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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/25/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Preliminary Amendment
Applicant submitted a preliminary amendment on 10/23/2024. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Claim Objections
Claims 3 and 13 are objected to because of the following informalities: Claims 3 and 13 do not need a first reference definition for “Augmented Reality (AR)” as this is already defined in claim 1.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-4, 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan (U.S. Patent Pub. No. 2021/0053229) in view of Sibley (U.S. Patent Pub. No. 2022/0117218).
Regarding Claim 1, Yuan teaches a computer implemented method for scouting of plants in an agricultural field comprising (¶5 the method may further include deploying one or more scout robots to travel among the plurality of plants and capture sensor data, wherein the sensor data is analyzed to designate each of the plurality of plants as a target for one of the plurality of agricultural tasks:)
in response to an input to initiate a continuous process for scouting of plants, obtaining image data (¶38 These robots may provide sensor data they capture while scouting to agricultural task system 102, which may store this data in database(s) 118; ¶40 vision data analysis engine 112 may analyze 2D and/or 3D vision data to perform object recognition and/or tracking, which can be used, for instance, to count and/or track plant-parts-of-interest such as fruits, flowers, nuts, berries, and/or other components that may have commercial or other value) from one or more sensors of a device (¶35 In the agricultural context these data may be obtained manually by human workers carrying cameras or other sensors, or automatically using one or more robots 108.sub.1-N equipped with 2D/3D vision sensors.)
analyzing one or more input images from the image data, generating a tracking grid of two dimensional (2D) reference points that are projected onto a ground plane to create a matching set of three dimensional (3D) anchor points (¶40 vision data analysis engine 112 may analyze 2D and/or 3D vision data to perform object recognition and/or tracking; ¶43 in some implementations in which robots are equipped with less expensive/complex 2D vision sensors, 3D data may be generated from multiple 2D digital images using techniques such as “structure from motion,” or “SFM” processing. For example, a sequence of 2D digital images of plant(s) may be captured, e.g., by a robot moving alongside the plant(s). This sequence of 2D digital images may be analyzed using SFM processing to generate “synthetic” 3D data,) and their positions in a 3D space of the agricultural field using an augmented reality (AR) framework (¶27 In some implementations, the plurality of agricultural tasks may be presented to a human operator visually, e.g., on a computer display, tablet display, or even as part of a virtual reality (“VR”) or augmented reality (“AR”) display;)
providing the one or more input images, tracking grid, and the positions in the 3D space of the agricultural field to a machine learning (ML) model having a convolutional neural network (CNN); and (¶40 For object recognition, vision data analysis engine 112 may utilize a variety of different techniques, including but not limited to… trained machine learning models (e.g., convolutional neural networks); ¶43 This synthetic 3D data may then be used to perform various vision data analysis tasks mentioned previously, such as object tracking, object recognition, fruit measuring and counting, etc.)
Yuan does not explicitly disclose generating inference results with the ML model including an array of detected objects and selecting most likely inferred plant locations and classifications for the array of detected objects.
Sibley is in the same field of art of image analysis. Further, Sibley teaches generating inference results with the ML model including an array of detected objects and selecting most likely inferred plant locations and classifications for the array of detected objects (¶143 The compute module 424 can include computing devices and components configured to receive and process image data from image sensors or other components. In this example, the compute module 424 can process images, compare images, identify, locate, and classify features in the images including classification of objects such as agricultural objects, landmarks, or scenes, as well as identify location, pose estimation, or both, of an object in the real world based on the calculations and determinations generated by compute module 424 on the images and other sensor data fused with the image data.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Yuan by explicitly determining the location and classification of the objects that is taught by Sibley; thus, one of ordinary skilled in the art would be motivated to combine the references for improving crop yield while reducing unwanted plants grown on a given farm or cultivated land (Sibley ¶4).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Yuan in view of Sibley discloses the computer implemented method of claim 1, further comprising: using object tracking anchor points to generate corrected inference result positions to compensate for movement of the device during image capture (Sibley, ¶179 The system would be able to calculate the amount of movement in space as it tracks the same point in space with keypoint matching or other techniques to perform VSLAM.)
The reasons for combining Yuan and Sibley are similar to that stated in the rejection of claim 1. In addition, this same reasoning is pertinent and applicable to the rejection of claim 3 below.
Regarding Claim 3, Yuan in view of Sibley discloses the computer implemented method of claim 2, further comprising: projecting corrected inference result positions into the 3D space using the augmented reality (AR) framework (Sibley, ¶139 Each object previously detected, indexed, and stored can be displayed to the user in real time via augmented reality or mixed reality, as the same objects in the real-world are detected by the electronic devices in real time; Yuan ¶27 also teaches displaying the information in AR.)
Regarding Claim 4, Yuan in view of Sibley discloses the computer implemented method of claim 3, further comprising:
creating plant markers in the 3D space based on locations of the corrected inference result positions; and separating the plant markers into targeted plants to be identified and other non-targeted plants (Yuan, ¶46 Based on this analysis, vision data analysis engine 112 may designate one or more individual plants of the plurality of plants as a target for performance of an agricultural task.)
Regarding Claim 10, Yuan in view of Sibley discloses the computer implemented method of claim 1, wherein the device comprises an edge device to determine crop emergence levels and spacing between neighboring plants in a row (Yuan, ¶40 For object recognition, vision data analysis engine 112 may utilize a variety of different techniques, including but not limited to appearance-based methods such as edge matching, divide-and-conquer, gradient matching, greyscale matching, histograms, and/or feature-based methods.)
Regarding claim 11, claim 11 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Yuan further teaching on: A computing device comprising (¶92 FIG. 9 is a block diagram of an example computing device 910 that may optionally be utilized to perform one or more aspects of techniques:)
a display device (¶94 User interface output devices 920 may include a display subsystem) for displaying a user interface (¶93 User interface input devices 922 may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and/or other types of input devices) to show plants that reside in an agricultural field being enhanced with computer-generated perceptual information for scouting of the plants; and (¶59 the human operator may be presented with the GUI 362 depicted in FIG. 3C. GUI 362 includes a window 364 that depicts vision data captured by a vision sensor of a robot assigned the agricultural task (00002) underlying second operable element 350.sub.2 in FIG. 3B)
a processor coupled to the display device (¶92 Computing device 910 typically includes at least one processor 914 which communicates with a number of peripheral devices via bus subsystem 912,)
Claim 12 recites limitations similar to claim 2 and is rejected under the same rationale and reasoning.
Claim 13 recites limitations similar to claim 3 and is rejected under the same rationale and reasoning.
Claim 14 recites limitations similar to claim 4 and is rejected under the same rationale and reasoning.
Allowable Subject Matter
Claims 5-9 and 15-20 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.
Regarding claims 5 and 15, no prior art teaches inputting locations of the targeted plants into a row detection and tracking algorithm to detect linear rows of the targeted plants in the agricultural field and group the locations of the targeted plants into the linear rows;
determining a predicted row direction vector and a center location that is equidistant between adjacent rows based on a moving window of row tracking results;
using the predicted row direction vector to project an estimated center location between the adjacent rows;
converting locations of the targeted plants to be relative to the predicted center location and projected onto an axis orthogonal to the predicted row direction vector over a range of angles; and
analyze each angle's projection using a Kernel Density estimate to determine a direction and spacing of each adjacent row.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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