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 .
The current application is CON. of application No. 16/740109, now Pat. No.
11734917, and 16/188114, now Pat. No. 10788835, relates to provisional application
No. 62/585170 filed on Nov. 13, 2017.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/27/2026 has been entered.
Response to Arguments
Applicant's arguments filed 02/27/2026 have been fully considered and are moot in view new grounds of rejection. Examiner responds to the Applicant’s argument as the following reasons:
Regarding the 103 Rejection: At page 8-10Applicant argues that Foster and Rusciolelli do not teach the claimed amendment filed on 02/27/2026, and specific argues (at page 9) that Rusciolelli does not teach “detect an obstacle nor calculate, and determine a trajectory of an obstacle …”.
Examiner has reviewed the cited references and conducted a search for new prior art, identifying a reference by Madsen. Madsen is now combined with Rusciolelli to form a new rejection.
Rusciolelli teaches the vehicles 10 as shown in Fig.2 that includes sensors system for detecting both dynamic and static obstacles while traversing a field. The vehicles 10 report captured data to a base station 52, and receives an updated mission plan to control the vehicle’s actuator and agricultural machines’ operations.
The updated rejection is provided below.
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.
Claim(s) 2-3, 5-7 & 9-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rusciolelli (20170316692) in view of Madsen (20140324272).
With regard to claim 2, Rusciolelli discloses a method for obstacle identification and agricultural machine control (an agricultural vehicle 10 including an autonomous drive portion 12 and agricultural machinery 14, see [0027]+) comprising:
capturing one or more attributes of a detected obstacle with a first sensor of one or more sensors, the one or more attributes including at least one of a spatial attribute or a pixel attribute (the agricultural vehicles 10 comprises GPS, camera and other sensor for capturing and identifying detected obstacles in an agricultural field 50, see [0029] & [0031]+);
indexing the detected obstacle including indexing a trajectory of the detected obstacle based on at least one of the captured one or more attributes of the detected obstacle (the vehicles 10 are labeled A, B, C and D and static attributes labeled trees, or pond labeled water, see [0032]-[0033]+);
generating navigation controls for an agricultural machine based on the identified and indexed detected obstacle (a base station 52 includes a mission plan 150 provides a mission plan (path) for an autonomous vehicle 10 with the agricultural machine, based on data structures received from other vehicles 10, see [0031]+ & [0041]+) ;
delivering the navigation controls to the agricultural machine through a vehicle control interface (the mission plan 150 is communicated to the vehicle 10 for controlling the agricultural machine operating within a field 50, [0031]-[0033]+); and
modifying an initial path plan of the agricultural machine, modifying the initial path plan including: calculating a new route based on the generated navigation controls; and refining the initial path plan to an updated path plan having the new route (the base station 52 includes a mission revision 156 which provides from time to time to update one or more portions of the mission plan 150 based upon receiving an event condition being tracked in the event log 154, adjusts paths of the vehicles 10 to resolve event conditions being monitored, see [0041]-[0042]).
Rusciolelli fails to teach identifying, using a trained artificial intelligence component, the detected obstacle based on the one or more attributes of the detected obstacle compared with training attributes of the trained artificial intelligence component, the training attributes including image characteristics used for training the trained artificial intelligence component.
Madsen discloses an operating system for operating an automatic guidance system of an agricultural vehicle (see the abstract). The system includes a 3D imaging device 24 for capturing real objects in front of the agricultural vehicle 12. The 3D imaging device 24 comprises a processing unit 32 visualized processes the captured information into a displayable and visually recognizable form (the 3D data set) which compared with reference data 34 stored in a memory, for recognizing the real objects allocated, see [0062]-[0063]+ which meets the scope of “identifying, using a trained artificial intelligence component, the detected obstacle based on the one or more attributes of the detected obstacle compared with training attributes of the trained artificial intelligence component, the training attributes including image characteristics used for training the trained artificial intelligence component”, wherein the processing unit 32 visualized processing through data visualization and visual analytics that is equivalent to an intelligent part of machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Rusciolelli by including the 3D imaging device 24 comprises a processing unit 32 visualized processes the captured information into a displayable and visually recognizable form (the 3D data set) which compared with reference data 34 stored in a memory, for recognizing the real objects allocated as taught by Madsen for improving the operation of the agricultural machine.
With regard to claim 3, Rusciolelli teaches that the method of claim 2, wherein the one or more sensors includes at least one of an RGB camera, a thermographic camera, a radar sensor, LiDAR sensor, sonar sensor, ultrasound, time of flight sensor, or GPS receiver (see [0029]+).
With regard to claim 5, Rusciolelli teaches that the method of claim 2, wherein modifying the initial path plan includes modifying the updated path plan including calculating of another new route based on second generated navigation controls (providing optimizations in subsequent mission revisions, see [0047]+).
