Patent Grant
11889787
The present description relates to agricultural machines, forestry machines, construction machines and turf management machines.
There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvester can also be fitted with different types of heads to harvest different types of crops.
A variety of different conditions in fields have a number of deleterious effects on the harvesting operation. Therefore, an operator may attempt to modify control of the harvester, upon encountering such conditions during the harvesting operation.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.
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.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background.
For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.
The present description relates to using in-situ data taken concurrently with an agricultural operation, in combination with prior data, to generate a predictive map and, more particularly, a predictive speed map. In some examples, the predictive speed map can be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, it may improve the performance of the agricultural harvester to control the speed of the agricultural harvester when the agricultural harvester engages different conditions in the field. For instance, if the crops have reached maturity, the weeds may still be green, thus increasing the moisture content of the biomass that is encountered by the agricultural harvester. This problem may be exacerbated when the weed patches are wet (such as shortly after a rainfall or when weed patches contain dew) and before the weeds have had a chance to dry. Thus, when the agricultural harvester encounters an area of increased biomass, the operator may slow the speed of the agricultural harvester to maintain a constant feed rate of material through the agricultural harvester. Maintaining a constant feed rate may maintain the performance of the agricultural harvester.
Performance of an agricultural harvester may be deleteriously affected based on a number of different criteria. Such different criteria may include changes in biomass, crop state, topography, soil properties, and seeding characteristics, or other conditions. Therefore, it may also be useful to control the speed of the agricultural harvester based on other conditions that may be present in the field. For example, the performance of the agricultural harvester may be maintained at an acceptable level by controlling the speed of the agricultural harvester based on the biomass encountered by the agricultural harvester, the crop state of the crop being harvested, the topography of the field being harvested, soil properties of soil in the field being harvested, seeding characteristics in the field being harvested, yield in the field being harvested, or other conditions that are present in the field.
Some current systems provide vegetative index maps. A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across different geographic locations in a field of interest. One example of a vegetative index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum.
A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps enable vegetation to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or other weeds.
In some examples, a biomass map is provided. A biomass map illustratively maps a measure of biomass in the field being harvested at different locations in the field. A biomass map may be generated from vegetative index values, from historically measured or estimated biomass levels, from images or other sensor readings taken during a previous operation in the field, or in other ways. In some examples, biomass may be adjusted by a factor representing a portion of total biomass passing through the agricultural harvester. For corn, this factor is typically around 50%. In some examples, this factor may vary based on crop moisture. In some examples, the factor may represent a portion of weed material or weed seeds. In some examples, the factor may represent a portion of one crop in an intercrop mix.
In some examples, a crop state map is provided. Crop state may define whether the crop is down, standing, partially down, the orientation of down or partially down crop relative to the ground surface or to a compass direction, and other things. A crop state map illustratively maps the crop state in the field being harvested at different locations in the field. A crop state map may be generated from aerial or other images of the field, from images or other sensor readings taken during a prior operation in the field or in other ways prior to harvesting.
In some examples, a seeding map is provided. A seeding map may map seeding characteristics such as seed locations, seed variety, or seed population to different locations in the field. The seeding map may be generated during a past seed planting operation in the field. The seeding map may be derived from control signals used by a seeder when planting seeds or from sensors on the seeder that confirm that a seed was metered or planted. Seeders may also include geographic position sensors that geolocate the seed characteristics on the field.
In some examples, a soil property map is provided. A soil property map illustratively maps a measure of one or more soil properties such as soil type, soil chemical constituents, soil structure, residue coverage, tillage history, or soil moisture in the field being harvested at different locations in the field. A soil properties map may be generated from vegetative index values, from historically measured or estimated soil properties, from images or other sensor readings taken during a previous operation in the field, or in other ways.
In some examples, other information maps are provided. Such information maps can include a topographic map of the field being harvested, a predictive yield map for the field being harvested, or other information maps.
