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 .
Response to Amendment
Applicant's amendment filed 7/2/2026 has been received and entered into the record. As a result, claims 1, 12, and 22 have been amended. Therefore, claims 1-22 are presented for examination.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 6, 7, 9-11, 12, 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Redden [US Pub. 2013/0238201] in view of Guo et al. [US Pub. 2019/0228224] ("Guo").
With regard to claim 1, Redden teaches a method for autonomous crop thinning ("method and apparatus for automated plant necrosis [title]"), comprising:
receiving, by a processor, one or more images of a crop field containing crops ("Identifying individual plants S100 functions to distinguish individual plants within an image of contiguous, close-growing plants [par. 0019]" and "processor [par. 0050]");
processing, by the processor, the one or more images using a machine learning model to identify one or more individual crops; ("Classifying the points of interest as plant centers or non-plant centers S130 functions to determine individual plants within the image [par. 0024]" and "the points of interest can be classified using machine learning algorithms or artificial intelligence [par. 0025]")
determining, by the processor, a location ("the virtual map functions to identify the relative position of each plant within the field, and can also function to identify the relative position of the crop thinning system relative to each identified plant [par. 0030]") and a parameter of each of the one or more identified crops ("The cultivation parameters can include the intra-row distance between adjacent plants (e.g. distance between plants in the same row), inter-row distance between adjacent plants (e.g. distance between plants in different rows), yield (e.g. number of plants per unit area or plant density), uniformity in plant size (e.g. uniformity between the retained sub-regions and/or foreground region area), uniformity in plant shape (e.g. uniformity in retained sub-region and/or foreground region perimeter), uniformity in plant appearance, plant size and/or shape similarity to a given size and/or shape, the confidence or probability that the sub-region or region is a plant, the practicality of keeping the respective plant, uniformity or conformance of measured plant health indicators to a plant health indicator threshold [par. 0032]");
generating a crop boundary around each identified crop based on the location, the parameter, or both of each of the one or more identified crops ("Segmenting the foreground region into sub-regions S140 functions to identify the image area associated with each individual plant and to indirectly identify the area occupied by the respective plant within the crop row [par. 0028]" and "Each sub-region or region encompassing a single point of interest classified as a plant center is preferably treated as a plant [par. 0030]" and [fig. 5]); and
selecting one or more target crops for removal based on their respective parameters, locations relative to the crop boundaries, or both ("Selecting sub-regions from the image preferably includes determining an optimal pattern of retained plants that best meets a set of cultivation parameters for the section of the plant field represented by the image, based on the parameters of each sub-region, and selecting the sub-regions within the image that correspond to the retained plants to obtain the optimal retained plant pattern. The cultivation parameters can include the intra-row distance between adjacent plants (e.g. distance between plants in the same row), inter-row distance between adjacent plants (e.g. distance between plants in different rows), yield (e.g. number of plants per unit area or plant density), uniformity in plant size (e.g. uniformity between the retained sub-regions and/or foreground region area), uniformity in plant shape (e.g. uniformity in retained sub-region and/or foreground region perimeter), uniformity in plant appearance, plant size and/or shape similarity to a given size and/or shape, the confidence or probability that the sub-region or region is a plant, the practicality of keeping the respective plant, uniformity or conformance of measured plant health indicators to a plant health indicator threshold, or any other suitable parameter that affects the plant yield that can be determined from the information extracted from the image [par. 0032]").
While Redden discloses variations when classifying the points of interest and defining sub-regions, Redden also discloses where modifications and changes can be made without departing from the scope of the invention [par. 0052]. It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have combined various methods taught by Redden, e.g., classifying points of interest and defining regions using machine learning or AI, because in having done so would have predictably used prior art elements according to their established functions to yield predictable results.
Although Redden teaches identifying a foreground region where multiple plants are identified [par. 0021], Redden does not explicitly teach wherein at least one crop boundary is generated around multiple identified crops.
In an analogous art (crop images), Gou teaches wherein at least one crop boundary is generated around multiple identified crops ("assigning a unique identifier to each crop field (or crop sub-field) associated with a detected crop boundary, providing crop fields (or sub-fields) search capabilities, and/or the like [par. 0024]" and "post-detection activities may further include overlaying indications of identified crop fields/sub-fields and crop types on the original images so as to visually present the crop type classification results, and otherwise visually augmenting the original images with detected information[par. 0059]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Gou's teachings of a boundary around multiple crops, with the teachings of Gou, for the benefit visually augmenting the displayed image.
Note: claim is presented in the alternative.
With regard to claim 2, the combination above teaches the method of claim 1. Redden in the combination further teaches wherein the method further comprises directing an autonomous vehicle equipped with a targeting system capable of removing the selected target crops ("The detection mechanism 200 is preferably coupled to the system 100 a known distance away from the elimination mechanism 300 … The system 100 is preferably configured to transiently couple (e.g. removably couple) to a drive mechanism, such as a tractor [par. 0042]" and "an autonomous plant-thinning vehicle, or any other suitable machine or vehicle [par 0051]").
