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 action is in reply to the amendments filed on 06/26/2026.
Claims 1-18 are currently pending and have been examined.
Claims 1-10 are amended.
Claims 11-18 are added.
Claims 1-18 are currently rejected.
This action is made FINAL.
Response to Arguments
Applicant’s arguments filed 06/26/2026 have been fully considered but they are not persuasive.
In light to the amended drawing, the drawing objection has been withdrawn.
Regarding the 112 rejections, in light of the amendments these rejections have been withdrawn.
Applicant’s arguments with regards to the art rejections have been considered and appear to be directed solely to the instant amendments to the claims. Accordingly, the claims are addressed in the body of the rejections below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hedman et. al. (US 6,157,875), herein Hedman in view of Farris et. al. (US 2025/0251242), herein Farris, O’Leary et. al. (US 2025/0074595), herein O’Leary, and Brockers et. al. (US 2023/0360547), herein Brockers.
Regarding claim 1:
Hedman teaches:
A method (an image guided system and method [col 1, lines 66-67]) for generating a predictive navigation system (navigation means for guiding the weapon to the aimpoint marked on the image template. [col 2, lines 18-20]) comprising:
defining, based on a mission tasking (a pilot utilizes the aimpoint selection device 25 to identify the target aimpoint, which is subsequently marked on the digital image as described further below. The aimpoint may alternatively be selected well in advance by a mission planner, who then physically tags the aimpoint on the image from image detector 15 [col 4, lines 51-56]), a mission route (The navigational direction can be pre-planned or can be determined during flight by the pilot prior to weapon launch [col 5, lines 9-11]);
identifying a plurality of mission planning images (Means for selecting an aimpoint for the target in the digital image are provided with the invention, and preferably comprise an aimpoint selection device 25 such as a pointing device [col 4, lines 48-51]), wherein the plurality of mission planning images correspond to a predetermined mission route (the image template generating software includes program means for carrying out the operations of marking a selected aimpoint onto the digital image from image sensor 15, adding GPS coordinates for the aimpoint from GPS sensor 30 to the digital image, and generating an image template from the digital image, the aimpoint marked on the digital image, and the GPS coordinates added to the digital image. The image template generated by the programming utilizes key geographical features of the digital image which are most easily recognizable, together with the aimpoint and the GPS coordinates for the aimpoint. Preferably, the image template also includes flight orientation data for the aircraft at the time of weapon launch [col 5, lines 32-44]);
identifying a plurality of geospatial data corresponding to the plurality of mission planning images (the image template generating software includes program means for carrying out the operations of marking a selected aimpoint onto the digital image from image sensor 15, adding GPS coordinates for the aimpoint from GPS sensor 30 to the digital image, and generating an image template from the digital image, the aimpoint marked on the digital image, and the GPS coordinates added to the digital image [col 5, lines 32-38]);
providing to a model (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20]):
a mission plan including a [plurality] of location markers that correspond to locations along the mission route (At step 115, the aimpoint is selected and the positional coordinates of the aimpoint are determined [col 6, lines 57-58]);
the plurality of mission planning images (At step 105 a three-dimensional or two-dimensional image of the target area is generated or acquired from one of a plurality of sources such as photographs, maps, synthetic aperture radar image, or an infrared image, which are generated by image sensor 15 or another source. The image may be generated on-board, or prior to flight [col 6, lines 42-48]), and
the plurality of geospatial data (The GPS Detector 30 can be used to determine the location of the aircraft, the target area generally, as well as the aimpoint [col 5, lines 2-4]);
operating the model to generate a plurality of predicted [time sequenced] and geo-sequenced images (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20]) and predicted mission data (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20])
wherein the plurality of [time sequenced] and geo-sequenced images depicts landscapes along the mission plan corresponding to the mission plan (The image detection algorithms evaluate and select specific features such as road edges, building edges, trees, streams and other physical characteristics to generate the image template 130 [col 7, lines 24-27])
wherein the predicted mission data includes one or more of predicted sensor data (the digitized image of the target area from step 105 [col 7, line 17]), magnetic vector and gravitation gradient data (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), Lidar information (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), digital terrain elevation data (DTED) (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), and inertial data (the image template 130 also includes flight orientation 110 data [col 7, lines 27-28]);
providing, to a platform control system, the plurality of predicted [time sequenced] and geo-sequenced images (At step 135, the image template 130 generated at step 125 is downloaded to the weapon or IGB from mission planner processor 40 via data link 50. [col 7, lines 30-34]) and predicted mission data (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20])
wherein the plurality of predicted time sequenced and geo-sequenced images and predicted mission data are provided to the platform control system before a mission (At step 135, the image template 130 generated at step 125 is downloaded to the weapon or IGB from mission planner processor 40 via data link 50. This step may be carried out in flight just prior to launch or prior to flight in cases where mission planner processor 40 is external to the aircraft [col 7, lines 30-34]); and
Farris also teaches:
wherein the predicted mission data includes one or more of predicted sensor data (), magnetic vector and gravitation gradient data (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), Lidar information (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), digital terrain elevation data (DTED) (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), and inertial data (the image template 130 also includes flight orientation 110 data [col 7, lines 27-28]);
Hedman does not explicitly teach, however Farris teaches:
an estimated mission time (imaging data of multiple modalities can be used to generate location information for navigating the aircraft; because the various modalities will have different operational characteristics which depend on the conditions (e.g., weather, time of day), as a combination, the use of multiple modalities improves the overall probability or confidence that aircraft guidance system 405 will continually receive reliable data for navigation [0065])
a mission plan including a plurality of location markers that correspond to locations along the mission route (Tiles 101 may include various topographical features such as man-made structures 151 and natural formations 152 [0029]);
operating the model to generate a plurality of predicted time sequenced and geo-sequenced images (In an implementation, LiDAR CNN 531 and EO CNN 532 are trained on training data 545 which includes EO image data 546 and LiDAR image data 547. Training data 545 includes image data including identifiable landmarks on images of terrain [0069]),
wherein the plurality of time sequenced and geo-sequenced images depicts landscapes along the mission plan corresponding to the mission plan (The 2D point cloud image is ingested by the LiDAR-trained CNN to detect landmarks in the image. When a landmark is identified, the model outputs latitude and longitude information of the landmark. The location data can then be used to extrapolate the latitude and longitude of the aircraft when the LiDAR image was taken [0020]);
providing, to a platform control system, the plurality of predicted time sequenced and geo-sequenced images (In an implementation, LiDAR CNN 531 and EO CNN 532 are trained on training data 545 which includes EO image data 546 and LiDAR image data 547. Training data 545 includes image data including identifiable landmarks on images of terrain [0069]),
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman to include the teachings as taught by Farris with a reasonable expectation of success. Both are in the same field of endeavor of aeronautical navigation. Farris additionally teaches the benefits of “while EO images may be sufficient for landmark identification while flying in clear weather during the day, during nighttime, thermal imaging sensors may produce more useful imaging data than the EO sensors. Similarly, where Light Detection and Ranging (LiDAR) sensing may be degraded in rainy conditions, a longer wavelength imaging modality such as radar may produce more useful imaging data than LiDAR sensors. By processing image data of multiple modalities of terrain over which an aircraft is flying to ascertain the aircraft's location and direction of travel, the aircraft can continually receive reliable location data for navigation. Thus, aircraft can fly missions using autonomous navigation in low-visibility environments or in areas where Global Positioning System (GPS) signals are unavailable for navigation [Farris, 0015]”.
Hedman in view of Farris do not explicitly teach, however O’Leary teaches:
presenting, during the mission, the predicted time sequenced and geo-sequenced images to a pilot in real mission time (One method of enabling the display methods described above may to provide multiple aircraft sensors that cover the entire sphere around the airplane. Images (data) from these sensors may be “stitched” together to form a single, spherical image that may be viewed from the inside. A selected portion of this view may be provided to device display 136. Building on the prior discussion of multiple viewpoints, more than one spherical image can be created. Different spherical views can be paired to provide stereoscopic views. Some spherical views may be created synthetically by interpolation or extrapolation from data from aircraft sensors. These synthetic views may be from a selected or variably selected viewpoint (spherical center). In addition to providing external aircraft data by input device 104 to device display 136 [0034]) corresponding to the estimated mission time associated with the predicted time sequenced and geo-sequenced images (In some embodiments, external aircraft data 108 may include virtually generated data 120 of an outside of an aircraft wherein external aircraft data 108 is a computer-generated environment. “Virtually generated data” as described herein is data created through a computer simulation as opposed to real data captured from, for example, a video input device. In some cases, input device 104 may generate a virtually generated environment or receive a virtually generated environment from a computing device 112. Additionally, or alternatively, the virtually generated environment may be representative of a surrounding environment of an aircraft. In some embodiments, external aircraft data 108 may include previously recorded data such as video data recorded on a previous occasion. In some embodiment, aircraft data may include a combination of aircraft orientation data, inertial data and/or pilot control data combined with virtually generated data 120. In this embodiment, a virtually generated environment may be created by computing device wherein computing device may generate a virtual environment based on the real data collected above. In some embodiments, external aircraft data 108 may include a combination or real-time data and previously recorded data [0020]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris to include the teachings as taught by O’Leary with a reasonable expectation of success. Both are in the same field of endeavor of detecting external environments of an aircraft. O’Leary additionally teaches the benefits of “display device 136 may be placed in lieu or in addition to flight deck windshields. In this embodiment, a pilot may use display device 136 to navigate aircraft. In some embodiments, display device 136 may contain a transparent display, wherein pilot may use display device 136 both as a window and as a display. In some embodiments, display device 136 may provide a pilot with information surrounding the aircraft such that a pilot may make informed decisions during aerial flight [O’Leary, 0044]”.