The present discussion thus proceeds with respect to systems that receive an information map of a field or map generated during a prior operation and also use an in-situ sensor to detect a variable indicative of one or more of a machine speed and an output from a feed rate control system. The systems generate a model that models a relationship between the information values on the information map and the output values from the in-situ sensor. The model is used to generate a functional predictive speed map that predicts, for example, an expected machine speed at different locations in the field. The functional predictive speed map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling an agricultural harvester during the harvesting operation, or both.
As shown in
Thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, agricultural harvester 100 also includes a separator 116. Agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve 124. The material handling subsystem 125 also includes discharge beater 126, tailings elevator 128, clean grain elevator 130, as well as unloading auger 134 and spout 136. The clean grain elevator moves clean grain into clean grain tank 132. Agricultural harvester 100 also includes a residue subsystem 138 that can include chopper 140 and spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester 100 may have left and right cleaning subsystems, separators, etc., which are not shown in
In operation, and by way of overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As agricultural harvester 100 moves, header 102 (and the associated reel 164) engages the crop to be harvested and gathers the crop toward cutter 104. An operator of agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header 102. For example, the operator inputs a setting or settings to a control system, described in more detail below, that controls actuator 107. The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of the header 102 and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of the header 102. The actuator 107 maintains header 102 at a height above ground 111 based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height of header 104 above ground 111 and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity.
Returning to the description of the operation of agricultural harvester 100, after crops are cut by cutter 104, the severed crop material is moved through a conveyor in feeder house 106 toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop material is threshed by rotor 112 rotating the crop against concaves 114. The threshed crop material is moved by a separator rotor in separator 116 where a portion of the residue is moved by discharge beater 126 toward the residue subsystem 138. The portion of residue transferred to the residue subsystem 138 is chopped by residue chopper 140 and spread on the field by spreader 142. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, the residue subsystem 138 can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.
Grain falls to cleaning subsystem 118. Chaffer 122 separates some larger pieces of material from the grain, and sieve 124 separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end of clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upwards, depositing the clean grain in clean grain tank 132. Residue is removed from the cleaning subsystem 118 by airflow generated by cleaning fan 120. Cleaning fan 120 directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly in agricultural harvester 100 toward the residue handling subsystem 138.
Tailings elevator 128 returns tailings to thresher 110 where the tailings are re-threshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well.
Machine speed sensor 146 senses the travel speed of agricultural harvester 100 over the ground. Machine speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axel, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of travel speed.
Loss sensors 152 illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, sensors 152 are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at the cleaning subsystem 118. The strike sensors for the right and left sides of the cleaning subsystem 118 may provide individual signals or a combined or aggregated signal. In some examples, sensors 152 may include a single sensor as opposed to separate sensors provided for each cleaning subsystem 118. Separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, not separately shown in
Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111; stability sensors that sense oscillation or bouncing motion (and amplitude) of agricultural harvester 100; a residue setting sensor that is configured to sense whether agricultural harvester 100 is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of fan 120; a concave clearance sensor that senses clearance between the rotor 112 and concaves 114; a threshing rotor speed sensor that senses a rotor speed of rotor 112; a chaffer clearance sensor that senses the size of openings in chaffer 122; a sieve clearance sensor that senses the size of openings in sieve 124; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor that senses the orientation of agricultural harvester 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. For example, in some instances, the crop property sensors may sense grain quality such as broken grain, MOG levels; grain constituents such as starches and protein; and grain feed rate as the grain travels through the feeder house 106, clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors may also sense the feed rate of biomass through feeder house 106, through the separator 116 or elsewhere in agricultural harvester 100. The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables.
Prior to describing how agricultural harvester 100 generates a functional predictive speed map, and uses the functional predictive speed map for control, a brief description of some of the items on agricultural harvester 100, and their operation, will first be described. The description of
After the general approach is described with respect to
Information map 258 may be downloaded onto agricultural harvester 100 and stored in data store 202, using communication system 206 or in other ways. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations of networks. Communication system 206 may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.