With regard to claim 4, the combination above teaches the method of claim 2. Redden in the combination teaches the method further comprising removing the selected target crops with the targeting system ("Selecting plants for retention S200 functions to determine the plants to retain and the plants to remove from the field segment represented by the image [par. 0031]").
With regard to claim 6, the combination above teaches the method of claim 2. Redden in the combination teaches wherein the method further comprises updating the crop boundaries based on real-time feedback from the targeting system ("As shown in FIG. 7B, the virtual map is preferably dynamically updated S162 and/or expanded with each successive image [par. 0030]").
With regard to claim 7, the combination above teaches the method of claim 1. Redden in the combination further teaches wherein selection of target crops for removal is based at least on a predetermined crop spacing within the crop field ("Selecting sub-regions from the image preferably includes determining an optimal pattern of retained plants that best meets a set of cultivation parameters … The cultivation parameters can include the intra-row distance between adjacent plants (e.g. distance between plants in the same row), inter-row distance between adjacent plants (e.g. distance between plants in different rows), yield (e.g. number of plants per unit area or plant density) [par. 0032]").
With regard to claim 9, the combination above teaches the method of claim 1. Redden in the combination further teaches wherein the parameter of each identified crop includes at least one of health, size, or growth stage ("The cultivation parameters can include … uniformity in plant appearance, plant size and/or shape similarity to a given size and/or shape, the confidence or probability that the sub-region or region is a plant, the practicality of keeping the respective plant, uniformity or conformance of measured plant health indicators to a plant health indicator threshold, or any other suitable parameter that affects the plant yield that can be determined from the information extracted from the image [par. 0032]"").
Note: claim is presented in the alternative.
With regard to claim 10, the combination above teaches the method of claim 1. Redden in the combination further teaches wherein the crop boundary is designated as a geometric shape selected from the group consisting of a rectangle, an ellipse, and a polygon that closely matches a contour of the crop ("each sub-region can be defined by a rectangle or any other suitable polygon or shape. When a polygon is used, the centerline preferably intersects the polygon at a corner, and the polygon preferably intersects at least one point on the foreground region edge proximal the respective point of interest, more preferably at least a first, second, and third point on the foreground region edge proximal the respective point of interest, wherein the first edge point preferably opposes the second edge point across the respective point of interest, and the third edge point preferably opposes a point on the centerline across the respective point of interest. When the sub-regions are defined by ovals, the ovals preferably intersect the centerpoint and as many points on the foreground edge proximal the respective point of interest as possible [par. 0028]").
Note: claim is presented in the alternative.
With regard to claim 11, the combination above teaches the method of claim 1. Redden in the combination further teaches wherein determining the location of the individual crop may comprise generating a virtual representation of a region around the individual crop ("Identifying individual plants can additionally include creating a virtual map of plants S160, which functions to map the sub-regions or regions indicative of plants that are extracted from the image to a virtual map of the plant field. As shown in FIG. 7A, the virtual map functions to identify the relative position of each plant within the field [par. 0030]").
With regard to claim 12, the combination above teaches claim 1 above. Claim 12 recites limitations having the same scope as those pertaining to claim 1; therefore, claim 12 is rejected along the same grounds as claim 1.
Claim 12 differs from claim 1 where claim 12 recites the additional elements (which Redden teaches) of: a memory comprising instructions stored thereon, which, when executed by the processor causes the system to perform operations ("processor [par. 0050]" and "a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components [par. 0051]").
With regard to claim 15, the combination above teaches claim 2 above. Claim 15 recites limitations having the same scope as those pertaining to claim 2; therefore, claim 15 is rejected along the same grounds as claim 2.
With regard to claim 16, the combination above teaches the system of claim 15. Redden in the combination further teaches wherein the autonomous vehicle includes a detection system that dynamically updates a virtual representation of the crop field as the vehicle moves through the field ("As shown in FIG. 7B, the virtual map is preferably dynamically updated S162 and/or expanded with each successive image [par. 0030]") and "The system 100 functions to image sections of a field of plants, more preferably sections of a crop row 10, determine whether to remove or retain each plant within each imaged segment, and remove the plants marked for removal as the system 100 moves along the crop row 10 [par. 0041]").
With regard to claim 17, the combination above teaches the system of claim 15. Redden in the combination further teaches wherein the autonomous vehicle collects environmental data from the crop field and adjusts the removal operations based on the collected data ("the processor adjusts the emitted wavelengths to supplement the radiation provided by the environment (e.g. sunlight) when certain wavelengths are detected to be below a predetermined threshold, increases the intensity when an image parameter (e.g. contrast) needs to be adjusted as determined through the confidence levels of the image analysis, or otherwise adjusts the radiation emitted by the emitter [par. 0029]").
With regard to claims 18, 19, 20, and 21, the combination above teaches claims 7, 9, 10, and 11 above. Claims 18, 19, 20 and 21 recite limitations having the same scopes as those pertaining to claims 7, 9, 10, and 11, respectively; therefore, claims 18, 19, 20, and 21 are rejected along the same grounds as claims 7, 9, 10, and 11.
With regard to claim 22, the combination above teaches claim 1 above. Claim 22 recites limitations having the same scope as those pertaining to claim 1; therefore, claim 22 is rejected along the same grounds as claim 1.
