Patent Application 18467544 - AUTOMATIC ENCODER CALIBRATION SYSTEM FOR AN - Rejection

Patent Application 18467544 - AUTOMATIC ENCODER CALIBRATION SYSTEM FOR AN

Title: AUTOMATIC ENCODER CALIBRATION SYSTEM FOR AN AGRICULTURAL VEHICLE

Application Information

• Invention Title: AUTOMATIC ENCODER CALIBRATION SYSTEM FOR AN AGRICULTURAL VEHICLE
• Application Number: 18467544
• Submission Date: 2025-04-10T00:00:00.000Z
• Effective Filing Date: 2023-09-14T00:00:00.000Z
• Filing Date: 2023-09-14T00:00:00.000Z
• National Class: 701
• National Sub-Class: 050000
• Examiner Employee Number: 89775
• Art Unit: 3661
• Tech Center: 3600

Rejection Summary

• 102 Rejections: 0
• 103 Rejections: 1

Cited Patents

The following patents were cited in the rejection:

• US 0293304🔗
• US 0375450🔗

Office Action Text

    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 .
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Medagoda, et al., US 2019/0375450 A1, in view of Dang, et al., US 2017/0293304 A1.
As per Claim 1, Medagoda teaches an agricultural vehicle (¶ 26; vehicle 100 of Figure 1) comprising: 
a steering input device configured to steer the agricultural vehicle to perform a turn (¶ 25; through dual-mode controller 102 of Figure 1); 
a steering control system configured to operate the steering input device (¶ 25; “steering controller”), the steering control system comprising: 
processing circuitry configured to: receive a steering input indicating a curvature to be performed by the agricultural vehicle (¶ 80). 
Medagoda does not expressly teach operating the steering input device using (1) a primary curvature model of the agricultural vehicle and (2) the steering input; wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device.  Dang teaches operating the steering input device using (1) a primary curvature model (¶ 45) of the agricultural vehicle and (2) the steering input (¶ 44); wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device (¶¶ 45-47).  At the time of the invention, a person of skill in the art would have thought it obvious to operate the agricultural vehicle of Medagoda according to a curvature model such as Dang teaches, in order to improve tracking control as the vehicle travels along a pre-programmed path.
As per Claim 2, Medagoda teaches that the primary curvature model is generated following the steps of: 
obtaining steering condition data corresponding to the steering conditions of the steering input device (¶¶ 34-35; through sensors); and
obtaining curvature data corresponding to the curvatures of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3). 
Medagoda does not expressly teach: using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data; determining that the primary curvature model has converged; and operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature.  Dang teaches: 
using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data (¶¶ 31-32); 
determining that the primary curvature model has converged (¶ 54); and 
operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature (¶ 55).  
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 3, Medagoda teaches that the primary curvature model is a third- degree polynomial function (¶ 50; a “third order” polynomial).
As per Claim 4, Medagoda teaches that a calibration curvature model is generated following the steps of: 
operating the steering input device of the agricultural vehicle for the given commanded curvature using the primary curvature model (¶¶ 34-35; through sensors); and
obtaining the steering condition data of the steering input device and the curvature data of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3).  
Medagoda does not expressly teach using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data.  Dang teaches using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data (¶¶ 31-32).  See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 5, Medagoda teaches that the processing circuitry may adjust a calibration constant of the calibration curvature model (¶ 63); and that the processing circuitry is further configured to: 
determine if the steering condition is within a center range (¶¶ 86-87); and 
adjust a constant of the primary curvature model to correspond to the calibration constant of the calibration curvature model if the steering condition is within the center range (¶¶ 91-92).
As per Claim 6, Medagoda teaches that the processing circuitry is further configured to: determine if a deviation between the primary curvature model and the calibration curvature model exceeds a threshold (¶¶ 92-93); and perform at least one of generating an alert or stop operating the steering input device using (1) the primary curvature model of the agricultural vehicle and (2) the given commanded curvature if the deviation is above the threshold (¶¶ 93-94).
As per Claim 7, Medagoda teaches that the steering control system is a retrofit system that may be installed to control the steering input device of the agricultural vehicle (¶¶ 60-61).
As per Claim 8, Medagoda teaches a steering control system configured to operate a steering input device of an agricultural vehicle to perform a turn (¶ 31), the steering control system comprising; processing circuitry configured to: receive a steering input indicating a curvature to be performed by the agricultural vehicle (¶ 25; through dual-mode controller 102 of Figure 1).   
Medagoda does not expressly teach operating the steering input device using (1) a primary curvature model of the agricultural vehicle and (2) the steering input; wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device.  Dang teaches operating the steering input device using (1) a primary curvature model of the agricultural vehicle (¶ 45) and (2) the steering input (¶ 44); wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device (¶¶ 45-47).  See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 9, Medagoda teaches that the primary curvature model is generated following the steps of: 
obtaining steering condition data corresponding to the steering conditions of the steering input device (¶¶ 34-35; through sensors); 
obtaining curvature data corresponding to the curvatures of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3).  
