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 the Application
This Office Action is in response to amendments and arguments received on July 9th, 2026. Claims 1 and 13 have been amended. Claims 1-20 remain now pending. This communication is the second Office Action on the Merits.
Key to Interpreting this Office Action
For readability, all claim language has been bolded. Citations from prior art are provided at the end of each limitation in parenthesis. Any further explanations that were deemed necessary by the Examiner are provided at the end of each claim limitation. The Applicant is encouraged to contact the Examiner directly if there are any questions or concerns regarding the current Office Action.
Response to Arguments
Applicant’s amendments and arguments made in accordance with 35 U.S.C. § 101 are accepted, and rejections based on 35 U.S.C. § 101 are withdrawn.
Applicant’s amendments and arguments made in accordance with 35 U.S.C. § 103 have been fully considered, but are not persuasive.
In response to arguments on pages 7-9 of 10 that none of the cited references disclose a model that outputs "a plant stem position map" as claimed, the Examiner respectfully disagrees.
With regards to arguments on page 7 of 10 that Hoeferlin does not disclose a plant stem, the Examiner agrees, as was admitted in the Office Action. Applicant’s arguments are not persuasive because the Applicant is attacking a rejection that was not proposed by the Examiner.
In regards to arguments on page 8 of 10 of arguments that Hoeferlin does not suggest a plant stem map, the Examiner respectfully disagrees. Applicant argues that the disclosures of Hoeferlin “directly contradicts” this assertion because Hoeferlin relies on the pixel regions of the semantic segmentation map. This is not persuasive because semantic segmentation and stem localization are not mutually exclusive processing techniques.
In regards to arguments on page 8 of 10 of arguments that Hoeferlin teaches away from this approach because only one head is evaluated according to the selected processing tool, the Examiner respectfully disagrees. MPEP § 2141.02, VI. states a prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Assoc., Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) However, "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004).
Accordingly, Hoeferlin does not “teach away” from the claimed invention, because Hoeferlin does not criticize, discredit, or otherwise discourage the use of plant stem mapping.
Applicant arguments on page 8 of 10 of arguments that the remainder of the prior art references fail to rectify this deficiency, the Examiner respectfully disagrees because Applicant has ignored the teachings of Flajolet as a modifying reference, as cited in the rejection.
Applicant arguments on page 9 of 10 of arguments that the references fail to disclose features of claim 13, these are not persuasive because these arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 pre-AIA 35 U.S.C. 103(a) 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 1-2, 4-5, 10-14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hoeferlin et al. (US 20230028506 A1) herein Hoeferlin, in view of Pham et al. (US 20220383037 A1) herein Pham and Flajolet et al. (US 20200073389 A1).
In regards to Claim 1, Hoeferlin discloses the following:
1. A method for crop treatment, comprising:
capturing an image of a crop row using a set of sensors onboard an agricultural implement; (see at least Fig. 1, step S104 and [0025] “in step S104, an image 12 of the field in which the plants are growing is captured by the image capturing means.” and [0014] “The seeds and thus the plants are primarily arranged in rows, in which case objects may be present between the rows and also between the individual plants within a row”)
at a processing system comprising a multi-head model: (see at least [0027] “position of the plants to be processed in the field is determined… using neural networks 10, 20, 30, 40… into which the image 12 captured in S104 is input in this case… neural networks 10, 20, 30, 40… are configured as so-called tree networks or treenets and have a plurality of heads”)
with a model backbone of the multi-head model, determining an embedding map for the image; (see at least Fig. 1, step S104 and [0026] “captured image 12 is processed in order to determine a position of the plants to be processed in the field... by means of a semantic segmentation of the captured image 12 correlated with the position information.” and “pixel-by-pixel semantic segmentation of images”, and “position of the plants to be processed can be determined by means of a classification of the image 12 or some other known method for object recognition in which a neural network is used. Hereinafter, both the semantic segmentation of the pixels or super pixels, i.e. the pixel-by-pixel classification, and the (standard) classification of the image are referred to as classification”)
Hoeferlin discloses semantic segmentation, known in the art as a computer vision technique that assigns a class label (like "road," "person," "sky") to every single pixel in an image, creating detailed, pixel-level understanding of a scene. It is the position of the Office that the broadest reasonable interpretation of an embedding map as claimed is a map of an image wherein information has been embedded. Accordingly, this is fully met by the disclosures of Hoeferlin.