With regard to claim 6, Rusciolelli teaches that the method of claim 2, wherein calculating the new route is based on the identified detected obstacle and one or more of heading of the agricultural machine, position of the agricultural machine, or operational characteristics of the agricultural machine (during execution of a mission, there may be events which cause deviations from the initial mission plane, a revised mission plan be provided, see [0048]-[0049]+).
With regard to claim 7, Madsen teaches that the method of claim 2, wherein the pixel attribute includes at least one of shape, brightness, color, edges, pixel grouping, variation in pixel intensity, or temperature, and the spatial attribute includes at least one of range, range-rate, reflectivity, or bearing of the detected obstacle (displaying objects with its shape and dimensions, see [0014] and the real object is calculated by determining the position, distance and bearing, see [0011]).
With regard to claims 9-10, Rusciolelli teaches that the method of claim 2, wherein generating the navigation controls for the agricultural machine includes generating one or more of steering control, speed control, brake control, gear control, or mode control (the vehicle operates autonomously of sensing environment,, driving, steering, stopping and otherwise operating without direct human input, see [0027]-[0030])+.
With regard to claim 11, Madsen teaches that the method of claim 2, further comprising: indexing the detected obstacle including indexing a position and movement of the detected obstacle based on at least one of the one or more attributes of the detected obstacle (Fig. 2 shown displayed objects 40 such as a person moving, a shape and boundary of swath, and etc., see [0063]+).
With regard to claim 12, Madsen teaches that the method of claim 11, wherein identifying the detected obstacle includes identifying the detected obstacle based on a first attribute of the one or more attributes; and indexing the detected obstacle includes indexing the detected obstacle based on the first attribute for identifying the detected obstacle (the displayed objects 40 are manipulable by touch input for selecting and indexing for guiding the vehicle around the obstacles, see [0064]+).
With regard to claim 13, Rusciolelli teaches that the method of claim 2, wherein capturing the one or more attributes of the detected obstacle includes capturing at least one attribute of the one or more attributes in multiple fields of view around the agricultural machine and at least another attribute of the one or more attributes in a forward-facing direction of travel of the agricultural machine (while the vehicles 10 are conducting their agricultural operations, they each provides progress information (events condition) to the base stations 52 which monitors the conditions and reports back to the vehicle with revised mission plans as necessary, see [0035]-[0036]+).
With regard to claim 14, Madsen teaches that the method of claim 2, further comprising: retraining the trained artificial intelligence component with the captured one or more attributes of the detected obstacle (the 3D imaging device includes a processing unit which derives a 3D data set and 3D range image for the captured real object, see [0010] & allocates and evaluates the received feedback corresponding to the 3D dimensional data set, see [0015]+).
With regard to claim 15, Rusciolelli discloses an obstacle identification and agricultural machine control system comprising:
a first sensor of one or more sensors configured to capture one or more attributes of a detected obstacle, the one or more attributes of the detected obstacle including at least one of a spatial attribute or a pixel attribute;
a framework including one or more processors in communication with the first sensor, the framework includes:
an obstacle kinematics module configured to index the detected obstacle based on at least one of the captured one or more attributes of the detected obstacle (a process monitors 152 periodically updated information received from vehicles, see [0041], wherein the updating includes the vehicles 10 are labeled A, B, C and D and static attributes labeled trees, or pond labeled water, see [0032]-[0033]+),
wherein indexing the detected obstacle includes calculating and determining a trajectory of the detected obstacle (vehicles 10 with labeled A, B, C, D are monitored and tracked based on the vehicles’ reporting their information such as position, speed, acceleration, and etc., see [0041] & [0029]+) ; and
a navigation controller in communication with an obstacle data processing module, the navigation controller includes: a vehicle control interface configured for coupling with an agricultural machine steering; and wherein the navigation controller is configured to deliver navigation controls to the agricultural machine steering through the vehicle control interface, the navigation controls are based on the identified and indexed detected obstacle (a base station 52 includes a mission plan 150 provides a mission plan (path) for an autonomous vehicle 10 with the agricultural machine such as operating the drive system 36, and the agricultural operation control 38, based on data structures received from other vehicles 10, see [0030]- [0031]+ & [0041]+).
Rusciolelli fails to teach an artificial intelligence component generating training attributes; an obstacle recognition module in communication with the artificial intelligence component, the obstacle recognition module configured to identify the detected obstacle; wherein identification of the detected obstacle is based on the one or more attributes of the detected obstacle compared with the training attributes generated by the artificial intelligence component.
