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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/06/2026 has been entered.
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, 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.
Claims 1, 2, 4, 6, 8-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US 20190150357 A1), hereinafter Wu, in view of McMenamy (US 20210321602 A1) and Booher et al. (US 20200156100 A1), hereinafter Booher.
Regarding claim 1, Wu discloses a method of operating an agricultural sprayer to treat weeds interspersed among a row crop in a field, comprising:
selectively spraying the weeds in the field during a spray treatment of the field by automatically detecting a presence of the weeds ahead of the agricultural sprayer, and then automatically target spraying the detected weeds with a product carried by the sprayer while avoiding broadcast spraying of the product on the row crop (¶ 0100, “During the post-emergent stage of planting [crop leaf has emerged from the soil], various references describe looking for weeds and spot spraying between the crop rows 12. However, unlike past references, this disclosure relies on the location of the emerging crop leaf being either known [e.g. from the information of the seeding vehicles, auto-track previous drive] or the crop row location is identifiable by the leaves [e.g. localized straight line fit to the location of the leaves and or image guidance described in FIG. 29 or by other guide markers, e.g. end of row or field markers]. Once the crop row 12 is identified, anything that is not on a crop row is classified as a ‘weed’ and is sprayed. The average width of a crop row can be entered by the operator before driving through the field, and anything that is not within, say, one or two standard deviations from a center/midpoint of the width would be sprayed”);
during the selectively spraying, gathering image data including images of the row crop using an imaging system carried by the sprayer and correlating the image data to a geographic location of the row crop in the field (¶ 0180, lines 12-17, “Applying a line model [the midpoint of an image sensor unit 50 is the x coordinate and the vanishing point is the y coordinate], keeps the image sensor unit 50 located between two parallel, adjacent crop rows, and distance to objects are determined based on the location in the x-y coordinate system”);
performing image processing of the image data to determine emergence data corresponding to an emergence state of the row crop and a magnitude of growth of the row crop in the field (¶ 0088, “In some embodiments, other types of sensors supplement the image sensors. Multiple or pairs of image sensors provides stereo to gauge distances and height [i.e. provide 3-Dimension assessment]. Alternatively, for example, LiDAR, proximity sensors or lasers are used to detect distances or height of crop leaves in the directions where the image sensors do not have coverage. The image sensors are focusing on the forward and rearward views if the sensor attachment fixtures are mounted to the top of a spray boom or planter unit. To detect distance of the boom to the crop height right below the boom itself, proximity sensors are used to point directly downward. As another example, if the visual imagery is poorer for night time operation, infrared sensors are used instead of visible-spectrum sensors. Or for example, chlorophyll detectors can be used to supplement or check the visible-green detection;” ¶ 0180, lines 12-17; ¶ 0181, lines 1-5, “Each image sensor captures a wide enough field of vision so as to determine the crop rows closest to the path traveled by the particular image sensor. As another example, if the crop leaves have already emerged, color contrast analysis determines where the crop rows are”);
correlating the emergence data to the geographic location of the row crop in the field (¶ 0181, “Each image sensor captures a wide enough field of vision so as to determine the crop rows closest to the path traveled by the particular image sensor. As another example, if the crop leaves have already emerged, color contrast analysis determines where the crop rows are. The method and software instructions for identifying lane markers are installed on a “smartphone” type electronics processor and used instead to identify the crop rows, implemented, for example, as API [application programming interface] instructions for an android platform. In some embodiments, the original GPS and RTK position information from planting the seeds [e.g. GPS position and spacing between seed drops and spacing between crop rows] are used to identify the crop rows based on a stored mapping of the farm field and coordinate system. Alternatively, the vehicle operator uses a feeler guidance system [e.g. tactile sensor mounted to the tires to detect location of plants] and/or visually sees the location of rows and positions the tractor [e.g. tires, headers] so as to traverse the field appropriately centered about the rows”); and
an emergence state and a threshold emergence setting, automatically comparing the emergence state to a threshold emergence setting (¶ 0098, lines 1-11, “During pre-emergent stage of planting [crop leaf has not emerged from the soil], the following example methods or electronic triggers include 1] threshold detection in individual pixels [e.g. element 101] for off-color objects [e.g. greenish objects against brown or tan soil or residue]; 2] threshold detection among pixels for protrusions [e.g. lump or height] from the ground; 3] aggregated percentage of pixels that do not match some baseline expectation [e.g. mass [density], color, shape, height]; 4] enhance green and protrusions or mass; 5] filter out noise; 6] optionally, correlate results from multiple images”).
