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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The disclosure is objected to because of the following informalities: On page 11 line 5, the word “mad” is grammatically incorrect and should be “made”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ENGEL (EP 3981244 A1).
Regarding Claim 1, Engel teaches A method of controlling an agricultural harvester, the method comprising: receiving past sensor signals from a field sensor of a first agricultural vehicle, the past sensor signals representing a field or crop property at specified locations in an agricultural field (See at least paragraph [0004], “One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field.”), while harvesting, receiving real-time sensor signals from a field sensor of the agricultural harvester, the real-time sensor signals representing the field or crop property at a current location in the agricultural field (See at least paragraph [0004], “An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field.”), based on the past sensor signals and the real-time sensor signals, determining a field prediction representing the field or crop property at a future location in the agricultural field (See at least paragraph [0004], “A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor.”), and based on the field prediction, controlling an operational parameter of the agricultural harvester (See at least paragraph [0004], “The predictive map can be output and used in automated machine control.”).
Regarding Claim 2, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches wherein the field sensor of the first agricultural vehicle and the field sensor of the agricultural harvester are radar sensors (See at least paragraph [0073], “In-situ sensors 208 illustratively include a biomass sensor, such as biomass sensor 336, as well as a processing system 338. In some instances, biomass sensor 336 may be located on-board of the agricultural harvester 100. The processing system 338 processes sensor data generated from on-board biomass sensor 336 to generate processed data, some examples of which are described below” and paragraph [0074], “In some examples, biomass sensor 336 may be an optical sensor, such as a camera, a stereo camera, a mono camera, lidar, or radar, that generates images of an area of a field to be harvested.”).
Regarding Claim 3, Engel teaches The method of controlling an agricultural harvester of claim 2, as set forth in the anticipation rejection above. Engel teaches wherein the field prediction representing the field or crop property at a future location in the agricultural field is based on the past and real-time radar signals, and on an additional sensor signal from an additional sensor of the first agricultural vehicle and/or the agricultural harvester (See at least paragraph [0074] and paragraph [0075], “In-situ sensor 208 may be or include other types of sensors, such as a camera located along a path by which severed vegetation material travels in agricultural harvester 100 (referred to hereinafter as "process camera")…In other examples, in-situ sensor 208 may include a material distribution sensor that measures the volume or mass of material at two or more locations. The measurements may be absolute or relative. In some examples, electromagnetic or ultrasonic sensors may be used to measure time of flight, phase shift, or binocular disparities of one or more signals reflected by material surfaces at distances relative to a reference surface.”).
Regarding Claim 4, Engel teaches The method of controlling an agricultural harvester of claim 3, as set forth in the anticipation rejection above. Engel teaches wherein the additional sensor is a camera (See at least paragraph [0075], “In-situ sensor 208 may be or include other types of sensors, such as a camera located along a path by which severed vegetation material travels in agricultural harvester 100 (referred to hereinafter as "process camera").”).
Regarding Claim 5, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches further comprising a step of using the field sensor of the first agricultural vehicle to obtain the real-time sensor signals (See at least paragraph [0031], “The in-situ sensors 208 include, for example, on-board sensors 222, remote sensors 224, and other sensors 226 that sense characteristics of a field during the course of an agricultural operation” and paragraph [0053], “Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of a characteristic, for example, a vegetation characteristic, such as biomass or a biomass characteristic, as indicated by block 288. Examples of in-situ sensors 208 are discussed with respect to blocks 222, 290, and 226. As explained above, the in-situ sensors 208 include on-board sensors 222; remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data, shown in block 290; or other types of in-situ sensors, designated by in-situ sensors 226. In some examples, data from on-board sensors is georeferenced using position, heading, or speed data from geographic position sensor 204.”).
Regarding Claim 6, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches wherein the field or crop property comprises a ground profile, a crop height, a grain height, a weed density or a crop density (See at least paragraph [0029], “Prior to describing how agricultural harvester 100 generates a functional predictive biomass map and uses the functional predictive biomass map for control, a brief description of some of the items on agricultural harvester 100 and their respective operations will first be described. The description of FIG. 2 and 3 describe receiving a general type of prior information map and combining information from the prior information map with a georeferenced sensor signal generated by an in-situ sensor, where the sensor signal is indicative of a characteristic in the field, such as characteristics of crop present in the field. Characteristics of the field may include, but are not limited to, characteristics of a field such as slope, weed intensity, weed type, soil moisture, surface quality; characteristics of vegetation properties, such as vegetation height, vegetation volume, vegetation moisture, vegetation mass, and vegetation density; characteristics of crop properties, such as crop height, crop volume, crop moisture, crop mass, crop density, and crop state; characteristics of grain properties such as grain moisture, grain size, grain test weight; and characteristics of machine performance such as loss levels, job quality, fuel consumption, and power utilization.”).
