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 Arguments
Applicant's arguments filed 03/11/2026 have been fully considered and are persuasive. However, due to the amendment, newly found reference Sibley (US 2023/0247928) teaches, the amended portion of the claim language and then some of the other parts of the claim language. For these reasons a new rejection applies.
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.
Claims are rejected under 35 U.S.C. 103 as being unpatentable over Morgan (US 2018/0184581) in view of Sibley (US 2023/0247928).
As per claim 1, Morgan teaches, a method for evaluating provided data accuracy for an agricultural machine (Morgan, fig.13 shows the method for evaluating accuracy for an agricultural machine of fig.1-2 ), comprising: obtaining image data from a camera on the agricultural machine (Morgan, fig.13 905 “Display successive row images at regular intervals” represents obtaining image data from a camera on an agricultural machine of fig.1-2 and ¶[0040] “Turning to FIG. 7, an image capture apparatus 700 is illustrated incorporating a camera 750 mounted to an extension 710.” Represents having the camera and ¶[0052] “It should be appreciated that because multiple row units may incorporate an image capture apparatus, it may be undesirable to simultaneously display images from all such row units.” Further evidence of a camera); processing the image data for a time interval and identifying inadequate images (Morgan, fig.13 905 intervals represents the time intervals, 915 represents inadequate images as the alarm conditions represent something wrong); determining the number of inadequate images compared to the total number of images for the time interval (Morgan, ¶[0052] “The alarm threshold may comprise a selected constant value of the alarm value or a statistical function (e.g., one or more standard deviation above or below the mean or average) of the alarm value reported to the monitor during a preceding period or during operation in a specified area (e.g., 30 seconds, 30 feet of travel, the entire field associated with the operation).” by taking these one or more standard deviation above or below the mean or average this represents determining the number of inadequate images compared to the total number of images for the time interval ); generating a camera confidence for the time interval; and providing a feedback indicating the camera confidence (Morgan, ¶[0052] “The alarm threshold may comprise a selected constant value of the alarm value or a statistical function (e.g., one or more standard deviation above or below the mean or average) of the alarm value reported to the monitor during a preceding period or during operation in a specified area (e.g., 30 seconds, 30 feet of travel, the entire field associated with the operation).” along with fig.13 920 display row corresponding to row exhibition alarm condition, this would represent the ratio as a median would represents the ratio and represents providing feedback).
Morgan doesn’t clearly teach, processing the image data for a time interval and identifying inadequate images that contain unclear image data, and generating a camera confidence based on the ratio of inadequate images to total images for the time interval; and providing a feedback indicating the camera confidence.
However, Sibley teaches, processing the image data for a time interval (Sibley, ¶[0122] “For example, a predetermined period may be used to sample images for further training. The predetermined period may be, e.g., once every 5 seconds or once every 10 seconds….” This represents the time interval processing) and identifying inadequate images that contain unclear image data (Sibley, ¶[0060] “An object such as the weed 1312, which was detected as an object, but not with such a confidence to take a specific action on the object, may be alerted for user input or labeling in real-time.” This is equivalent to identifying inadequate images that contain unclear image data, to not take specific action would mean an unclear image and would need further help), and generating a camera confidence based on the ratio of inadequate images to total images for the time interval (Sibley, ¶[0060] “1. detection that a portion of an image includes an object and a confidence level associated to the detection” This would be equivalent to camera confidence and ¶[0046] “Images may be stored into a database 1108 that may be used to store the captured images from multiple cameras and sensors fitted on the agricultural vehicles 1106 that have undergone processing such as annotations (1106A), object detection information (1106B), or a manifest generation (1106C). A transfer function 1110 may be used to identify which images in the database 1108 (or a corresponding manifest) are new and suitable for transfer to an offline computing platform 1120 for additional processing.” The images are taken from a camera on an agricultural machine); and providing a feedback indicating the camera confidence (Sibley, ¶[0088] “In one example, any detections above a certain confidence (e.g. over 70% or over 90%), the detection does not get sent to a user or further processing offline for analysis since the confidence is high.” This confidence score would be the confidence of the camera because it has not been sent for further processing yet).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Sibley with Morgan to be able to get a confidence measure for the camera on an agricultural machines and identify inadequate images that contain unclear image data.
The motivation would have been to improve system processing efficiency by not having to do extra processing when confidence is high as taught by Sibley ¶[0088] “In one example, any detections above a certain confidence (e.g. over 70% or over 90%), the detection does not get sent to a user or further processing offline for analysis since the confidence is high.”.
As per claims 2, Morgan in view of Sibley teaches, the method of claim 1, wherein the image data comprises images of a furrow (Morgan, ¶[0052] “the alarm value may correspond to an agronomic property or planting criterion (e.g., residue density, trench collapse, trench shape, trench depth, seed spacing, seed singulation, seed population, fertilizer flow rate) which may be estimated based on analysis of the row image or measured by another agronomic property sensor (such as a seed sensor, fertilizer flow rate sensor, trench depth sensor).” a furrow is equivalent to a trench).