Wu, however, fails to specifically disclose generating a graphic report illustrating the emergence state of the row crop as a function of the geographic location of the row crop in the field, the graphic report including: data representative of a percentage of planted seeds resulting in emerged row crop plants as a function of the geographic location of the row crop in the field and/or data representative of emergence quality as a percentage of targeted plant emergence achieved by emerged row crop plants as a function of the geographic location of the row crop in the field; and if the emergence state is below the threshold emergence setting, automatically stopping the selectively spraying.
McMenamy is in the field of plant monitoring systems and teaches generating a graphic report illustrating the emergence state of the row crop as a function of the geographic location of the row crop in the field (¶ 0209, “In any of the embodiments for measuring stalk diameter, any arm [such as linkages 1520a, 1520b; arm 1720a, 1720b; linkage arms 1952a, 1952b; fluid arms 2620a, 2620b; and/or fluid arms 2720a, 2720b] or planting contacting member can also be used to count emerged plants to provide a stand count. In these embodiments, since stalk diameter does not need to be measured, one arm can be used to detect the presence of an emerged plant. The deflection of an arm indicates the presence of an emerged plant. The location of a sensed, emerged plant can be coupled with its GPS location and stored in memory 1205. A map can then be generated and displayed of the location of emerged plants in the field. The number of emerged plants can be compared to the population of seeds that were planted to calculate a percent emergence score. The location of emerged plants can also be combined with yield data after harvest,” ¶ 0210, “Processing logic of a processing system [e.g., 1262, 1220] is configured to receive sensed data from any arm, to determine GPS location for emerged plants, and to generate a map data to display the locations of the emerged plants in a field. The processing logic is further configured to compare a number of emerged plants to the population of seeds that were planted for a given row or region and to calculate a percent emergence score”), the graphic report including: data representative of a percentage of planted seeds resulting in emerged row crop plants as a function of the geographic location of the row crop in the field; and/or data representative of emergence quality as a percentage of targeted plant emergence achieved by emerged row crop plants as a function of the geographic location of the row crop in the field (¶ 0209, ¶ 0210).
Therefore, it would have been obvious to one of ordinary skill in the art of plant monitoring systems before the effective filing date of the claimed invention to modify the method of Wu to include generating a graphic report illustrating the emergence state of the row crop as a function of the geographic location of the row crop in the field, the graphic report including: data representative of a percentage of planted seeds resulting in emerged row crop plants as a function of the geographic location of the row crop in the field and/or data representative of emergence quality as a percentage of targeted plant emergence achieved by emerged row crop plants as a function of the geographic location of the row crop in the field, as taught by the report generation of McMenamy. The graphical reporting would more effectively communicate data and information to the user, which would improve overall visibility over the method. The modification would have a reasonable expectation of success.
Booher is in the field of agricultural spraying systems and teaches further comprising: automatically comparing the state to a threshold setting; and if the state is below the threshold setting, automatically stopping the selectively spraying (¶ 0011, lines 43-65, “the mobile device may be further configured to receive one or more inputs from a user defining user-selectable criteria for spraying, and to receive geographic location and velocity information from the GPS antenna system, and to process the geographic location and velocity information in view of one or more databases of information comprising map data defining spray regions and no-spray regions, and plant data corresponding to one or more of locations, heights, widths, shapes, and densities of plants located within the spray regions, and vehicle data defining the locations of each of the nozzle assemblies relative to the locations of the GPS antenna system and the LiDAR sensing system when installed on the vehicle, and based thereon wirelessly communicate on, off, and pulse-width modulating signals to the one or more controllers to individually turn on and off flow of the liquid through each of the individual nozzle assemblies based on whether each nozzle assembly is within a spray region or a no-spray region, and to turn on or off or vary flow rate of the liquid through each of the nozzle assemblies based on the user-selectable criteria, velocity information, and plant data corresponding to a portion of a plant proximate each nozzle assembly when installed on the vehicle”).