However, an alternative interpretation is that an embedding map should be interpreted as a “term of the art” as an image analysis technique where embeddings (numerical representations) to map visual data (like pixels or image features) into a semantic vector space. Although considered a derivative of the semantic segmentation of Hoeferlin, it is admitted that Hoeferlin does not explicitly disclose this interpretation of an embedding map.
Accordingly, for the sake of compact prosecution, this interpretation is also known in the art as taught by Pham. (see at least [0019] “multi-attribute extraction system generates a low-level attribute feature map utilizing the embedding neural network and the digital image.”, [0038] “an image includes a digital file having a visual illustration and/or depiction of an object…. In some cases, an object includes… plants”, [0042] “object-label embedding vector 308 with a high-level attribute feature map from the embedding neural network 304”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Pham with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of overcoming the shortcomings with regard to accuracy and flexibility in predicting visual attributes at a large scale for arbitrary digital images. (Pham, [0001])
Hoeferlin discloses the following:
with a first model head of the multi-head model, determining a crop species map using the embedding map, (see at least [0027] “neural networks 10, 20, 30, 40 according to the invention are configured as so-called tree networks or treenets and have a plurality of heads, wherein only one of the heads is evaluated according to the selected processing tool and/or the types of useful plant grown in the field.”, [0033] “individual heads 14, 16, 18 of the neural network 10 according to the invention are trained in such a way that a different type of useful plant, grown in different fields to be processed, can be recognized using their classification results 14a to 14c, 16a to 16c, 18a to 18c. The heads can then distinguish between a useful plant 14a, 16a, 18a (e.g. maize, sugar beet, etc.), weeds 14b, 16b, 18b and the soil 14c, 16c, 18c.” and [0034] “individual heads 24, 26, 28 of the neural network 20 according to the invention, as shown in FIG. 3, are trained for different hierarchical levels. In this case, e.g. one head 24 is trained only for a differentiation between a useful plant 24a (e.g. maize or sugar beet), weeds 24b and soil 24c. Further heads 26, 28 can be trained, moreover, which enable a differentiation between a useful plant 26a, dicotyledonous weeds 26b, monocotyledonous weeds 26c and the soil 26d or generally a type-specific differentiation between plant A 28a, plant. B 28b, plat C 28c, etc.”)
wherein the crop species map comprises a first 2D array of elements each representing a location and a set of crop species; (see at least [0039] “After the position of the plants to be processed in the field has been determined in step S106 using the neural network 10, 20, 30, 40… the selected processing tool can be guided to the position of the plant and the corresponding processing can be carried out for the individual plant.”, “In order to enable an exact control of the movable apparatus, it may be necessary here for the position of the plant ascertained by means of the image to be converted into the coordinate system of the movable apparatus.”)
Hoeferlin suggest the following:
and using a second model head of the multi-head model, determining a plant stem position map using the embedding map, wherein the plant stem position map comprises a second 2D array of elements each representing a location and an estimate of a relative plant stem position; (see at least [0040] “in step S110, the plant is processed by the processing tool. In this case, by means of the use of the mechanical tool, the plant is removed, chopped or destroyed”)
Hoeferlin discloses processing the plants by removal or chopping by the processing tool. However, Hoeferlin does not explicitly disclose determining a plant stem during the direction of the processing tool. However, one of ordinary skill before the effective filing date would understand that the removal of a plant via a processing tool of Hoeferlin would require locating the plant stem during removal in order to properly remove or eliminate the plant. Further, the results of this modification would have been predictable.
However, for the sake of compact prosecution, this feature is more explicitly taught by Flajolet. (see at least [0015] “Once the autonomous machine 100 detects a target plant in the image, the autonomous machine 100 can extract a pixel location of the target plant in the image (e.g. a centroid of the target plant or an approximate location of the stem of a target plant).”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Flajolet with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of effectively weeding, watering, fertilizing, or otherwise operating around the target plant. (Flajolet, [0015])
Hoeferlin, as modified, discloses the following:
and based on both the crop species map and the plant stem position map, controlling the agricultural implement along the crop row. (see at least [0039] “step S108 the selected processing tool can be guided to the position of the plant and the corresponding processing can be carried out for the individual plant.”)
Further, this feature is also taught by Flajolet. (see at least [0015]-[0020] “tool modules 130”) See above for motivation to combine.