As per claim 3, Morgan in view of Sibley teaches, the method of claim 1, wherein inadequate images comprise images that cannot be evaluated to determine a depth of a furrow (Morgan, ¶[0052] “The monitor 50 may optionally indicate a graphical representation of the alarm condition adjacent to the row image, e.g. in a separate window indicating the alarm or by adding an attention-drawing indication (e.g., a red border) to a window (e.g., soil data measurement window 830, agronomic property window 840). At step 925, the monitor 50 preferably identifies a resolution of the alarm condition (e.g., by enabling the user to cancel the alarm or by determining that the alarm condition is no longer active) and preferably returns to step 905.” By pointing this out the user can then is able to tell images that cannot be evaluated to determine a depth of a furrow ).
As per claims 4 and 14, Morgan in view of Sibley teaches, the method of claim 1, wherein the feedback is displayed visually on a user interface (Morgan, fig.13 920 “display..” ).
As per claims 5 and 15, Morgan in view of Sibley teaches, the method of claim 4, wherein the user interface displays a bar configured to change in size to correspond with the camera confidence (Morgan, fig.12 showing the bar of percentages of the confidence).
As per claims 6 and 16, Morgan in view of Sibley teaches, the method of claim 5, wherein the bar transitions from solid to hashed when the camera confidence is below a threshold (Morgan, ¶[0052] “The alarm threshold may comprise a selected constant value of the alarm value or a statistical function (e.g., one or more standard deviation above or below the mean or average) of the alarm value reported to the monitor during a preceding period or during operation in a specified area (e.g., 30 seconds, 30 feet of travel, the entire field associated with the operation). At step 915, the monitor 50 preferably identifies a row exhibiting an alarm condition (e.g., at which the alarm value has exceeded the alarm threshold). At step 920, the monitor 50 preferably displays (e.g., on the screen 800) the row image captured by the image capture apparatus associated with the row unit exhibiting the alarm condition. The monitor 50 may optionally indicate a graphical representation of the alarm condition adjacent to the row image, e.g. in a separate window indicating the alarm or by adding an attention-drawing indication (e.g., a red border) to a window (e.g., soil data measurement window 830, agronomic property window 840). At step 925, the monitor 50 preferably identifies a resolution of the alarm condition (e.g., by enabling the user to cancel the alarm or by determining that the alarm condition is no longer active) and preferably returns to step 905.” When below a threshold the display would display bar transitions from solid to hashed as an alarm, as traditional alarm system do).
As per claims 7 and 17, Morgan in view of Sibley teaches, the method of claim 1, wherein the feedback is considered by an automated system (Morgan, fig.12 showing an automated feedback system).
As per claims 8 and 18, Morgan in view of Sibley teaches, the method of claim 7, wherein the automated system is a downforce automation system (Morgan, ¶[0019] Turing to FIG. 2, an embodiment is illustrated in which the row unit 200 is a planter row unit. The row unit 200 is preferably pivotally connected to the toolbar 14 by a parallel linkage 216. An actuator 218 is preferably disposed to apply lift and/or downforce on the row unit 200. A solenoid valve 390 is preferably in fluid communication with the actuator 218 for modifying the lift and/or downforce applied by the actuator.” Therefore, this is a downforce system and it is automated).
As per claims 9 and 19, Morgan in view of Sibley teaches, the method of claim 7, wherein the automated system is a row cleaner automation system (Morgan, ¶ [0011] “FIG. 7 illustrates a row unit incorporating an embodiment of an image capture apparatus.” Represents a row cleaner automation system ).
As per claims 10, 12, 13 and 20 Morgan in view of Sibley teaches, in view of Sibley teaches, the method of claim 7, wherein the automated system is a depth control automation system (Morgan, ¶ [0021] “Turning to FIG. 3, a depth control and soil monitoring system 300 is schematically illustrated.” This represents depth control automation system).
As per claim 11, Morgan in view of Sibley teaches, a method for evaluating quality of provided values for an agricultural machine (Morgan, fig.13 shows the method for evaluating accuracy for an agricultural machine of fig.1-2 ), comprising: obtaining a plurality of values for a time interval; processing the plurality of values by comparing each of the plurality of values to one or more other of the plurality of values (Morgan, fig.13 905 intervals represents the time intervals, 915 represents inadequate images as the alarm conditions represent something wrong and the values would be alarms); identifying the number of outliers in the plurality of values for the time interval; generating a value confidence for the time interval by comparing the number of outliers with the total number of plurality of values for the time interval; providing feedback indicating the value confidence (Morgan, ¶[0052] “The alarm threshold may comprise a selected constant value of the alarm value or a statistical function (e.g., one or more standard deviation above or below the mean or average) of the alarm value reported to the monitor during a preceding period or during operation in a specified area (e.g., 30 seconds, 30 feet of travel, the entire field associated with the operation).” along with fig.13 920 display row corresponding to row exhibition alarm condition, this would represent as a median would represents the outliers by doing the higher and the lower).
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.
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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