Hedman in view of Farris and O’Leary does not explicitly teach, however Brockers teaches:
and include one or more of a predicted light level at the estimated mission time (When high-accuracy elevation and texture models of the terrain are available, full robustness to illumination could potentially be achieved by artificially rendering the map image in the query conditions. [0020]; alter the brightness of pixels in the map ortho-image/map image in order to derive descriptors that are adapted to the simulated illumination regime at the different times of day. [0199]), predicted shadow lines that correspond to the mission time, mission location, and platform orientation during the mission (the digital elevation map (DEM) may be used to render virtual shadows on the terrain for different times of day and use the rendered (virtual) shadows to alter the brightness of pixels in the map ortho-image/map image in order to derive descriptors that are adapted to the simulated illumination regime at the different times of day. [0199]), and predicted celestial views including the sun, moon, stars, and manmade objects in space from a specific viewing location, viewing orientation, and viewing time (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris and O’Leary to include the teachings as taught by Brockers with a reasonable expectation of success. Both are in the same field of endeavor of detecting external environments of an aircraft. Brockers teaches “The following description reviews existing research work addressing the problem of matching a query image against a map image. The query image is assumed to have been captured by a camera mounted on the robot one wishes to localize (e.g., MSH in an exemplary application). The map image is assumed to have been captured at a different time, potentially by a different camera (e.g., HiRISE). Both the query and map images are assumed to be in the visible spectrum, and to overlap over a portion of terrain with enough texture and enough illumination to provide the information for a unique match. The objective of this research area is to identify image descriptors invariant to the potentially-severe transformation between the query and the map: scale, viewpoint, illumination or even change in the terrain itself. [Brockers, 0013]”
Regarding claim 2:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Farris further teaches:
wherein operating the model to generate a plurality of predicted time sequenced and geo-sequenced images further includes:
(a) generating a provisional set of predicted time sequenced and geo-sequenced images (Upon receiving the 3D point cloud data, LiDAR image processor 521 processes the data to produce a 2D point cloud of data from the 3D point cloud data. To produce the 2D point cloud data, LiDAR image processor 521 may geo-rectify or orthorectify the data to remove distortions from data, then project the point cloud to a ground plane. LiDAR image processor 521 then transmits the 2D point cloud data to LiDAR CNN 531. LiDAR image processor 521 also determines the altitude of the aircraft based on ranging information embodied in the image data from LiDAR sensor 511 [0072]);
(b) reviewing the provisional set of predicted time sequenced and geo-sequenced images to determine if the provisional set depicts the planned mission region to an acceptable degree (Based on location information from the LiDAR imaging and the EO imaging, the flight control system onboard aircraft 110 computes a location of aircraft 110 by weighting the location information of the two modalities according to the respective confidence levels. For example, the flight control system may extrapolate a location of the aircraft from the landmark location information of each landmark identified in the LiDAR imaging and EO imaging and generating a composite location by aggregating (e.g., averaging) the extrapolated locations weighted according to the respective confidence metrics [0036]);
(c) if the provisional set is determined not to depict the planned mission region to an acceptable degree, rerunning the model with different weightings to generate another provisional set of predicted time sequenced and geo-sequenced images (The computing device determines a location of the aircraft based on the landmark locations identified in the LiDAR imaging data and other imaging data (step 205). In an implementation, the computing device receives the location information from the CNNs in the form of latitude and longitude. The latitude and longitude of a final or composite location of the aircraft are computed as weighted averages of the latitudes and longitudes of the landmark locations. The weighting for computing the weighted averages is based on the confidence metrics determined by the respective CNNs [0043]; The aircraft location determined based on the physical sensor data may also be used to refine the output of the CNNs to improve accuracy. [0044]); and
(d) repeating steps (a) to (c) until the provisional set of predicted time sequenced and geo-sequenced imaged are determined to depict the planned mission region to an acceptable degree (In various implementations, the computing device continually acquires imaging data and processes the data to get up-to-date location information. As its present position is determined, the computing device may execute a location verification system to check or confirm the location ascertained based on the output of the CNNs. For example, the verification system may continually calculate latitude and longitude using gyroscopic, compass, IMU, and/or accelerometer data to remove false position determinations from the convolutional neural network. The aircraft location determined based on the physical sensor data may also be used to refine the output of the CNNs to improve accuracy [0044]).
Regarding claim 3:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Farris further teaches:
wherein the model is a neural network that uses a plurality of nodes to generate the plurality of predicted time sequenced and geo-sequenced images (the aircraft navigation system processes the image to detect identifiable landmarks using a trained convolutional neural network (CNN) [0016]).
Regarding claim 4:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman further teaches:
presenting, during the mission, the predicted time sequenced and geo-sequenced images to the pilot (At step 235, IGB processor 55 compares and correlates the image template 130 with each seeker image obtained in step 225 and processed in step 230. If a satisfactory correlation between the image template and a seeker image, step 240 below is carried out. If no correlation of the image template and the seeker image is made, step 220 is repeated wherein the image template is again scaled and rotated, and then step 235 is carried out again with the next sequential seeker image being compared to the image template [col 7, line 62 – col 8, line 3]; examiner notes that while Hedman teaches an autopilot system, it would be obvious to take the data generated by Hedman and display it like taught in O’Leary.); and
O’Leary further teaches:
presenting images captured in real time, during the mission, using a platform sensor to the pilot (display device 136 may be placed in lieu or in addition to flight deck windshields. In this embodiment, a pilot may use display device 136 to navigate aircraft. In some embodiments, display device 136 may contain a transparent display, wherein pilot may use display device 136 both as a window and as a display. In some embodiments, display device 136 may provide a pilot with information surrounding the aircraft such that a pilot may make informed decisions during aerial flight [0044]).