Medagoda does not expressly teach: using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data; determining that the primary curvature model has converged; and operating the steering input device using (1) the primary curvature model and operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature.  Dang teaches: 
using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data (¶¶ 31-32); 
determining that the primary curvature model has converged (¶ 54); and 
operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature (¶ 55).  
See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 10, Medagoda teaches that the primary curvature model is a third- degree polynomial function (¶ 50; a “third order” polynomial).
As per Claim 11, Medagoda teaches that a calibration curvature model is generated following the steps of: 
operating the steering input device of the agricultural vehicle for the given commanded curvature using the primary curvature model (¶¶ 34-35; through sensors); 
obtaining the steering condition data of the steering input device and the curvature data of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3).  
Medagoda does not expressly teach using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data.  Dang teaches using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data (¶¶ 31-32).  See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 12, Medagoda teaches that the processing circuitry may adjust a calibration constant of the calibration curvature model (¶ 63); and that the processing circuitry is further configured to: 
determine if the steering condition is within a center range (¶¶ 86-87); and 
adjust a constant of the primary curvature model to correspond to the calibration constant of the calibration curvature model if the steering condition is within the center range (¶¶ 91-92).
As per Claim 13, Medagoda teaches that the processing circuitry is further configured to: 
determine if a deviation between the primary curvature model and the calibration curvature model exceeds a threshold (¶¶ 92-93); and 
perform at least one of generating an alert or stop operating the steering input device using (1) the primary curvature model of the agricultural vehicle and (2) the given commanded curvature if the deviation is above the threshold (¶¶ 93-94).
As per Claim 14, Medagoda teaches that the steering control system is a retrofit system that may be installed to control the steering input device of the agricultural vehicle (¶¶ 60-61).
As per Claim 15, Medagoda teaches a method for controlling a steering control system of an agricultural vehicle (¶¶ 37-38), the method comprising: receiving a steering input indicating a curvature to be performed by the agricultural vehicle (¶¶ 30-31).  
Medagoda does not expressly teach operating a steering input device of the agricultural vehicle using (1) a primary curvature model of the agricultural vehicle and (2) the steering input; wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device.  Dang teaches operating a steering input device of the agricultural vehicle using (1) a primary curvature model (¶ 45) of the agricultural vehicle and (2) the steering input (¶ 44); wherein the primary curvature model of the agricultural vehicle is configured to predict a steering condition of the steering input device to perform the steering input, the primary curvature model comprising a non-linear relationship between curvatures and steering conditions of the steering input device (¶¶ 45-47).  See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 16, Medagoda teaches that the primary curvature model is generated following the steps of: 
obtaining steering condition data corresponding to the steering conditions of the steering input device (¶¶ 34-35; through sensors); and
obtaining curvature data corresponding to the curvatures of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3). 
Medagoda does not expressly teach: using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data; determining that the primary curvature model has converged; and operating the steering input device using (1) the primary curvature model and operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature.  Dang teaches: 
using the steering condition data and the curvature data to obtain the primary curvature model that predicts steering condition data given commanded curvature data (¶¶ 31-32); 
determining that the primary curvature model has converged (¶ 54); and 
operating the steering input device using (1) the primary curvature model and (2) a given commanded curvature (¶ 55).
As per Claim 17, Medagoda teaches that the primary curvature model is a third-degree polynomial function (¶ 50; a “third order” polynomial).
As per Claim 18, Medagoda teaches that a calibration curvature model is generated following the steps of: 
operating the steering input device of the agricultural vehicle for the given commanded curvature using the primary curvature model (¶¶ 34-35; through sensors); and 
obtaining the steering condition data of the steering input device and the curvature data of the agricultural vehicle (¶¶ 30-32; along curved path 130 of Figure 3).  
Medagoda does not expressly teach using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data.  Dang teach using the steering condition data and the curvature data to obtain the calibration curvature model that predicts the steering condition data given the commanded curvature data (¶¶ 31-32).  See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 19, Medagoda teaches: determining if a deviation between the primary curvature model and the calibration curvature model exceeds a threshold (¶¶ 92-93); and performing at least one of generating an alert or stop operating the steering input device using (1) the primary curvature model of the agricultural vehicle and (2) the steering if the deviation is above the threshold (¶¶ 93-94).
As per Claim 20, Medagoda teaches: 
determining if the steering condition is within a center range (¶¶ 86-87); and 
adjusting a constant of the primary curvature model to correspond to a calibration constant of the calibration curvature model if the steering condition is within the center range (¶¶ 91-92).
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661



/ATUL TRIVEDI/Primary Examiner, Art Unit 3661