In regards to Claim 2, Hoeferlin suggests, but Flajolet more explicitly teaches the following:
2. The method of claim 1, further comprising determining a plant stem position by aggregating a subset of estimates of relative stem plant positions from the plant stem position map. (see at least [0015] “Once the autonomous machine 100 detects a target plant in the image, the autonomous machine 100 can extract a pixel location of the target plant in the image (e.g. a centroid of the target plant or an approximate location of the stem of a target plant).”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Flajolet with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of effectively weeding, watering, fertilizing, or otherwise operating around the target plant. (Flajolet, [0015])
In regards to Claim 4, Hoeferlin suggests, but Flajolet more explicitly teaches the following:
4. The method of claim 2, wherein the agricultural implement is controlled based on a moment of the subset of estimates. (see at least [0015] “Once the autonomous machine 100 detects a target plant in the image, the autonomous machine 100 can extract a pixel location of the target plant in the image (e.g. a centroid of the target plant or an approximate location of the stem of a target plant).”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Flajolet with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of effectively weeding, watering, fertilizing, or otherwise operating around the target plant. (Flajolet, [0015])
In regards to Claim 5, Hoeferlin discloses the following:
5. The method of claim 2, wherein the subset of estimates each correspond to a single plant instance. (see at least [0003] “For selective (the plant to be processed is distinguished from other plants and the soil) plant processing in a field, it is necessary for the position of a plant to be processed in a field to be recognized exactly.”, [0013] “objects or plants to be processed are individually processed successively” and [0026] “positions of the plants to be processed are determined individually”)
In regards to Claim 10, Hoeferlin discloses the following:
10. The method of claim 1, wherein the crop species map is determined based on a field crop type corresponding to a crop type of a current operation period. (see at least [0024] “in step S102, the processing tool is selected which is intended to process the plants or objects in a field. In this case, as described above, the spatial accuracy with which the plants are processed by the processing tool is dependent on the type of processing tool.” And [0027] “neural networks 10, 20, 30, 40 according to the invention are configured as so-called tree networks or treenets and have a plurality of heads, wherein only one of the heads is evaluated according to the selected processing tool and/or the types of useful plant grown in the field.”)
In regards to Claim 11, Hoeferlin is silent, but Pham teaches the following:
11. The method of claim 1, wherein the embedding map comprises a translation-equivariant image embedding. (see at least previous citations, see also [0048] “a neural network includes one or more machine learning algorithms such as, but not limited to, deep convolutional neural networks (CNN)” and [0077] “the multi-attribute extraction system 106 can utilize various types of neural networks for these components (e.g., CNN, FCN)”, wherein CNN/FCN networks are translation-equivariant by design, and therefore inherent.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Pham with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of overcoming the shortcomings with regard to accuracy and flexibility in predicting visual attributes at a large scale for arbitrary digital images. (Pham, [0001])
In regards to Claim 12, Hoeferlin discloses the following:
12. The method of claim 11, wherein the first model head and the second model head are parallel neural network decoders, (see at least Fig. 2, 3 and 4)
Hoeferlin is silent, but Pham teaches the following:
each configured to receive the translation-equivariant image embedding from the model backbone. (see at least previous citations, see also [0048] “a neural network includes one or more machine learning algorithms such as, but not limited to, deep convolutional neural networks (CNN)” and [0077] “the multi-attribute extraction system 106 can utilize various types of neural networks for these components (e.g., CNN, FCN)”, wherein CNN/FCN networks are translation-equivariant by design, and therefore inherent.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Pham with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of overcoming the shortcomings with regard to accuracy and flexibility in predicting visual attributes at a large scale for arbitrary digital images. (Pham, [0001])
In regards to Claim 13: Claim 13 claims a related method to claim 1, and comprises limitations that have a broadest reasonable interpretation (BRI) that is broader than the BRI of the limitations of claim 1. Accordingly, all the rejections of claim 1 also apply to claim 13 in the same or similar fashion.
Dependent claim 14 comprises limitations that have a broadest reasonable interpretation (BRI) that are broader than the BRI of the limitations of claims 2 and 5, and is therefore rejected the same or similar to claims 2 and 5, above.
Dependent claim 16 comprises limitations that are the same or similar to limitations of claim 1, and is rejected per claim 1, above.