Regarding claim 5:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 3, upon which this claim is dependent.
Hedman further teaches:
comparing the predicted time sequenced and geo-sequenced images to the images captured in real time, during the mission, using a platform sensor (At step 235, IGB processor 55 compares and correlates the image template 130 with each seeker image obtained in step 225 and processed in step 230. If a satisfactory correlation between the image template and a seeker image, step 240 below is carried out. If no correlation of the image template and the seeker image is made, step 220 is repeated wherein the image template is again scaled and rotated, and then step 235 is carried out again with the next sequential seeker image being compared to the image template [col 7, line 62 – col 8, line 3]); and
Farris further teaches:
sending an alert to the pilot if the predicted time sequenced and geo-sequenced images do not match the real time images (the independent verification system may detect significant difference between the aircraft's presently identified location and previously identified location, the verification system will flag the data as false [0024] examiner notes that while Farris teaches an autopilot system, it would be obvious to take the data generated by Farris and display it like taught in O’Leary to a pilot.).
Regarding claim 6:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 3, upon which this claim is dependent.
Hedman further teaches:
comparing the predicted time sequenced and geo-sequenced images to the images captured in real time, during the mission, using a platform sensor (At step 235, IGB processor 55 compares and correlates the image template 130 with each seeker image obtained in step 225 and processed in step 230. If a satisfactory correlation between the image template and a seeker image, step 240 below is carried out. If no correlation of the image template and the seeker image is made, step 220 is repeated wherein the image template is again scaled and rotated, and then step 235 is carried out again with the next sequential seeker image being compared to the image template [col 7, line 62 – col 8, line 3]); and
Farris further teaches:
rerouting the platform, during the mission, using the platform control system, to reduce errors between the predicted time sequenced and geo-sequenced images and the images captured using the platform sensor (Navigation system 530 may also correct the position data for the angle of the sensor, the aircraft velocity, and/or other factors which may impact the accuracy of the determinations [0075]).
Regarding claim 7:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman further teaches:
sending the predicted time sequenced and geo-sequenced images to an inertial navigation system (At step 235, IGB processor 55 compares and correlates the image template 130 with each seeker image obtained in step 225 and processed in step 230 [col 7, lines 62-64]); and
reducing inertial sensor errors in the inertial navigation system using the predicted time sequenced and geo-sequenced images (Once a satisfactory correlation is made between the image template 130 and seeker image, step 240 is carried out in which IGB processor 55 updates the positional coordinates of the aimpoint of the IGB by using inertial navigation system 65 to calculate a setoff distance in inertial space. The setoff distance is based on or reference to the GPS navigation coordinates used in 210 and/or INS navigation coordinates used in step 215 [col 8, lines 3-11]).
Regarding claim 9:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
O’Leary further teaches:
presenting, during the mission, the predicted time sequenced and geo-sequenced images to the pilot in real mission time using a multi-axis viewing system (In some embodiments, display device 136 may be located within a cockpit of aircraft. In some embodiments, display device 136 may be placed in lieu or in addition to flight deck windshields. In this embodiment, a pilot may use display device 136 to navigate aircraft. In some embodiments, display device 136 may contain a transparent display, wherein pilot may use display device 136 both as a window and as a display. In some embodiments, display device 136 may provide a pilot with information surrounding the aircraft such that a pilot may make informed decisions during aerial flight [0044]).
Regarding claim 10:
Hedman teaches:
A mission control system (fig. 1, image guided weapon system 10) comprising:
a processor (fig. 1, mission planner processor 40) configured to receive from a model (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20]) a plurality of predicted time sequenced and geo-sequenced images that depict landscapes along a mission plan (the image template generating software includes program means for carrying out the operations of marking a selected aimpoint onto the digital image from image sensor 15, adding GPS coordinates for the aimpoint from GPS sensor 30 to the digital image, and generating an image template from the digital image, the aimpoint marked on the digital image, and the GPS coordinates added to the digital image [col 5, lines 32-38]) corresponding to a mission plan (a pilot utilizes the aimpoint selection device 25 to identify the target aimpoint, which is subsequently marked on the digital image as described further below. The aimpoint may alternatively be selected well in advance by a mission planner, who then physically tags the aimpoint on the image from image detector 15 [col 4, lines 51-56])
and predicted mission data (At step 125, an image template 130 is generated by template generation software associated with mission planner processor 40. The template generation software processes the digitized image of the target area from step 105 and step 120, the flight orientation data from step 110, and the selected aimpoint and corresponding GPS coordinate from step 115, to create image template 130 [col 7, lines 14-20])