Claim 22 differs from claim 1 where claim 22 recites the additional elements (which Redden teaches) of: a non-transitory computer readable medium containing computer executable instructions that, when executed by a computer hardware arrangement, cause the computer hardware arrangement to perform procedures ("processor [par. 0050]" and "a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components [par. 0051]").
Claims 3, 5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Redden in view of Guo further in view of Schwarz [US Pub. 2011/0211733].
With regard to claim 3, the combination of Redden and Guo teaches the method of claim 2. Redden in the combination further teaches wherein the targeting system comprises adevice capable ofremoving the selected target crops ("Removing the plants with the elimination mechanism preferably includes operating the elimination mechanism in the plant removal mode at the instructed time point or location [par. 0040]").
Although Redden teaches various devices to remove the selected target crops ("Operating the crop thinning system in plant removal mode can include spraying a removal fluid at a predetermined concentration, operating a cutting mechanism (e.g. a hoe or a scythe), operating an uprooting mechanism, generating directional heat, generating directional electricity, or include any other suitable means of facilitating plant necrosis [par. 0040]"),
Redden does not explicitly teach a laser irradiating the target crops.
In an analogous art (crop thinning), Schwarz teaches a laser irradiating crops ("When the imaging system detects a weed the laser 30 is directed to be positioned substantially above the weed by operation of the "X-Y" table and then the laser 30 is operated to substantially destroy the weed [par. 0132]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have substituted the laser device as taught by Schwarz, for the removing device taught by Redden, since the laser device incorporated into Redden's system would operate the same way in Schwarz' system and would predictably allow for target crops to be removed.
With regard to claim 5, the combination of Redden and Guo teaches the method of claim 4. Redden in the combination further teaches wherein removing the selected target crops comprisesremoving the target crops with adevice ("Removing the plants with the elimination mechanism preferably includes operating the elimination mechanism in the plant removal mode at the instructed time point or location [par. 0040]").
Although Redden teaches various devices to remove the selected target crops ("Operating the crop thinning system in plant removal mode can include spraying a removal fluid at a predetermined concentration, operating a cutting mechanism (e.g. a hoe or a scythe), operating an uprooting mechanism, generating directional heat, generating directional electricity, or include any other suitable means of facilitating plant necrosis [par. 0040]"),
Redden does not explicitly teach a laser irradiating the target crops.
In an analogous art (crop thinning), Schwarz teaches a laser irradiating crops ("When the imaging system detects a weed the laser 30 is directed to be positioned substantially above the weed by operation of the "X-Y" table and then the laser 30 is operated to substantially destroy the weed [par. 0132]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have substituted the laser device as taught by Schwarz, for the removing device taught by Redden, since the laser device incorporated into Redden's system would operate the same way in Schwarz' system and would predictably allow for target crops to be removed.
With regard to claim 14, the combination above teaches claim 3 above. Claim 14 recites limitations having the same scope as those pertaining to claim 3; therefore, claim 14 is rejected along the same grounds as claim 3.
Claims 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Redden in view of Guo further in view of Grant et al. [US Pub. 2021/0092891] ("Grant").
With regard to claim 8, the combination of Redden and Guo teaches the method of claim 1. Redden in the combination further teaches wherein the machine learning model is a ("the points of interest can be classified using machine learning algorithms or artificial intelligence, wherein the machine learning or artificial intelligence algorithms are preferably supervised learning algorithms trained on a labeled set of examples (e.g. images of plants with pre-identified plant centers) but can alternatively be unsupervised learning, semi-supervised learning, reinforcement learning, transduction, or utilize any other suitable machine learning or artificial intelligence algorithm [par. 0025]").
Although Redden teaches where the model can be various machine learning or artificial intelligence algorithms,
Redden does not explicitly teach where the machine learning model is a convolutional neural network.
In an analogous art (crop vision analysis) Grant teaches a convolutional neural network (" one or more machine learning models, such as a convolutional neural network (“CNN”), may be trained to detect one or more undesirable plant attributes in vision data [par. 0025]").
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have substituted the convolutional neural network taught by Grant, for the machine learning model taught by Redden, since the neural network incorporated into Redden's system would operate the same way in Grant's system and would predictably allow for features of crops to be recognized.
With regard to claim 13, the combination above teaches claim 8 above. Claim 13 recites limitations having the same scope as those pertaining to claim 8; therefore, claim 13 is rejected along the same grounds as claim 8.
Response to Arguments
Applicant's arguments filed 7/2/2026 have been considered but are moot in light of the new grounds of rejection necessitated by Applicant's amendment. Specifically, Guo et al. is now relied upon to teach aspects of the newly added limitations.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bainbridge et al. [US Pub. 2023/0292647] teaches a method of automated crop monitoring based on the processing and analysis of a large number of high resolution aerial images that map an area of interest using computer vision and machine learning techniques.
Conclusion
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 VINCENT W CHANG whose telephone number is (571)270-1214. The examiner can normally be reached (M-F) 10:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT WEN-LIANG CHANG
Examiner
Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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