Geographic position sensor 204 illustratively senses or detects the geographic position or location of agricultural harvester 100. Geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor 204 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.
In-situ sensors 208 may be any of the sensors described above with respect to
Predictive model generator 210 generates a model that is indicative of a relationship between the values sensed by the in-situ sensor 208 and a metric mapped to the field by the information map 258. For example, if the information map 258 maps a vegetative index value to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of machine speed, then information variable-to-in-situ variable model generator 228 generates a predictive speed model that models the relationship between the vegetative index value and the machine speed value. The predictive speed model can also be generated based on vegetative index values from the information map 258 and multiple in-situ data values generated by in-situ sensors 208. Then, predictive map generator 212 uses the predictive speed model generated by predictive model generator 210 to generate a functional predictive speed map that predicts the expected machine speed sensed by the in-situ sensors 208 at different locations in the field based upon the information map 258.
In some examples, the type of values in the functional predictive map 263 may be the same as the in-situ data type sensed by the in-situ sensors 208. In some instances, the type of values in the functional predictive map 263 may have different units from the data sensed by the in-situ sensors 208. In some examples, the type of values in the functional predictive map 263 may be different from the data type sensed by the in-situ sensors 208 but have a relationship to the type of data type sensed by the in-situ sensors 208. For example, in some examples, the data type sensed by the in-situ sensors 208 may be indicative of the type of values in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 may be different than the data type in the information map 258. In some instances, the type of data in the functional predictive map 263 may have different units from the data in the information map 258. In some examples, the type of data in the functional predictive map 263 may be different from the data type in the information map 258 but has a relationship to the data type in the information map 258. For example, in some examples, the data type in the information map 258 may be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one of, or both of the in-situ data type sensed by the in-situ sensors 208 and the data type in the information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors 208 and the data type in information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the in-situ data type sensed by the in-situ sensors 208 or the data type in the information map 258, and different than the other.
Continuing with the preceding example, in which information map 258 is a vegetative index map and in-situ sensor 208 senses a value indicative of machine speed, predictive map generator 212 can use the vegetative index values in information map 258 and the model generated by predictive model generator 210 to generate a functional predictive map 263 that predicts the expected machine speed at different locations in the field. Predictive map generator 212 thus outputs predictive map 264.
As shown in
Some variations in the data types that are mapped in the information map 258, the data types sensed by in-situ sensors 208, and the data types predicted on the predictive map 264 will now be described.
In some examples, the data type in the information map 258 is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In another example, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop height. The predictive map 264 may then be a predictive crop height map that maps predicted crop height values to different geographic locations in the field.
Also, in some examples, the data type in the information map 258 is different from the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is different from both the data type in the information map 258 and the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop height. The predictive map 264 may then be a predictive biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive speed map that maps predicted harvester speed values to different geographic locations in the field.
In some examples, the information map 258 is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a seed population map generated during planting, and the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the information map 258 may be a seeding hybrid map, and the variable sensed by the in-situ sensors 208 may be crop state such as standing crop or down crop. The predictive map 264 may then be a predictive crop state map that maps predicted crop state values to different geographic locations in the field.
In some examples, the information map 258 is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is also the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 210 to generate a predictive yield map.
In another example, the 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. The predictive map 264 may then be a predictive weed intensity map that maps predicted weed intensity values to different geographic locations in the field. In such an example, a map of the weed intensities at time of spraying is geo-referenced recorded and provided to agricultural harvester 100 as an information map 258 of weed intensity. In-situ sensors 208 can detect weed intensity at geographic locations in the field and predictive model generator 210 may then build a predictive model that models a relationship between weed intensity at time of harvest and weed intensity at time of spraying. This is because the sprayer will have impacted the weed intensity at time of spraying, but weeds may still crop up in similar areas again by harvest. However, the weed areas at harvest are likely to have different intensity based on timing of the harvest, weather, weed type, among other things.