wherein the predicted mission data includes one or more of predicted sensor data (the digitized image of the target area from step 105 [col 7, line 17]), magnetic vector and gravitation gradient data (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), Lidar information (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), digital terrain elevation data (DTED) (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), and inertial data (the image template 130 also includes flight orientation 110 data [col 7, lines 27-28]);
Farris also teaches:
wherein the predicted mission data includes one or more of predicted sensor data (), magnetic vector and gravitation gradient data (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), Lidar information (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), digital terrain elevation data (DTED) (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.), and inertial data (the image template 130 also includes flight orientation 110 data [col 7, lines 27-28]);
Hedman does not explicitly teach, however Farris teaches:
a plurality of predicted time sequenced and geo-sequenced images that depict landscapes along a mission plan (The 2D point cloud image is ingested by the LiDAR-trained CNN to detect landmarks in the image. When a landmark is identified, the model outputs latitude and longitude information of the landmark. The location data can then be used to extrapolate the latitude and longitude of the aircraft when the LiDAR image was taken [0020]);
an estimated mission time (imaging data of multiple modalities can be used to generate location information for navigating the aircraft; because the various modalities will have different operational characteristics which depend on the conditions (e.g., weather, time of day), as a combination, the use of multiple modalities improves the overall probability or confidence that aircraft guidance system 405 will continually receive reliable data for navigation [0065])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman to include the teachings as taught by Farris with a reasonable expectation of success. Both are in the same field of endeavor of aeronautical navigation. Farris additionally teaches the benefits of “while EO images may be sufficient for landmark identification while flying in clear weather during the day, during nighttime, thermal imaging sensors may produce more useful imaging data than the EO sensors. Similarly, where Light Detection and Ranging (LiDAR) sensing may be degraded in rainy conditions, a longer wavelength imaging modality such as radar may produce more useful imaging data than LiDAR sensors. By processing image data of multiple modalities of terrain over which an aircraft is flying to ascertain the aircraft's location and direction of travel, the aircraft can continually receive reliable location data for navigation. Thus, aircraft can fly missions using autonomous navigation in low-visibility environments or in areas where Global Positioning System (GPS) signals are unavailable for navigation [Farris, 0015]”.
Hedman in view of Farris do not explicitly teach, however O’Leary teaches:
wherein the processor is further configured to present, during the mission, the predicted time sequenced and geo-sequenced images to a pilot of a vehicle in real mission time corresponding to the estimated mission time associated with the predicted time sequenced and geo-sequenced images (In some embodiments, external aircraft data 108 may include virtually generated data 120 of an outside of an aircraft wherein external aircraft data 108 is a computer-generated environment. “Virtually generated data” as described herein is data created through a computer simulation as opposed to real data captured from, for example, a video input device. In some cases, input device 104 may generate a virtually generated environment or receive a virtually generated environment from a computing device 112. Additionally, or alternatively, the virtually generated environment may be representative of a surrounding environment of an aircraft. In some embodiments, external aircraft data 108 may include previously recorded data such as video data recorded on a previous occasion. In some embodiment, aircraft data may include a combination of aircraft orientation data, inertial data and/or pilot control data combined with virtually generated data 120. In this embodiment, a virtually generated environment may be created by computing device wherein computing device may generate a virtual environment based on the real data collected above. In some embodiments, external aircraft data 108 may include a combination or real-time data and previously recorded data [0020]). to permit the pilot of the vehicle to navigate the vehicle during execution of the mission (One method of enabling the display methods described above may to provide multiple aircraft sensors that cover the entire sphere around the airplane. Images (data) from these sensors may be “stitched” together to form a single, spherical image that may be viewed from the inside. A selected portion of this view may be provided to device display 136. Building on the prior discussion of multiple viewpoints, more than one spherical image can be created. Different spherical views can be paired to provide stereoscopic views. Some spherical views may be created synthetically by interpolation or extrapolation from data from aircraft sensors. These synthetic views may be from a selected or variably selected viewpoint (spherical center). In addition to providing external aircraft data by input device 104 to device display 136 [0034]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris to include the teachings as taught by O’Leary with a reasonable expectation of success. Both are in the same field of endeavor of detecting external environments of an aircraft. O’Leary additionally teaches the benefits of “display device 136 may be placed in lieu or in addition to flight deck windshields. In this embodiment, a pilot may use display device 136 to navigate aircraft. In some embodiments, display device 136 may contain a transparent display, wherein pilot may use display device 136 both as a window and as a display. In some embodiments, display device 136 may provide a pilot with information surrounding the aircraft such that a pilot may make informed decisions during aerial flight [O’Leary, 0044]”.