With respect to claim 14, please see the rejection above with respect to claim 6, which is commensurate in scope to claim 14, with claim 6 being drawn to a method of controlling an agricultural harvester and claim 14 being drawn to a corresponding system.
Regarding Claim 7, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches wherein the operational parameter comprises a driving speed, a header setting, a threshing setting, or a cleaning setting of the agricultural harvester (See at least paragraph [0028], “In one example, various machine settings can be set or controlled to achieve a desired performance. The machine settings can include such things as concave clearance, rotor speed, sieve and chaffer settings, and cleaning fan speed. Other machine settings can also be controlled…The machine speed, as well as various other machine settings, such as header height, can be controlled based on the estimated biomass to maintain the desired throughput.”).
With respect to claim 15, please see the rejection above with respect to claim 7, which is commensurate in scope to claim 15, with claim 7 being drawn to a method of controlling an agricultural harvester and claim 15 being drawn to a corresponding system.
Regarding Claim 8, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches wherein the first agricultural vehicle is a sprayer or a weeder (See at least paragraph [0045], “In another example, the prior information map 258 may be a weed intensity map generated during a prior operation, such as from a sprayer, and the variable sensed by the in-situ sensors 208 may be weed intensity.”).
Regarding Claim 9, Engel teaches The method of controlling an agricultural harvester of claim 1, as set forth in the anticipation rejection above. Engel teaches wherein the first agricultural vehicle is an autonomous agricultural vehicle (See at least paragraph [0053], “As explained above, the in-situ sensors 208 include on-board sensors 222; remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data, shown in block 290; or other types of in-situ sensors, designated by in-situ sensors 226. In some examples, data from on-board sensors is georeferenced using position, heading, or speed data from geographic position sensor 204.”).
Regarding Claim 10, Engel teaches The method of controlling an agricultural harvester of claim 1, wherein the agricultural harvester is a combine harvester (See at least paragraph [0015], “FIG. 1 is a partial pictorial, partial schematic, illustration of a self-propelled agricultural harvester 100. In the illustrated example, agricultural harvester 100 is a combine harvester.”).
With respect to claim 16, please see the rejection above with respect to claim 10, which is commensurate in scope to claim 16, with claim 10 being drawn to a method of controlling an agricultural harvester and claim 16 being drawn to a corresponding system.
Regarding Claim 11, Engel teaches A non-transitory, computer-readable storage medium storing instructions thereon that when executed by one or more processors cause the one or more processors to execute the method claim 1 (See at least paragraph [0199], “Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor 17 may be activated by other components to facilitate their functionality as well.”): Regarding Claim 1, Engel teaches A method of controlling an agricultural harvester, the method comprising: receiving past sensor signals from a field sensor of a first agricultural vehicle, the past sensor signals representing a field or crop property at specified locations in an agricultural field (See at least paragraph [0004], “One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field.”), while harvesting, receiving real-time sensor signals from a field sensor of the agricultural harvester, the real-time sensor signals representing the field or crop property at a current location in the agricultural field (See at least paragraph [0004], “An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field.”), based on the past sensor signals and the real-time sensor signals, determining a field prediction representing the field or crop property at a future location in the agricultural field (See at least paragraph [0004], “A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor.”), and based on the field prediction, controlling an operational parameter of the agricultural harvester (See at least paragraph [0004], “The predictive map can be output and used in automated machine control.”).
Regarding Claim 12, Engel teaches An agricultural harvester comprising a field sensor for generating real-time sensor signals representing a field or crop property at a current location in an agricultural field and a controller, operatively coupled to the field sensor and configured to: receive past sensor signals from a field sensor of a first agricultural vehicle, the past sensor signals representing the field or crop property at specified locations in the agricultural field (See at least paragraph [0004], “One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.”), while harvesting, receive the real-time sensor signals generated by the field sensor of the agricultural harvester(See at least paragraph [0004], “An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field.”) , based on the past sensor signals and the real-time sensor signals, determine a field prediction representing the field or crop property at a future location in the agricultural field (See at least paragraph [0004], “A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor.”), and based on the field prediction, control an operational parameter of the agricultural harvester (See at least paragraph [0004], “The predictive map can be output and used in automated machine control.”).
Regarding Claim 13, Engel teaches The agricultural harvester of claim 12, as set forth in the anticipation rejection above. Engel teaches wherein the field sensor of the agricultural harvester is a radar sensor (See at least paragraph [0074], “In some examples, biomass sensor 336 may be an optical sensor, such as a camera, a stereo camera, a mono camera, lidar, or radar, that generates images of an area of a field to be harvested.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEWEL ASHLEY KUNTZ whose telephone number is (571)270-5542. The examiner can normally be reached M-F 8:30am-5:30pm.
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/JEWEL A KUNTZ/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666