Therefore, it would have been obvious to one of ordinary skill in the art of plant monitoring systems before the effective filing date of the claimed invention to modify the method of Wu in view of McMenamy to include further comprising: automatically comparing the state to a threshold setting; and if the state is below the threshold setting, automatically stopping the selectively spraying, as taught by the spraying control of Booher. This would allow for more selective crop treatment, which would allow the method to be more adaptable to specific crop conditions. The modification would have a reasonable expectation of success.
Regarding claim 2, Wu in view of McMenamy and Booher discloses the method of claim 1, and furthermore, the modified reference teaches further comprising:
selectively spraying the weeds in the field during one or more further spray treatments of the field (Wu; ¶ 0093, lines 1-5, “Using weed and fungus monitoring and control as an example of the framework, FIG. 1 depicts an agricultural vehicle 24 such as a sprayer tractor towing a tank 22, both traversing the field during the pre-emergent stage and also during the post-emergent stage;” ¶ 0093, lines 38-40, “The health of the soil and plants are visible and with their image captured, further activity may be planned [e.g. add more spray, add special types of spray, damaged crops may be revived]” ¶ 0100; ¶ 0191, lines 4-24, “Desired spray patterns corresponding to these different input images and other inputs [e.g. vehicle speed, wind, humidity] are used to train the neural network or other artificial intelligent algorithms. In some embodiments, the training samples are divided up based on category such as pre-emergent fields, post-emergent fields, corn field, wheat field, rice field, day time, dusk time, rainy weather, drought conditions, and so on. Each neural network architecture would then be trained or optimized for that particular category. Then during actual field operation, the appropriately trained algorithm would be exercised to match the field scenario. Although the artificial intelligent methods are described here in the context of field chemical applications, the methods can also be applied during harvesting or other times in the life cycle of a crop. For example, for harvesting, pairs of input images and desired output action can be used to train a neural network. Input images include the plants and the fields, and the desired output action include slowing down a harvester, lowering the headers, slowing the draper, adjusting the cutting blade widths, and so on”);
during the selectively spraying of the weeds in the field during the one or more further spray treatments of the field, gathering further image data including images of the row crop using the imaging system carried by the sprayer and correlating the further image data to the geographic location of the row crop in the field (Wu; ¶ 0180, lines 12-17);
performing image processing of the further image data to determine further emergence data corresponding to a further emergence state of the row crop in the field (Wu; ¶ 0180, lines 12-17; ¶ 0181, lines 1-5);
correlating the further emergence data to the geographic location of the row crop in the field (Wu; ¶ 0181); and
updating the graphic report illustrating the further emergence state of the row crop as a function of the geographic location of the row crop in the field (McMenamy; ¶ 0129, The implement 1240 [e.g., planter, cultivator, plough, sprayer, spreader, irrigation implement, etc.] includes an implement network 1250, a processing system 1262, a network interface 1260, and optional input/output ports 1266 for communicating with other systems or devices including the machine 1202. The implement network 1250 [e.g, a controller area network [CAN] serial bus protocol network, an ISOBUS network, etc.] includes a pump 1256 for pumping fluid from a storage tank[s] 1290 to application units 1280, 1281, . . . N of the implement, sensors 1252 (e.g., plant sensors for detecting plants, positional sensors for detecting positional data of applicator arms, linkage members, or flexible members to determine plant data, speed sensors, seed sensors for detecting passage of seed, downforce sensors, actuator valves, moisture sensors or flow sensors for a combine, speed sensors for the machine, seed force sensors for a planter, fluid application sensors for a sprayer, or vacuum, lift, lower sensors for an implement, flow sensors, etc.], controllers 1254 [e.g., GPS receiver], and the processing system 1262 having processing logic 1264 for controlling and monitoring operations of the implement. The pump controls and monitors the application of the fluid to crops or soil as applied by the implement. The fluid application can be applied at any stage of crop development including within a planting trench upon planting of seeds, adjacent to a planting trench in a separate trench, or in a region that is nearby to the planting region [e.g., between rows of corn or soybeans] having seeds or crop growth. The plant data can be obtaining at any stage of crop development upon emergence of plants,” ¶ 0209, ¶ 0210).