Hedman in view of Farris and O’Leary does not explicitly teach, however Brockers teaches:
and include one or more of a predicted light level at the estimated mission time (When high-accuracy elevation and texture models of the terrain are available, full robustness to illumination could potentially be achieved by artificially rendering the map image in the query conditions. [0020]; alter the brightness of pixels in the map ortho-image/map image in order to derive descriptors that are adapted to the simulated illumination regime at the different times of day. [0199]), predicted shadow lines that correspond to the mission time, mission location, and platform orientation during the mission (the digital elevation map (DEM) may be used to render virtual shadows on the terrain for different times of day and use the rendered (virtual) shadows to alter the brightness of pixels in the map ortho-image/map image in order to derive descriptors that are adapted to the simulated illumination regime at the different times of day. [0199]), and predicted celestial views including the sun, moon, stars, and manmade objects in space from a specific viewing location, viewing orientation, and viewing time (the examiner is interpreting this limitation in the alternative which does not require it to be mapped.);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris and O’Leary to include the teachings as taught by Brockers with a reasonable expectation of success. Both are in the same field of endeavor of detecting external environments of an aircraft. Brockers teaches “The following description reviews existing research work addressing the problem of matching a query image against a map image. The query image is assumed to have been captured by a camera mounted on the robot one wishes to localize (e.g., MSH in an exemplary application). The map image is assumed to have been captured at a different time, potentially by a different camera (e.g., HiRISE). Both the query and map images are assumed to be in the visible spectrum, and to overlap over a portion of terrain with enough texture and enough illumination to provide the information for a unique match. The objective of this research area is to identify image descriptors invariant to the potentially-severe transformation between the query and the map: scale, viewpoint, illumination or even change in the terrain itself. [Brockers, 0013]”
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hedman et. al. (US 6,157,875), herein Hedman in view of Farris et. al. (US 2025/0251242), herein Farris, O’Leary et. al. (US 2025/0074595), herein O’Leary, and Brockers et. al. (US 2023/0360547), herein Brockers in further view of Wang et. al. (CN 112213244).
Regarding claim 8:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Wang teaches:
adjusting, during the mission, an image frame rate of the predicted time sequenced and geo-sequenced images to match the image frame rate of the images captured using a platform sensor (the visible light video and infrared video of the invention are based on the same fixed camera source but both frame rate and size are different, designing interpolation size matching method and frame rate matching method to make two video collected by the binocular camera are used in the same moving target segmentation algorithm [page 7]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Wang with a reasonable expectation of success. This is applying a known solution to achieve a predictable result which would be obvious to one having ordinary skill in the art.
Claim(s) 11-12 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hedman et. al. (US 6,157,875), herein Hedman in view of Farris et. al. (US 2025/0251242), herein Farris, O’Leary et. al. (US 2025/0074595), herein O’Leary, and Brockers et. al. (US 2023/0360547), herein Brockers in further view of Akiva et. al. (US 16,969,229), herein Akiva (from IDS).
Regarding claim 11:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Akiva teaches:
retrieving from the platform control system actual images obtained during the mission (The images 520 may include street level images depicting the known visual objects from road level view as is seen by a vehicle [col 17, lines 65-67]);
comparing the plurality of predicted time sequenced and geo-sequenced images to the actual images obtained during the mission (As shown at 404, the map enhancer 510 analyzes the images 520 to identify one or more visual objects, in particular road infrastructure objects. The map enhancer 510 may analyze the captured image(s) using one or more computer vision analyses as known in the art, for example, pattern recognition, visual classification (classifiers) and/or the like to identify the visual object(s) in the images 520. The visual classification functions may be implemented through one or more probabilistic models, for example, a neural network and/or the like. The visual classifiers may further use machine learning to train the classifiers. [col 18, lines 26-36]);
refining, training, or enhancing the model based on comparison of the plurality of predicted time sequenced and geo-sequenced images to the actual images obtained during the mission (As shown at 408, once identified, the visual objects become known visual objects. The map enhancer 510 may create and/or update the visual data record 212 with information relating to each of the known visual objects, in particular, a known geographical position (location) and visual information of the known visual object. [col 18, lines 57-62]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Akiva with a reasonable expectation of success. Akiva is in the same field of endeavor of creating maps for navigational purposes. Akiva teaches the benefit of “enhancing the positing of the vehicle and applying the enhanced positioning to the navigation system, accuracy of the positioning of the vehicle may be significantly increased. The increased positioning accuracy may apply to the geographical position of the vehicle in the real world and/or the relative positioning of the vehicle with respect to the known visual object(s). This may serve to determine more accurately the positioning of the vehicle with respect, for example, a road center, a turning point, a decision point and/or the like. [Akiva, col 2, lines 3-13]”.
Regarding claim 12:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Akiva teaches:
wherein the system is configured for land vehicle applications and the plurality of predicted time sequenced and geo-sequenced images and predicted mission data are from a ground-based perspective (The images 520 may include street level images depicting the known visual objects from road level view as is seen by a vehicle [col 17, lines 65-67]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Akiva with a reasonable expectation of success. Akiva is in the same field of endeavor of creating maps for navigational purposes. Akiva teaches the benefit of “enhancing the positing of the vehicle and applying the enhanced positioning to the navigation system, accuracy of the positioning of the vehicle may be significantly increased. The increased positioning accuracy may apply to the geographical position of the vehicle in the real world and/or the relative positioning of the vehicle with respect to the known visual object(s). This may serve to determine more accurately the positioning of the vehicle with respect, for example, a road center, a turning point, a decision point and/or the like. [Akiva, col 2, lines 3-13]”.