Dependent claim 19 is the same or similar to claim 12, and is rejected the same or similar to claim 12, outlined above.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Hoeferlin in view of Pham and Flajolet, and further in view of Taguchi et al. (US 20130156262 A1) herein Taguchi.
In regards to Claim 3, Hoeferlin is silent, but Taguchi teaches the following:
3. The method of claim 2, wherein aggregation comprises voting. (see at least [0006] “Hough voting scheme” and [0059]-[0061] and [0069]-[0072] “voting scheme”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Taguchi with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of solving the correspondence problem in the presence of sensor noise, occlusions, and clutter. (Taguchi, [0005])
Claims 6-9, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hoeferlin in view of Pham and Flajolet, and further in view of Gurzoni, Jr. et al. (US 20200019777 A1) herein Gurzoni.
In regards to Claim 6, Hoeferlin discloses the following:
6. The method of claim 1, further comprising, at the processing system, with a third model, determining a plant instance map, (see at least Fig. 2, items 14, 16 and 18, see also Fig. 3, items 24, 26 and 28 and Fig. 4, items 14, 16, 26 and 28)
Hoeferlin is silent, but Gurzoni teaches the following:
wherein the plant instance map comprises a third 2D array of elements each representing a location and a set of plant instances. (see at least [0047] “the map 114 may be presented on a graphical user interface including graphical representations (e.g., 2D and/or 3D) graphical representations, indicating within the map 114 and/or modeling the various properties within the surveyed plant area.” and [0048] “the map 114 may show individual plants or groups of plants on the plant area.”, see also [0098] “FIG. 11 is a flowchart 400 illustrating a method for generating a map 114 of a plant area”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Gurzoni with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of with the motivation of allowing for accurate and timely machine counting of fruit on the tree or vine that normally rely on manual estimation and are often inaccurate and labor intensive (Gurzoni, [0002]) and/or helping farmers improve fruit quality and reduce operating cost by making better decisions on intensity of fruit thinning and size of the harvest labor force. (Gurzoni, [0003])
In regards to Claim 7, Hoeferlin suggests, but Flajolet more explicitly teaches the following:
7. The method of claim 6, wherein the plant stem position map is determined independently of the plant instance map. (see at least [0015] “Once the autonomous machine 100 detects a target plant in the image, the autonomous machine 100 can extract a pixel location of the target plant in the image (e.g. a centroid of the target plant or an approximate location of the stem of a target plant).”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Flajolet with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of effectively weeding, watering, fertilizing, or otherwise operating around the target plant. (Flajolet, [0015])
Further, this is also taught by Gurzoni. (see at least [0047] “graphical representations (e.g., 2D and/or 3D) graphical representations, indicating within the map 114 and/or modeling the various properties within the surveyed plant area. The various properties may include… trunk or stem diameter, trunk or stem circumference”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Gurzoni with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of with the motivation of allowing for accurate and timely machine counting of fruit on the tree or vine that normally rely on manual estimation and are often inaccurate and labor intensive (Gurzoni, [0002]) and/or helping farmers improve fruit quality and reduce operating cost by making better decisions on intensity of fruit thinning and size of the harvest labor force. (Gurzoni, [0003])
In regards to Claim 8, Hoeferlin suggests, but Flajolet more explicitly teaches the following:
8. The method of claim 7, wherein the plant stem position map is determined independently of the crop species map. (see at least [0015] “Once the autonomous machine 100 detects a target plant in the image, the autonomous machine 100 can extract a pixel location of the target plant in the image (e.g. a centroid of the target plant or an approximate location of the stem of a target plant).”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Flajolet with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of effectively weeding, watering, fertilizing, or otherwise operating around the target plant. (Flajolet, [0015])
Further, this is also taught by Gurzoni. (see at least [0047] “graphical representations (e.g., 2D and/or 3D) graphical representations, indicating within the map 114 and/or modeling the various properties within the surveyed plant area. The various properties may include… trunk or stem diameter, trunk or stem circumference”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Gurzoni with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of with the motivation of allowing for accurate and timely machine counting of fruit on the tree or vine that normally rely on manual estimation and are often inaccurate and labor intensive (Gurzoni, [0002]) and/or helping farmers improve fruit quality and reduce operating cost by making better decisions on intensity of fruit thinning and size of the harvest labor force. (Gurzoni, [0003])
In regards to Claim 9, Hoeferlin discloses the following:
9. The method of claim 6, wherein each of a subset of elements of the third 2D array in the plant instance map corresponds to multiple plant instances. (see at least [0014] “individual plants within a row”)
Further, Gurzoni also teaches this limitation. (see at least [0048] “the map 114 may show individual plants or groups of plants on the plant area.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Gurzoni with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of with the motivation of allowing for accurate and timely machine counting of fruit on the tree or vine that normally rely on manual estimation and are often inaccurate and labor intensive (Gurzoni, [0002]) and/or helping farmers improve fruit quality and reduce operating cost by making better decisions on intensity of fruit thinning and size of the harvest labor force. (Gurzoni, [0003])
In regards to Claim 15: Claim 15 is the same or similar to claim 7, and is rejected per claim 7, above.