Regarding claim 4, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses wherein: the detecting of the presence of the weeds ahead of the sprayer is performed at least in part using the same imaging system used to gather images of the row crop (¶ 0100).
Regarding claim 6, Wu in view of McMenamy and Booher discloses the method of claim 1, and furthermore, the modified reference teaches wherein: the graphic report includes data representative of a total number of emerged row crop plants as a function of the geographic location of the row crop in the field (McMenamy; ¶ 0209, ¶ 0210).
Regarding claim 8, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses wherein: the performing, the correlating and the generating are performed on the agricultural sprayer (¶ 0045, lines 19-38, “In many embodiments, two or more of the local modular image sensor units 50 are grouped together at a fixed distance apart and mounted together on a common platform structure [e.g. rod 52 or more generally, referred to as attachment fixture 1952] for easy portability to another agricultural machine. The image sensor system 60 [i.e. the platform 52 together with more than one of the sensor units 50 mounted on the agricultural equipment or machine] is organic and can have any number of image sensor units 50 [e.g. several units 50 of re-purposed smartphone-type electronics on a single rod 52]. A local bank of methods or procedures accompanies or is remotely coupled to each local image sensor unit 50 in the portable sensor system [e.g. like the smartphones have a CPU and other processors co-located with the cameras on the smartphones]. The bank of methods or procedures includes a set of electronic-software programs or algorithms that enable an end-user to add more monitoring or targeted procedures via a portal [e.g. screen or keyboard] to each of the local image sensor units 50;” ¶ 0154, lines 1-9, “In some embodiments, the image sensor system causes real time action based on lowered resolution images. In other embodiments for high resolution images [e.g. over 100,000 elements], the image sensor system sends data to the vehicle cab's central computer or to remote servers or to memory so that the data can be further analyzed, a method that is useful for more complex analysis that includes too many instructions to be executed while the vehicle is traveling”).
Regarding claim 9, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses wherein: the performing, the correlating and the generating are performed remotely from the agricultural sprayer (¶ 0045, lines 19-38; ¶ 0154, lines 1-9).
Regarding claim 10, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses wherein: the performing, the correlating and the generating are performed during the selectively spraying (¶ 0180, lines 12-17; ¶ 0181, lines 1-5).
Regarding claim 11, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses wherein: the performing, the correlating and the generating are performed subsequent to the selectively spraying (¶ 0154, lines 1-9).
Regarding claim 13, Wu in view of McMenamy and Booher discloses the method of claim 1.
Wu discloses further comprising: generating a planting prescription for future planting operations using the image data and/or the emergence data (¶ 0185, lines 54- 84, “Identify distance from the image sensor unit 50. 923, Include precision GPS data for the farm. 924, Include distance calibration correction. 925, Correct for jitter. 926, Optionally use two or more images [cameras] for improved estimate of how far away is the object. 927, Apply automation and/or provide alerts to notify the vehicle operator for identified patterns, potential harms, obstacles, predicted patterns. 928, Vehicle responds to instruction as a result of identified patterns [e.g. change height of cultivator shanks and disks, release spray, hot inject additional chemicals, adjust reel speed, depending on the type of agricultural vehicle that is in motion]. 929, Perform offline analysis to obtain crop yield for each crop row and plant location in the file. 930, Perform offline analysis to find patterns and compare found-offline patterns with the real-time patterns calculated results found in real time. Adjust real-time pattern recognition and calibration when major discrepancies are found. 940, Collect data on each pass of the vehicle through the farm field. 941, Perform precision planting and crop maintenance. 942, Re-position and move vehicle containing the intelligence between the crop rows based on crop lanes identified, positioning the vehicle equipment to optimize performance of the response to the found patterns for each of the applications. 943, Plan for next crop growth cycle. Save the previously obtained data containing the amount of residue, fertilizer, herbicide, crop yield and other information that are correlated with the plant, row, weather, time and field location. Perform prescription farming in the next crop cycle. If the crop yield is past a threshold of good or high, the prescription would remain the same and can be applied to the same location in the field in real time”).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20190150357 A1), in view of McMenamy (US 20210321602 A1) and Booher (US 20200156100 A1), as applied to claim 1, and further in view of Wellner et al. (US 20170074788 A1), hereinafter Wellner.