Regarding claim 15:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 10, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Akiva teaches:
retrieving from the platform control system actual images obtained during the mission (The images 520 may include street level images depicting the known visual objects from road level view as is seen by a vehicle [col 17, lines 65-67]);
comparing the plurality of predicted time sequenced and geo-sequenced images to the actual images obtained during the mission (As shown at 404, the map enhancer 510 analyzes the images 520 to identify one or more visual objects, in particular road infrastructure objects. The map enhancer 510 may analyze the captured image(s) using one or more computer vision analyses as known in the art, for example, pattern recognition, visual classification (classifiers) and/or the like to identify the visual object(s) in the images 520. The visual classification functions may be implemented through one or more probabilistic models, for example, a neural network and/or the like. The visual classifiers may further use machine learning to train the classifiers. [col 18, lines 26-36]);
refining, training, or enhancing the model based on comparison of the plurality of predicted time sequenced and geo-sequenced images to the actual images obtained during the mission (As shown at 408, once identified, the visual objects become known visual objects. The map enhancer 510 may create and/or update the visual data record 212 with information relating to each of the known visual objects, in particular, a known geographical position (location) and visual information of the known visual object. [col 18, lines 57-62]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Akiva with a reasonable expectation of success. Akiva is in the same field of endeavor of creating maps for navigational purposes. Akiva teaches the benefit of “enhancing the positing of the vehicle and applying the enhanced positioning to the navigation system, accuracy of the positioning of the vehicle may be significantly increased. The increased positioning accuracy may apply to the geographical position of the vehicle in the real world and/or the relative positioning of the vehicle with respect to the known visual object(s). This may serve to determine more accurately the positioning of the vehicle with respect, for example, a road center, a turning point, a decision point and/or the like. [Akiva, col 2, lines 3-13]”.
Regarding claim 16:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 10, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Akiva teaches:
wherein the system is configured for land vehicle applications and the plurality of predicted time sequenced and geo-sequenced images and predicted mission data are from a ground-based perspective (The images 520 may include street level images depicting the known visual objects from road level view as is seen by a vehicle [col 17, lines 65-67]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Akiva with a reasonable expectation of success. Akiva is in the same field of endeavor of creating maps for navigational purposes. Akiva teaches the benefit of “enhancing the positing of the vehicle and applying the enhanced positioning to the navigation system, accuracy of the positioning of the vehicle may be significantly increased. The increased positioning accuracy may apply to the geographical position of the vehicle in the real world and/or the relative positioning of the vehicle with respect to the known visual object(s). This may serve to determine more accurately the positioning of the vehicle with respect, for example, a road center, a turning point, a decision point and/or the like. [Akiva, col 2, lines 3-13]”.
Claim(s) 13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hedman et. al. (US 6,157,875), herein Hedman in view of Farris et. al. (US 2025/0251242), herein Farris, O’Leary et. al. (US 2025/0074595), herein O’Leary, and Brockers et. al. (US 2023/0360547), herein Brockers in further view of Rivers (US 11,328,155), herein Rivers (from IDS).
Regarding claim 13:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Rivers teaches:
wherein the system is configured for maritime applications (In the embodiment illustrated by FIG. 1B, mobile structure 101 is implemented as a motorized boat [col 14, lines 57-59]) and the plurality of predicted time sequenced and geo-sequenced images include shoreline silhouette image information can be predicted (Scene 200 includes features above waterline 205 illustrated in FIG. 2 and additionally includes mountains/land features 204, tree 202, vehicle 213, floating object 211a, surface 205c of body of water 205a, and deck 106b (e.g., of mobile structure/boat 101 in FIG. 1B)… Additionally or alternatively, contour lines 332 may be rendered by the controller in portion 334 above the waterline. The contour lines 332 above the waterline 205 may distinguish elevation, relative distances, and various other characteristics of terrestrial features. [col 24, lines 15-39]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Rivers with a reasonable expectation of success. Rivers teaches the benefit of “there is a need in the art for a methodology to combine navigational sensor data from disparate sensors to provide an intuitive, meaningful, and relatively full representation of the environment, particularly in the context of aiding in the navigation of a mobile structure. [Rivers, col 2, lines 33-38]”.
Regarding claim 17:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 10, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Rivers teaches:
wherein the system is configured for maritime applications (In the embodiment illustrated by FIG. 1B, mobile structure 101 is implemented as a motorized boat [col 14, lines 57-59]) and the plurality of predicted time sequenced and geo-sequenced images include shoreline silhouette image information can be predicted (Scene 200 includes features above waterline 205 illustrated in FIG. 2 and additionally includes mountains/land features 204, tree 202, vehicle 213, floating object 211a, surface 205c of body of water 205a, and deck 106b (e.g., of mobile structure/boat 101 in FIG. 1B)… Additionally or alternatively, contour lines 332 may be rendered by the controller in portion 334 above the waterline. The contour lines 332 above the waterline 205 may distinguish elevation, relative distances, and various other characteristics of terrestrial features. [col 24, lines 15-39]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Rivers with a reasonable expectation of success. Rivers teaches the benefit of “there is a need in the art for a methodology to combine navigational sensor data from disparate sensors to provide an intuitive, meaningful, and relatively full representation of the environment, particularly in the context of aiding in the navigation of a mobile structure. [Rivers, col 2, lines 33-38]”.
Claim(s) 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hedman et. al. (US 6,157,875), herein Hedman in view of Farris et. al. (US 2025/0251242), herein Farris, O’Leary et. al. (US 2025/0074595), herein O’Leary, and Brockers et. al. (US 2023/0360547), herein Brockers in further view of Chen et. al. (US 11,151,447), herein Chen.