In regards to claim 20, Hoeferlin is silent, but Gurzoni teaches the following:
20. The method of claim 13, further comprising using a fourth set of neural network layers, determining a crop health parameter map based on the embedding map and determining a second set of control instructions based on the crop health parameter map. (see at least [0047] “estimated plant health, disease estimate, nutrient deficiency estimate” and [0072] “health estimation blocks (e.g., canopy health estimation block 212, fruit disease estimation block 214, flower disease estimation block 216, disease identification block 218, etc.),”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Gurzoni with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of with the motivation of allowing for accurate and timely machine counting of fruit on the tree or vine that normally rely on manual estimation and are often inaccurate and labor intensive (Gurzoni, [0002]) and/or helping farmers improve fruit quality and reduce operating cost by making better decisions on intensity of fruit thinning and size of the harvest labor force. (Gurzoni, [0003])
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Hoeferlin in view of Pham and Flajolet, and further in view of Redden (US 20150245554 A1) herein Redden.
In regards to claim 17, Hoeferlin is silent, but Redden teaches the following:
17. The method of claim 13, wherein a crop component position within the set of crop component positions is based on a prior crop component position determined using a prior image of the crop row and a set of motion information for the sensor. (see at least Fig. 3 and [0026] “The confidence level for the point of interest is preferably increased if the point of interest is identified or extracted from a predetermined area or pixel-neighborhood (after accounting for movement of the system) in a subsequent image S134 (as shown in FIG. 3B), and preferably decreased otherwise.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Redden with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of increasing the classification confidence level for each detected plant center, which increases with each new image. (Redden, [0026])
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hoeferlin in view of Pham and Flajolet, and further in view of Schuh et al. (US 20150245554 A1) herein Schuh.
In regards to claim 18, Hoeferlin is silent, but Schuh teaches the following:
18. The method of claim 13, further comprising determining a set of uncertainty regions for the set of crop component positions, (see at least [0020] “quantifying the uncertainty of that spatial relationship, and using this uncertainty measurement as a factor to adjust the region of the field targeted by the farming machine to apply the plant treatment to the plant”) wherein the control instructions cause the agricultural implement to actuate along a path determined using the set of uncertainty regions. (see at least [0079] “Dynamic adjustment of the treatment buffer is affected by an uncertainty measurement of an expected position.” and [0081] “[0081] If the uncertainty measurement of the expected position decreases from the determination of one adjustment to the next (e.g., upon the pose module 305 factoring for a new image and/or new sensor signals), the pose module 305 generates a smaller treatment buffer, e.g., a treatment buffer covering a smaller area of the field. Conversely, if the uncertainty measurement of the expected position increases from the determination of one adjustment to the next (e.g., upon the pose module 305 factoring for a new image and/or new sensor signals), the pose module 305 generates a larger treatment buffer, e.g., a treatment buffer covering a larger area of the field.”)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the features of Schuh with the invention of Hoeferlin, with a reasonable expectation of success, with the motivation of precisely and accurately targeting plants for the application of treatment compounds, preventing the need to broadly distribute treatment compounds across a field, which can be wasteful. (Schuh, [0004])
Conclusion
THIS ACTION IS MADE FINAL. 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 Jason Roberson, whose telephone number is (571) 272-7793. The examiner can normally be reached from Monday thru Friday between 8:00 AM and 4:30 PM. The examiner may also be reached through e-mail at Jason.Roberson@USPTO.GOV, or via FAX at (571) 273-7793. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh, can be reached on (571) 272-7691.
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Applicants are invited to contact the Office to schedule either an in-person or a telephone interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/JASON R ROBERSON/
Patent Examiner, Art Unit 3669
September 17, 2026
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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