Madsen discloses an operating system for operating an automatic guidance system of an agricultural vehicle (see the abstract). The system includes a 3D imaging device 24 for capturing real objects in front of the agricultural vehicle 12. The 3D imaging device 24 comprises a processing unit 32 visualized processes the captured information into a displayable and visually recognizable form (the 3D data set) which compared with reference data 34 stored in a memory, for recognizing the real objects allocated, see [0062]-[0063]+ which meets the scope of “an artificial intelligence component generating training attributes; an obstacle recognition module in communication with the artificial intelligence component, the obstacle recognition module configured to identify the detected obstacle; wherein identification of the detected obstacle is based on the one or more attributes of the detected obstacle compared with the training attributes generated by the artificial intelligence component”, wherein the processing unit 32 visualized processing through data visualization and visual analytics that is equivalent to an intelligent part of machine learning.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify Rusciolelli by including the 3D imaging device 24 comprises a processing unit 32 visualized processes the captured information into a displayable and visually recognizable form (the 3D data set) which compared with reference data 34 stored in a memory, for recognizing the real objects allocated as taught by Madsen for improving the operation of the agricultural machine.
With regard to claim 16, Rusciolelli teaches that the control system of claim 15, wherein the one or more sensors includes at least one of an RGB camera, a thermographic camera, a radar sensor, LiDAR sensor, sonar sensor, ultrasound, time of flight sensor, or GPS receiver (see [0029]+).
With regard to claim 17, Rusciolelli teaches that the control system of claim 15, further comprising: an initialization component configured to set one or more fields of view for each sensor of the one or more sensors, the one more field of view are based on at least one of a weather condition, an expected weather condition, an agricultural machine type, an agricultural machine configuration, known obstacles, or known terrain characteristic (a mission plan for execution in an agricultural field 50 using vehicles 10 with predetermined entry points 54, operated on paths 60, see [0031]-[0035]+).
With regard to claim 18, Rusciolelli teaches that the control system of claim 15, wherein the navigation controller includes a path planning module configured to modify an initial path plan of an agricultural machine, modify the initial path plan includes: calculate a new route based on the generated navigation controls; and refine the initial path plan to an updated path plan having the new route (the base station 52 includes a mission revision 156 which provides from time to time to update one or more portions of the mission plan 150 based upon receiving an event condition being tracked in the event log 154, adjusts paths of the vehicles 10 to resolve event conditions being monitored, see [0041]-[0042]).
With regard to claim 19, Rusciolelli teaches that the control system of claim 18, wherein modify the initial path plan further comprises modify the updated path plan including calculate a second new route based on second generated navigation controls (providing optimizations in subsequent mission revisions, see [0047]+).
With regard to claim 20, Rusciolelli teaches that the control system of claim 18, wherein calculate the new route is based on the identified detected obstacle and one or more of heading of the agricultural machine, position of the agricultural machine, or operational characteristics (during execution of a mission, there may be events which cause deviations from the initial mission plane, a revised mission plan be provided, see [0048]-[0049]+).
With regard to claim 21, Rusciolelli teaches that the control system of claim 15, wherein the navigation controls for an agricultural machine includes one or more of a steering control, a speed control, a brake control, a gear control, or a mode control (the vehicle operates autonomously of sensing environment, driving, steering, stopping and otherwise operating without direct human input, see [0027]-[0030])+.
With regard to claim 22, Madsen teaches that the control system of claim 15, further comprising: the obstacle kinematics module configured to index a position and a movement of the detected obstacle based on at least one of the one or more attributes of the detected obstacle (the displayed objects 40 are interactively manipulable by touch input, see [0064]+).
With regard to claim 23, Madsen teaches that the control system of claim 15, wherein the artificial intelligence component is configured to train itself with the captured one or more attributes of the detected obstacle (the 3D imaging device includes a processing unit which derives a 3D data set and 3D range image for the captured real object, see [0010] & allocates and evaluates the received feedback corresponding to the 3D dimensional data set, see [0015]+).
With regard to claim 24, Madsen teaches that the control system of claim 15, wherein the artificial intelligence component includes one or more of a k-nearest neighbor, logistic regression, support vector machine, or neural network (a processing unit of the 3D imaging device, derives the captured 3D data set and range image for the objects to generate a displayable 2D and/or 3D image to interact with, see [0010] is equivalent a logistic regression model).
With regard to claim 25, Madsen teaches that the control system of claim 15, wherein the pixel attribute includes at least one of shape, brightness, color, edges, pixel grouping, variation in pixel intensity, or temperature, and the spatial attribute includes at least one of range, range-rate, reflectivity, or bearing of the detected obstacle (displaying objects with its shape and dimensions, see [0014] and the real object is calculated by determining the position, distance and bearing, see [0011]).
Conclusion
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NGA X. NGUYEN
Examiner
Art Unit 3662
/NGA X NGUYEN/Primary Examiner, Art Unit 3662
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