Regarding claim 14:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 1, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Chen teaches:
wherein the plurality of predicted time sequenced and geo-sequenced images simulate a lens structure of sensors on the platform (The aberration module 320 may include functionality to transform datasets (e.g., the dataset 106) to simulate or recreate various lens aberrations that may be associated with a camera lens [col 11, lines 23-26]), sensor wavelength sensitives (It may be understood that if a sensor dataset includes data corresponding to wavelengths of light (or signals) from a first frequency to a second frequency (e.g., ranging from infrared to ultraviolet) the color filter array module 316 may generate any number of datasets corresponding to any range of wavelengths included in the dataset. [col 11, lines 5-10]), and image view angles used on the platform (angle of orientation [col 10, line 31]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Chen with a reasonable expectation of success. Chen teaches the benefit of “methods, apparatuses, and systems for network training and testing for evaluating hardware characteristics and for selecting hardware. For example, the methods described herein can be used to evaluate how changing characteristics of a single sensor or groups of sensors may affect a performance outcome of the system. The methods described herein may operate on real data obtained from one or more sensors, real data that has been modified to replicate or simulate various hardware configurations or operating conditions, synthetic data that has been generated and annotated by a computer, or any combination therein. [Chen, col 1, line 60 – col 2, line 4]”.
Regarding claim 18:
Hedman in view of Farris, O’Leary, and Brockers teaches all the limitations of claim 10, upon which this claim is dependent.
Hedman in view of Farris, O’Leary, and Brockers does not explicitly teach, however Chen teaches:
wherein the plurality of predicted time sequenced and geo-sequenced images simulate a lens structure of sensors on the platform (The aberration module 320 may include functionality to transform datasets (e.g., the dataset 106) to simulate or recreate various lens aberrations that may be associated with a camera lens [col 11, lines 23-26]), sensor wavelength sensitives (It may be understood that if a sensor dataset includes data corresponding to wavelengths of light (or signals) from a first frequency to a second frequency (e.g., ranging from infrared to ultraviolet) the color filter array module 316 may generate any number of datasets corresponding to any range of wavelengths included in the dataset. [col 11, lines 5-10]), and image view angles used on the platform (angle of orientation [col 10, line 31]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Hedman in view of Farris, O’Leary, and Brockers to include the teachings as taught by Chen with a reasonable expectation of success. Chen teaches the benefit of “methods, apparatuses, and systems for network training and testing for evaluating hardware characteristics and for selecting hardware. For example, the methods described herein can be used to evaluate how changing characteristics of a single sensor or groups of sensors may affect a performance outcome of the system. The methods described herein may operate on real data obtained from one or more sensors, real data that has been modified to replicate or simulate various hardware configurations or operating conditions, synthetic data that has been generated and annotated by a computer, or any combination therein. [Chen, col 1, line 60 – col 2, line 4]”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hulet (US 9,098,939) discloses A method and system for generating light maps for use with an out the window, or a night vision goggles display utilizes inputs from a variety of different geographical and structural data sources. These data sources are then used to determine the locations of structures, roads, and areas of interest within a selected land area. Realistic light maps can then be created quickly and automatically.
Aalund (US 10,124,893) discloses Techniques are described for assessing the health of an unmanned vehicle such as an unmanned aerial vehicle (UAV). In some embodiments, sensors corresponding to subsystems of the UAV may be utilized to assess the health of a particular subsystem. Predictive models may be stored within memory of the UAV to enable such assessments to be performed at the UAV (e.g., during performance of a mission). Sensor data collected from sensors on the UAV may be provided as input for a predictive model associated with a particular subsystem. The predictive model may output a failure prediction indicating a likelihood, and in some cases, a time by which failure of the subsystem is predicted to occur given the sensor data. In some embodiments, one or more corrective actions may be identified and triggered based, at least in part, on the failure prediction.
Eraker (US 9,836,885) discloses Under an embodiment of the invention, an image capturing and processing system creates 3D image-based rendering (IBR) for real estate. The system provides image-based rendering of real property, the computer system including a user interface for visually presenting an image-based rendering of a real property to a user; and a processor to obtain two or more photorealistic viewpoints from ground truth image data capture locations; combine and process two or more instances of ground truth image data to create a plurality of synthesized viewpoints; and visually present a viewpoint in a virtual model of the real property on the user interface, the virtual model including photorealistic viewpoints and synthesized viewpoints.
Johnson (US 9,652,888) discloses A system and method for providing visual depictions of a predictive weather forecast for in-route vehicle trajectory planning. The method includes displaying weather information on a graphical display, displaying vehicle position information on the graphical display, selecting a predictive interval, displaying predictive weather information for the predictive interval on the graphical display, and displaying predictive vehicle position information for the predictive interval on the graphical display, such that the predictive vehicle position information is displayed relative to the predictive weather information, for in-route trajectory planning.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott R Jagolinzer whose telephone number is (571)272-4180. The examiner can normally be reached M-Th 8AM - 4PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights