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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 1 is directed to: “A computer implemented method for forming a digital surface model of treetops” with the steps of obtaining, detecting, determining and forming which are nothing more than software instructions. Software instructions are non-statutory under 35 U.S.C. 101.
Claims 2-9 depend from claim 1 and contain further steps, for example claim 2 includes the steps of obtaining, detecting and determining, therefore claims 2-10 are rejected under the same rationale.
Claim 10 depends from claim 1 but has not been rejected under 101, because it contains a computer program product comprising a non-transitory crm.
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 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.
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.
Claim(s) 1, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong et. al., “A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography”, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 10, OCTOBER 2014 (from IDS submitted on 4/25/24) in view of Pimpler, “Generating Digital Surface Models from Lidar Data in ArcGIS Pro”, July 26, 2020, URL: https://geospatialtraining.com/generating-digital-surface-models-from-lidar-data-in-arcgis-pro/%23:~:text=The%20workflow%20is%20as%20follows:%20*%20Populate,cloud%20data%20layers%20into%20raster%20surface%20models.
Regarding claim 1, Tong teaches:
1. (Currently Amended) A computer implemented method for forming a digital surface model of treetops, the method (100) comprises the steps of:
obtaining (110) at least two images from a flying platform (TONG: pg. 4154, left col. line 53-right col. line 2);
detecting (130) treetops in each image (TONG: pg. 4154, left col.: lines 15-18 and line 53-right col. line 2);
determining (140) a treetop position for each matching treetop detected in plurality of said at least two images (TONG: pg. 4154, left col., lines.: 4-7, lines 15-18, line 53-right col. line 2; note: treetop position is being interpreted as the tree height); and
forming (150) the normalized digital surface model (i.e., nDSM) based on said at least one determined treetop position (TONG: fig. 1 (upper right portion); pg. 4154, left col.: lines 4-7, 16-18 and line 45-right col. line 13).
Tong doesn’t teach however the analogous prior art Pimpler teaches:
forming (150) the digital surface model (PIMPLER: pg. 1, first par.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine forming (150) the digital surface model as shown in Pimpler with Tong for the benefit of providing convenience for the user provided by ArcGIS Pro that enables the creation of raster surface models based on point cloud data [pg. 1, first par.].
Regarding claim 10, Tong teaches:
10. (Currently Amended) A computer program product comprising a non-transitory computer-readable storage medium (412) having thereon a computer program comprising program instructions, the computer program being loadable into a processor (411) and configured to cause the processor (411) to perform the method (100) for forming a digital surface model of treetops according to claim 1 (TONG: pg. 4154 left col. line 53-right col. line 4; POSITA would recognize that integrating LIDAR with high-resolution imagery for obtaining a classification map includes a computer having a crm with instructions and processor).
Regarding claim 11, Tong teaches:
11. (Currently Amended) A system for forming a digital surface model of treetops, the system (300) comprising:
a set of sensors (310) arranged to capture images (TONG: pg. 4154, left col., lines 53-56; POSITA would recognize that integrated LIDAR and high-resolution aerial imagery comprises sensors.),
and a computer (320) configured to communicate with the set of sensors (310), wherein the computer (320) is further configured to (TONG: pg. 4154, left col., line 53-right col. line 4; POSITA would recognize that integrated LIDAR and high-resolution aerial imagery for classification uses a computer.);
obtain at least two images from the set of sensors (310) (TONG: pg. 4154, left col., line 53-right col. line 2);
detect treetops in each image (TONG: pg. 4154, left col.: lines 15-18, line 53-right col. line 2);
determine a treetop position for each matching treetop detected in a plurality of said at least two images (TONG: pg. 4154, left col.: lines 4-7, lines 15-18, line 53-right col. line 2); and
form the normalized digital surface model (i.e., nDSM) based on said at least one determined treetop position (TONG: fig. 1 (upper right portion); pg. 4154, left col.: lines 4-7, 16-18 and line 45-right col. line 13).
Tong doesn’t teach however the analogous prior art Pimpler (with the same motivation from claim 1) teaches:
forming (150) the digital surface model (PIMPLER: pg. 1, first par.).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Pimpler in view of Wu et. al., “Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests”, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; wxqhero@iCloud.com (X.W.); shenxin1903@gmail.com (X.S.); gbwang@njfu.edu.cn (G.W.); cao@njfu.edu.cn (F.C.), Received: 18 March 2019; Accepted: 11 April 2019; Published: 14 April 2019.
Regarding claim 5, the previous combination of Tong and Pimpler don’t teach however the analogous prior art Wu teaches:
5. (Currently Amended) The method according to claim 1, wherein obtaining (110) said at least two images comprises capturing images in the visible spectrum, infrared spectrum, and/or ultraviolet spectrum (WU: pg. 3 lines 2-8; note: CCD camera captures images in the visible spectrum).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein obtaining (110) said at least two images comprises capturing images in the visible spectrum, infrared spectrum, and/or ultraviolet spectrum as shown in Wu with the previous combination for the benefit of improving the canopy cover (i.e., CC) detection accuracy [pg. 3 lines 9-11].
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Pimpler in view of KAARTINEN, “An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning”, Remote Sens. 2012, 4, 950-974; doi:10.3390/rs4040950.
Regarding claim 8, the previous combination of Tong and Pimpler don’t teach however the analogous prior art KAARTINEN teaches:
8. (Currently Amended) The method according to claim 1, wherein determining (140) the treetop position for each matching treetop detected in a plurality of said at least two images comprises detecting and eliminating treetop position outliers (KAARTINEN: pg. 970 lines 3-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein determining (140) the treetop position for each matching treetop detected in a plurality of said at least two images comprises detecting and eliminating treetop position outliers as shown in KAARTINEN with the previous combination for the benefit of improving the tree height accuracy [pg. 970 lines 3-6].
Allowable Subject Matter
Claims 2-4, 6-7 and 9 would be objected to (except for the 101 rejection) as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 12-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 2-4, 6-7, 9 and 12-14 the prior art doesn’t teach:
2. (Currently Amended) The method according to claim 1, further omprising:obtaining (120) a neural network (200,201) trained to detect treetops in images, wherein detecting (130) treetops in each image comprises utilizing each image as an input image (202) for said neural network (200,201), determining for each image a neural network output, and, if said output comprises at least one detected treetop, determining for each detected treetop a feature vector based on intermediate values calculated in the neural network (200,201) while generating said output; and based on said determined neural network outputs and corresponding at least one feature vector.
3. (Currently Amended) The method according to claim 2, wherein: determining (140) the treetop position comprises comparing feature vectors corresponding to said determined neural network outputs, and
4. (Currently Amended) The method according to claim 3, wherein comparing the feature vectors is based on utilizing Euclidian distance, and/or cosine similarity, and/or scalar product, and/or phase correlation.
6. (Currently Amended) The method according to claim 1
7. (Currently Amended) The method according to claim 1
9. (Currently Amended) The method according to claim 1 further comprising:updated version of said obtained predetermined digital surface model based on the formed digital surface model.
12. (Currently Amended) The system according to claim 11, wherein the computer (320) is further configured
13. (Currently Amended) The system according to claim 11 configured configured
14. (New) The system according to claim 12, further comprising:a memory storage (330) comprising at least one predetermined digital surface model, wherein the computer (320) is configured to communicate with the memory storage (330), and wherein the computer (320) is further configured to: obtain a predetermined digital surface model from the memory storage (330); and determine a position and/or a pose based on the formed digital surface model and the obtained predetermined digital surface model, and/or form an updated version of the obtained predetermined digital surface model based on the formed digital surface model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HENRY (US2021/0263515A1) discloses in some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model; ABEYWARDENA (US2023/0244250A1) discloses a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature; BACHRACH (US2019/0378423A1) discloses a technique for user interaction with an autonomous unmanned aerial vehicle (UAV) is described. In an example embodiment, perception inputs from one or more sensor devices are processed to build a shared virtual environment that is representative of a physical environment. The sensor devices used to generate perception inputs can include image capture devices onboard an autonomous aerial vehicle that is in flight through the physical environment. The shared virtual environment can provide a continually updated representation of the physical environment which is accessible to multiple network-connected devices, including multiple UAVs and multiple mobile computing devices. The shared virtual environment can be used, for example, to display visual augmentations at network-connected user devices and guide autonomous navigation by the UAV; ZHANG CN110741409A discloses a image data processing method, unmanned aerial vehicle, a system and a storage medium, wherein the method comprises: determining the first elevation window set on the initial digital surface model, a first elevation window set comprises a first elevation window, the first height window comprises vacancy point elevation, the first elevation of height vacancy point is the elevation estimation value; in the initial digital surface model area with high vacancy point other than determining second altitude window set, the second elevation window set comprises a second elevation window; performing optimization calculation to obtain the elevation of each elevation point compensation value of each elevation value for each second altitude window covering of each elevation and matching of the first elevation window covering based on minimizing a predetermined cost algorithm; supplementary value update elevation-deficient in the elevation estimation point according to the height, to obtain the updated digital surface model. This embodiment can fills the height gap area, so as to better obtain digital surface model; YAJIMA (WO2021/020570A1) discloses an unmanned aerial vehicle equipped with a LiDAR sensor is flown to perform forest measurement on a forest. A rotary shaft or a swing shaft of the LiDAR sensor is mounted on the unmanned aerial vehicle to face the advancing direction of the unmanned aerial vehicle body. This method for measuring a forest involves: measuring the surrounding space by using the LiDAR sensor to emit laser pulses while changing the emission direction at a predetermined angular pitch; and performing forest measurement on the forest by using the LiDAR sensor while flying an unmanned aerial vehicle such that the relative values of α・L with respect to V/(f・N) are within a predetermined range where L is the distance to the forest, V is the flight speed of an unmanned aerial vehicle, N is the number of laser pulses emitted simultaneously in the same angular direction, f is the frequency of the laser pulses emitted in the same angular direction, and α is a predetermined angular pitch.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Fri: 2pm-10pm.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612