Regarding claim 14, Wu in view of McMenamy and Booher discloses the method of claim 1, however, the modified reference fails to specifically disclose further comprising: determining an emergence date as part of the emergence data.
Wellner is in the field of plant monitoring and teaches determining an emergence date as part of the emergence data (¶ 0014, “In preferred embodiments a ratio of eye-region and reference measurements, at a single wavelength or over a plurality of wavelengths, is used in a regression model, in particular a continuum regression model, to provide a prediction in the form y=Kx where y is a prediction of time to sprouting, K is a scalar or vector constant, and x is a scalar or vector wavelength ratio or spectrum ratio”).
Therefore, it would have been obvious to one of ordinary skill in the art of plant monitoring systems before the effective filing date of the claimed invention to modify the method of Wu in view of McMenamy and Booher to include further comprising: determining an emergence date as part of the emergence data, as taught by the determination process of Wellner. This would allow for more accurate crop tracking, which would improve overall visibility over the method and improve planning for the user. The modification would have a reasonable expectation of success.
Response to Arguments
Applicant's arguments filed 07/06/2026 have been fully considered but they are not persuasive.
Regarding the argument on page 7 that “Wu does not appear to be concerned about monitoring and reporting on the emergence state of the row crop as a function of the geographic location of the row crop in the field. The quotation above from Wu Para 0116, lines 1-7, and the cited paragraphs 0180 and 0181, do not say anything different. The discussion of ‘height or density’ in the quoted language is discussing identification of weeds, not a measurement of the magnitude of growth of the row crop,” the Examiner maintains that under the broadest reasonable interpretation of the limitation “performing image processing of the image data to determine emergence data corresponding to an emergence state of the row crop from the field,” as recited by claim 1, Wu discloses the claimed structure, as ¶ 0180, lines 12-17, details collection of image data with respect to the specific location of row crops in a field, and ¶ 0181, lines 1-5, details image processing for the row crops in the field, where emergence data corresponding to an emergence state is collected and processed as claimed. Therefore, the claimed emergence state determination of claim 1 is disclosed by Wu. Furthermore, Wu details in ¶ 0088 that the image processing determines a magnitude of the row crop in the field, as Wu discloses that the image sensors are arranged in a stereo configuration to determine a height of the crop that has emerged.
Regarding the argument on page 10 that “Thus, in the method of claim 1 the ‘selective spraying’ of the weeds may be automatically stopped based on a detection of crop failure of the row crop. And although Booher does discuss defining ‘no-spray’ regions based on ‘user-selectable criteria’ it does so only in general terms and it never proposes any of: Setting of an threshold emergence setting; Automatically comparing the emergence state to the threshold emergence setting; or If the emergence setting is below the threshold emergence setting, automatically stopping the selectively spraying,” the Examiner submits that although Booher discusses spray regions and no-spray regions, Booher also teaches automatically comparing the state to a threshold setting; and if the state is below the threshold setting, automatically stopping the selectively spraying, as ¶ 0011 of Booher details threshold settings in the form of user inputs such as locations, heights, widths, shapes, and densities of plants located within the spray regions. Booher also teaches if the state is below the threshold setting, automatically stopping the selectively spraying, as ¶ 0011 further details that the flow is turned on or off depending on if the threshold settings in the form of user inputs are met or not met. Therefore, Booher teaches the claimed spraying in response to threshold settings.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Kanagaraj et al., US 20240206454 A1, discusses a system mounted in a vehicle for agricultural application and method of operation of the system.
Webb et al., US 20230252791 A1, discusses performing actions based on evaluation and comparison of multiple input processing schemes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SPENCER THOMAS CALLAWAY whose telephone number is (571)272-3512. The examiner can normally be reached 9am-5pm.
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/S.T.C./Examiner, Art Unit 3642
/JOSHUA D HUSON/ Supervisory Patent Examiner, Art Unit 3642
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