected delivery point satisfies the condition (see at least, [0031] Upon marker placement, a validation may be performed to determine whether the delivery vehicle is capable of navigating to the target drop-off spot and whether the package, based on anticipated size and shape of the package, is expected to fit and/or rest securely in the target drop-off spot), initiating, by the UAV, a payload delivery operation towards the pre-selected delivery point (see at least, Fig 8B; [0152] the delivery vehicle may navigate to the target drop-off spot to place the object at the target drop-off spot based on the determined spatial relationship between the delivery vehicle and the target drop-off location…FIG. 8B illustrates aerial UDV 180 hovering above target drop-off spot 818 before placing package 820 down at the
target drop-off spot 820…package 820 is placed in the target drop-off spot specified by the package recipient).
Schubert does not explicitly teach determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location. However, Moster teaches this limitation.
Moster teaches determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder (see at least, Fig 13-D; [0163] As the UAV descends, the vehicle flight buffer 1026 is compared to the point
cloud buffer. The UAV system obtains a geospatial position of the UAV, and determines an x, y, z position in the same 3-D coordinate space as the point cloud. A vehicle flight buffer is computed resulting in a volumetric perimeter around the UAV), the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance (see at least, [0164] The UAV system obtains a geospatial position of the UAV…a radius of based on the certainty of the GPS signal can be used…FIG. D illustrates a cylinder), does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location (see at least, [0164] The vehicle flight buffer may be compared against the point cloud for a given position around the point cloud by the UAV. Any intersection with actual points of the point cloud, or of the point cloud buffer indicates a possible incursion by the UAV into the physical structure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Schubert et al. (US 20180107211 A1; hereinafter Schubert) in view of Moster et al. (US 20180204469 A1; hereinafter Moster) in further view of Harris et al. (US 10056001 B1; hereinafter Harris).
Regarding claim 8, the combination of Schubert and Moster teaches the method of claim 1. The combination does not explicitly teach wherein the condition comprises a first condition and a second condition, wherein the first condition indicates that the descent path is at least a first lateral distance away from a point cloud area with a semantic classification indicative of a building of a first height, and the second condition indicates that the descent path is at least a second lateral distance away from a point cloud area with a semantic classification indicative of a building of a second height, where the first height is greater than the second height and the first lateral distance is greater than the second lateral distance. However, Moster and Harris teach these limitations.
Moster further teaches wherein the at least one condition comprises a first condition and a second condition, wherein the first condition indicates that the descent path is at least a first lateral distance away from point cloud areas with corresponding semantic classifications (see at least, [0032] UAVs may be able to modify their course…move laterally…move a second threshold distance…e.g., 50 centimeters, 1 meter), wherein the second condition indicates that the descent path is at least a second lateral distance away from point cloud areas with corresponding semantic classifications (see at least, [0032] UAVs may be able to modify their course…move laterally…and move a first threshold distance
…e.g., 10 centimeters, 20 centimeters) and the first lateral distance (see at least, [0032] a second
threshold distance …e.g., 50 centimeters, 1 meter) is greater than the second lateral distance (see at least, [0032] a first threshold distance…e.g., 10 centimeters, 20 centimeters).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schubert to include the at least one condition comprises a first condition and a second condition, wherein the first condition indicates that the descent path is at least a first lateral distance away from point cloud areas with corresponding semantic classifications wherein the second condition indicates that the descent path is at least a second lateral distance away from point cloud areas with corresponding semantic classifications and the first lateral distance is greater than the second lateral distance as taught by Moster so that the system can utilize sources of error associated with the point cloud and UAV's ability to determine its location and navigate, to determine offsets from the structure that the UAV has to remain to avoid collision (Moster, [0122]).
The combination does not explicitly teach wherein the at least one condition comprises a first condition and a second condition, wherein the first condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a first height, wherein the second condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a second height, wherein the first height is greater than the second height . However, Harris teach these limitations.
Harris teaches wherein the at least one condition comprises a first condition and a second condition, wherein the first condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a first height (see at least, Col 2 lines 54-65, an aerial vehicle…is descending…a first object…extends approximately twenty meters above the surface…is descending toward a delivery destination, the objects may include buildings), wherein the second condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a second height (see at least, Col 2 lines 54-65, an aerial vehicle…is descending…a second object…extends approximately ten meters above the surface…is descending toward a delivery destination, the objects may include buildings), wherein the first height is greater (Col 2 lines 57-58, a first object…extends approximately twenty meters above the surface) than the second height (Col 2 lines 58-59, a second object…extends approximately ten meters above the surface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Schubert and Moster to include the first condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a first height, wherein the second condition indicates that the descent path is a distance away from point cloud areas with corresponding semantic classifications indicative of a building of a second height, wherein the first height is greater than the second height and the first lateral distance is greater than the second lateral distance as taught by Harris in order to aid an aerial vehicle in object detection and avoidance (Harris, Col 6 lines 56-57).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Schubert et al. (US 20180107211 A1; hereinafter Schubert) in view of Moster et al. (US 20180204469 A1; hereinafter Moster) in further view of Meadow et al. (US 20170369167 A1; hereinafter Meadow).
Regarding claim 9, the combination of Schubert and Moster teaches the method of claim 1. The combination does not explicitly teach wherein determining the pre-selected delivery point comprises selecting the pre-selected delivery point from a plurality of candidate delivery points evenly spaced in a grid pattern in the environment. However, Meadow teaches this limitation.
Meadow teaches wherein determining, based on the segmented point cloud (see at least, [0015] three dimensional (3-D) geometries or point cloud around landings may be used for identification and subsequent registration of a Delivery Zone), the at least one delivery point in the delivery location comprises selecting a delivery point from a plurality of candidate delivery points evenly spaced in a grid pattern in the environment (see at least, [0057] The Delivery Zones can include special patterns…so that current information about the Delivery Zones can be determined by the marks…Landings
zones may be large in size, and in an area that may be very large and identifiable by a grid pattern, where the grid pattern may appear dynamically so that the Unmanned Aerial Vehicle 101 can see a spot that UAV…or its payload is designated to land).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of Schubert and Moster teaches to include determining, based on the segmented point cloud, the at least one delivery point in the delivery location comprises selecting a delivery point from a plurality of candidate delivery points evenly spaced in a grid pattern in the environment as taught by Meadow so that the UAV can access just the relevant portions of the delivery path data and adjust its delivery path accordingly precise placement of a payload carried by an unmanned aerial vehicle (UAV) to a delivery zone (Meadow, [0024]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jenkins et al. (US 20240019589 A1) discloses navigating, by an uncrewed aerial vehicle (UAV), to a delivery location in an environment ([0075] horizontal descent and/or horizontal ascent as described do not include a hover phase during which UAV 100 holds a position over a landing or pickup/delivery location).
Dupray et al. (US 20170069214 A1) discloses navigating the UAV to a plurality of locations ([0306] UAV travel in a coordinated manner, along paths such that any given UAV is within landing distance of a LTS. Having a plurality of LTS provides improved safety, and reliability of UAV, delivery services, and other related benefits to successfully carry out a given UAV mission plan).
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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