In some examples, predictive map 264 can be provided to the control zone generator 213. Control zone generator 213 groups adjacent portions of an area into one or more control zones based on data values of predictive map 264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems 216 may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map 264. In that case, control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216. In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones may be added to the predictive map 264 to obtain predictive control zone map 265. Predictive control zone map 265 can thus be similar to predictive map 264 except that predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263, as described herein, may or may not include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, a functional predictive map 263 does not include control zones, such as predictive map 264. In another example, a functional predictive map 263 does include control zones, such as predictive control zone map 265. In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case, predictive map generator 212 and control zone generator 213 are able to identify the location and characteristics of the two or more crops and then generate predictive map 264 and predictive control zone map 265 accordingly.
It will also be appreciated that control zone generator 213 can cluster values to generate control zones and the control zones can be added to predictive control zone map 265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating agricultural harvester 100 or both. In other examples, the control zones may be presented to the operator 260 and used to control or calibrate agricultural harvester 100, and, in other examples, the control zones may be presented to the operator 260 or another user or stored for later use.
Predictive map 264 or predictive control zone map 265 or both are provided to control system 214, which generates control signals based upon the predictive map 264 or predictive control zone map 265 or both. In some examples, communication system controller 229 controls communication system 206 to communicate the predictive map 264 or predictive control zone map 265 or control signals based on the predictive map 264 or predictive control zone map 265 to other agricultural harvesters that are harvesting in the same field. In some examples, communication system controller 229 controls the communication system 206 to send the predictive map 264, predictive control zone map 265, or both to other remote systems.
Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218. The operator interface controller 231 is also operable to present the predictive map 264 or predictive control zone map 265 or other information derived from or based on the predictive map 264, predictive control zone map 265, or both to operator 260. Operator 260 may be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 for the operator 260. Controller 231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a weed type displayed on the map, based on the operator's observation. Settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based upon predictive map 264, the predictive control zone map 265, or both. For instance, settings controller 232 can generate control signals to control machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control and other actuators 248 that affect the other functions of the agricultural harvester 100. Path planning controller 234 illustratively generates control signals to control steering subsystem 252 to steer agricultural harvester 100 according to a desired path. Path planning controller 234 can control a path planning system to generate a route for agricultural harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Feed rate controller 236 may receive a variety of different inputs indicative of a feed rate of material through agricultural harvester 100 and can control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control the feed rate based upon the predictive map 264 or predictive control zone map 265 or both. For instance, as agricultural harvester 100 approaches a weed patch having an intensity value above a selected threshold, feed rate controller 236 may generate a control signal to control propulsion subsystem 252 to reduce the speed of agricultural harvester 100 to maintain constant feed rate of biomass through the agricultural harvester 100. Header and reel controller 238 can generate control signals to control a header or a reel or other header functionality. Draper belt controller 240 can generate control signals to control a draper belt or other draper functionality based upon the predictive map 264, predictive control zone map 265, or both. Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header based on predictive map 264 or predictive control zone map 265 or both, and residue system controller 244 can generate control signals to control a residue subsystem 138 based upon predictive map 264 or predictive control zone map 265, or both. Machine cleaning controller 245 can generate control signals to control machine cleaning subsystem 254. For instance, based upon the different types of seeds or weeds passed through agricultural harvester 100, a particular type of machine cleaning operation or a frequency with which a cleaning operation is performed may be controlled. Other controllers included on the agricultural harvester 100 can control other subsystems based on the predictive map 264 or predictive control zone map 265 or both as well.
At 280, agricultural harvester 100 receives information map 258. Examples of information map 258 or receiving information map 258 are discussed with respect to blocks 281, 282, 284 and 286. As discussed above, information map 258 maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated at block 282. As indicated at block 281, receiving the information map 258 may involve selecting one or more of a plurality of possible information maps that are available. For instance, one information map may be a vegetative index map generated from aerial imagery. Another information map may be a map generated during a prior pass through the field which may have been performed by a different machine performing a previous operation in the field, such as a sprayer or a planting machine or seeding machine or unmanned aerial vehicle (UAV) or other machine. The process by which one or more information maps are selected can be manual, semi-automated, or automated. The information map 258 is based on data collected prior to a current harvesting operation. This is indicated by block 284. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current growing season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, agricultural harvester 100 may be fitted with a sensor, such as an internal optical sensor, that identifies weed seeds or other types of material exiting agricultural harvester 100. The weed seed or other data detected by the sensor during a previous year's harvest may be used as data used to generate the information map 258. The sensed weed data or other data may be combined with other data to generate the information map 258. For example, based upon a magnitude of the weed seeds exiting agricultural harvester 100 at different locations and based upon other factors, such as whether the seeds are being spread by a spreader or dropped in a windrow; the weather conditions, such as wind, when the seeds are being dropped or spread; drainage conditions which may move seeds around in the field; or other information, the location of those weed seeds can be predicted so that the information map 258 maps the predicted seed locations in the field. The data for the information map 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data store 202. The data for the information map 258 can be provided to agricultural harvester 100 using communication system 206 in other ways as well, and this is indicated by block 286 in the flow diagram of
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, such as a speed characteristic, as indicated by block 288. Examples of in-situ sensors 288 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.
Predictive model generator 210 controls the information variable-to-in-situ variable model generator 228 to generate a model that models a relationship between the mapped values contained in the information map 258 and the in-situ values sensed by the in-situ sensors 208 as indicated by block 292. The characteristics or data types represented by the mapped values in the information map 258 and the in-situ values sensed by the in-situ sensors 208 may be the same characteristics or data type or different characteristics or data types.
The relationship or model generated by predictive model generator 210 is provided to predictive map generator 212. Predictive map generator 212 generates a predictive map 264 that predicts a value of the characteristic sensed by the in-situ sensors 208 at different geographic locations in a field being harvested, or a different characteristic that is related to the characteristic sensed by the in-situ sensors 208, using the predictive model and the information map 258, as indicated by block 294.
It should be noted that, in some examples, the information map 258 may include two or more different maps or two or more different map layers of a single map. Each map layer may represent a different data type from the data type of another map layer or the map layers may have the same data type that were obtained at different times. Each map in the two or more different maps or each layer in the two or more different map layers of a map maps a different type of variable to the geographic locations in the field. In such an example, predictive model generator 210 generates a predictive model that models the relationship between the in-situ data and each of the different variables mapped by the two or more different maps or the two or more different map layers. Similarly, the in-situ sensors 208 can include two or more sensors each sensing a different type of variable. Thus, the predictive model generator 210 generates a predictive model that models the relationships between each type of variable mapped by the information map 258 and each type of variable sensed by the in-situ sensors 208. Predictive map generator 212 can generate a functional predictive map 263 that predicts a value for each sensed characteristic sensed by the in-situ sensors 208 (or a characteristic related to the sensed characteristic) at different locations in the field being harvested using the predictive model and each of the maps or map layers in the information map 258.
Predictive map generator 212 configures the predictive map 264 so that the predictive map 264 is actionable (or consumable) by control system 214. Predictive map generator 212 can provide the predictive map 264 to the control system 214 or to control zone generator 213 or both. Some examples of different ways in which the predictive map 264 can be configured or output are described with respect to blocks 296, 295, 299 and 297. For instance, predictive map generator 212 configures predictive map 264 so that predictive map 264 includes values that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester 100, as indicated by block 296.
Control zone generator 213 can divide the predictive map 264 into control zones based on the values on the predictive map 264. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216, based on wear considerations, or on other criteria as indicated by block 295. Predictive map generator 212 configures predictive map 264 for presentation to an operator or other user. Control zone generator 213 can configure predictive control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the predictive map 264 or predictive control zone map 265 or both may contain one or more of the predictive values on the predictive map 264 correlated to geographic location, the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of machine 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the predictive map 264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also be able to change the predictive map 264. In some instances, the predictive map 264 accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The predictive map 264 or predictive control zone map 265 or both can be configured in other ways as well, as indicated by block 297.
At block 298, input from geographic position sensor 204 and other in-situ sensors 208 are received by the control system. Particularly, at block 300, control system 214 detects an input from the geographic position sensor 204 identifying a geographic location of agricultural harvester 100. Block 302 represents receipt by the control system 214 of sensor inputs indicative of trajectory or heading of agricultural harvester 100, and block 304 represents receipt by the control system 214 of a speed of agricultural harvester 100. Block 306 represents receipt by the control system 214 of other information from various in-situ sensors 208.
At block 308, control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in-situ sensors 208. At block 310, control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems 216 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both that is being used. Similarly, the control signals that are generated and the controllable subsystems 216 that are controlled and the timing of the control signals can be based on various latencies of crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.
By way of example, a generated predictive map 264 in the form of a predictive speed map can be used to control one or more subsystems 216. For instance, the predictive speed map can include speed values georeferenced to locations within the field being harvested. The speed values from the predictive speed map can be extracted and used to control the propulsion subsystem 250. By controlling the propulsion subsystem 250, a feed rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to take in more or less material, and, thus, the header height can also be controlled to control feed rate of material through the agricultural harvester 100. In other examples, if the predictive map 264 maps weed height relative to positions in the field, control of the header height can be implemented. For example, if the values present in the predictive weed map indicate one or more areas having weed height with a first height amount, then header and reel controller 238 can control the header height so that the header is positioned above the first height amount of the weeds within the one or more areas having weeds at the first height amount when performing the harvesting operation. Thus, the header and reel controller 238 can be controlled using georeferenced values present in the predictive weed map to position the header to a height that is above the predicted height values of weeds obtained from the predictive weed map. Further, the header height can be changed automatically by the header and reel controller 238 as the agricultural harvester 100 proceeds through the field using georeferenced values obtained from the predictive weed map. The preceding example involving weed height and intensity using a predictive weed map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive weed map or other type of predictive map to control one or more of the controllable subsystems 216.
At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not completed, the processing advances to block 314 where in-situ sensor data from geographic position sensor 204 and in-situ sensors 208 (and perhaps other sensors) continue to be read.
In some examples, at block 316, agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other triggered learning.
The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322 and 324. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors 208. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors 208 that exceeds a threshold trigger or causes the predictive model generator 210 to generate a new predictive model that is used by predictive map generator 212. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors 208 triggers the creation of a new relationship represented by a predictive model generated by predictive model generator 210. Further, new predictive map 264, predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model.
In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in information map 258) are within a selected range or is less than a defined amount, or below a threshold value, then a new predictive model is not generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model using all or a portion of the newly received in-situ sensor data that the predictive map generator 212 uses to generate a new predictive map 264. At block 320, variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in the information map 258, can be used as a trigger to cause generation of a new predictive model and predictive map. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.
Other learning trigger criteria can also be used. For instance, if predictive model generator 210 switches to a different information map (different from the originally selected information map 258), then switching to the different information map may trigger re-learning by predictive model generator 210, predictive map generator 212, control zone generator 213, control system 214, or other items. In another example, transitioning of agricultural harvester 100 to a different topography or to a different control zone may be used as learning trigger criteria as well.
In some instances, operator 260 can also edit the predictive map 264 or predictive control zone map 265 or both. The edits can change a value on the predictive map 264, change a size, shape, position, or existence of a control zone on predictive control zone map 265, or both. Block 321 shows that edited information can be used as learning trigger criteria.
In some instances, it may also be that operator 260 observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, the operator 260 may provide a manual adjustment to the controllable subsystem reflecting that the operator 260 desires the controllable subsystem to operate in a different way than is being commanded by control system 214. Thus, manual alteration of a setting by the operator 260 can cause one or more of predictive model generator 210 to relearn a model, predictive map generator 212 to regenerate map 264, control zone generator 213 to regenerate one or more control zones on predictive control zone map 265, and control system 214 to relearn a control algorithm or to perform machine learning on one or more of the controller components 232 through 246 in control system 214 based upon the adjustment by the operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.
In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block 326.
If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block 326, then one or more of the predictive model generator 210, predictive map generator 212, control zone generator 213, and control system 214 performs machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based upon the learning trigger criteria. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block 328.
If the harvesting operation has been completed, operation moves from block 312 to block 330 where one or more of the predictive map 264, predictive control zone map 265, and predictive model generated by predictive model generator 210 are stored. The predictive map 264, predictive control zone map 265, and predictive model may be stored locally on data store 202 or sent to a remote system using communication system 206 for later use.
It will be noted that while some examples herein describe predictive model generator 210 and predictive map generator 212 receiving an information map in generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator 210 and predictive map generator 212 can receive, in generating a predictive model and a functional predictive map, respectively other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation.
In some examples, sensor 336 may be a sensor, that generates a signal indicative of the control outputs from feed rate controller 236. The control signals may be speed control signals or other control signals that are applied to controllable subsystems 216 to control feed rate of material through agricultural harvester 100. Processing system 338 processes the signals obtained via the sensor 336 to generate processed data 340 identifying the speed of agricultural harvester 100.
In some examples, raw or processed data from in-situ sensor(s) 208 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be onboard the agricultural harvester 100 or at a remote location.
The present discussion proceeds with respect to an example in which in-situ sensor 208 is machine speed sensor 146. It will be appreciated that this is just one example, and the sensors mentioned above, as other examples of in-situ sensor 208 from which machine speed can be derived, are contemplated herein as well. As shown in
Model generator 342 identifies a relationship between machine speed detected in processed data 340, at a geographic location corresponding to where the processed data 340 were obtained, and a vegetative index value from the vegetative index map 332 corresponding to the same location in the field where the speed characteristic was detected. Based on this relationship established by model generator 342, model generator 342 generates a predictive speed model. The predictive speed model is used by speed map generator 352 to predict target machine speeds at different locations in the field based upon the georeferenced vegetative index value contained in the vegetative index map 332 at the same locations in the field.
Model generator 344 identifies a relationship between machine speed represented in the processed data 340, at a geographic location corresponding to the processed data 340, and the biomass value at the same geographic location. Again, the biomass value is the georeferenced value contained in the biomass map 335. Model generator 344 then generates a predictive speed model that is used by speed map generator 352 to predict the target machine speed at a location in the field based upon the biomass value for that location in the field.
Model generator 345 identifies a relationship between machine speed identified by processed data 340 at a particular location on the field and the topographic speed model that is used by speed map generator 352 to predict expected machine speed at a particular location in the field based on the topographic characteristic value at that location in the field.
Model generator 346 identifies a relationship between the machine speed identified by processed data 340 at a particular location in the field and the seeding characteristic value from the seeding characteristic map 343 at that same location. Model generator 346 generates a predictive speed model that is used by speed map generator 352 to predict the expected machine speed at a particular location in the field based upon the seeding characteristic value at that location in the field.
Model generator 347 identified a relationship between the machine speed identified by processed data 340 at a particular location on the field and the yield characteristic value from predictive yield map 333 at that same location model generator 347 generates a predictive speed model that is used by speed map generator 352 to predict the expected machine speed at a particular location on the field based upon the yield characteristic value at that location in the field.
Model generator 349 identifies a relationship between the machine speed identified by processed data 340 at a particular location in the field and the crop state characteristic value from crop state map 337 at the same location. Model generator 349 generates a predictive speed model that is used by speed map generator 352 to predict the expected machine speed at a particular location in the field based on the crop state characteristic value at that location in the field.
Model generator 351 identifies a relationship between the machine speed identified by processed data 340 at a particular location in the field and the soil property characteristic value from soil property map 341 at the same location. Model generator 351 generates a predictive speed model that is used by speed map generator 352 to predict the expected machine speed at a particular location in the field based on the soil property characteristic value at that location in the field.
In light of the above, the predictive model generator 210 is operable to produce a plurality of predictive speed models, such as one or more of the predictive speed models generated by model generators 342, 344, 345, 346, 347, 349 and 351. In another example, two or more of the predictive speed models described above may be combined into a single predictive speed model that predicts expected machine speed based on two or more of the vegetative index value, the biomass value, the topography, the yield, the seeding characteristic, the crop state, or the soil property, at different locations in the field. Any of these speed models, or combinations thereof, are represented collectively by predictive model 350 in
The predictive model 350 is provided to predictive map generator 212. In the example of
Predictive map generator 212 outputs one or more functional predictive speed maps 360 that are predictive of expected machine speed. The functional predictive speed map 360 predicts the expected machine speed at different locations in a field. The functional predictive speed maps 360 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and incorporates those control zones into the functional predictive map, i.e., predictive map 360, to produce predictive control zone map 265. One or both of predictive map 264 and predictive control zone map 265 may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216, such as propulsion subsystem 250 based upon the predictive map 264, predictive control zone map 265, or both.
At block 372, processing system 338 processes the one or more received signals to generate processed data 340 indicative of a machine speed of agricultural harvester 100.
At block 382, predictive model generator 210 also obtains the geographic location 334 corresponding to the processed data. For instance, the predictive model generator 210 can obtain the geographic position from geographic position sensor 204 and determine, based upon machine delays, machine speed, etc., a precise geographic location where the processed data was taken or from which the processed data 340 was derived.
At block 384, predictive model generator 210 generates one or more predictive models, such as predictive model 350, that model a relationship between a value in the one or more information map 258, and a machine speed being sensed by the in-situ sensor 208. VI value-to-speed model generator 342 generates a predictive model that models a relationship between VI values in VI map 332 and machine speed sensed by in-situ sensor 208. Biomass-to-speed model generator 344 generates a predictive model that models a relationship between biomass values in biomass map 335 and machine speed sensed by in-situ sensor 208. Topography-to-speed model generator 345 generates a predictive model that models a relationship between one or more topography values such as pitch, roll, or slope in topographic map 339 and machine speed sensed by in-situ sensor 208. Seeding characteristic-to-speed model generator 346 generates a predictive model that models a relationship between seeding characteristics in seeding map 343 and machine speed sensed by in-situ sensor 208. Yield-to-speed model generator 347 generates a predictive model that models a relationship between yield values in yield map 333 and machine speed sensed by in-situ sensor 208. Crop state-to-speed model generator 342 generates a predictive model that models a relationship between crop state values in crop state map 337 and machine speed sensed by in-situ sensor 208. Soil property-to-speed model generator 351 generates a predictive model that models a relationship between soil property values in soil property map 341 and machine speed sensed by in-situ sensor 208. At block 386, the predictive model 350 is provided to predictive map generator 212 which generates a functional predictive speed map 360 that maps a predicted, target machine speed based on the information map 258 and the predictive speed model 350. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between VI values in VI map 332 and machine speed and using the VI map 332. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between yield values in yield map 333 and machine speed and using the yield map 333. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between biomass values in biomass map 335 and machine speed and using the biomass map 335. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between crop state values in crop state map 337 and machine speed and using the crop state map 337. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between topographic values in topographic map 339 and machine speed and using the topographic map 332. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between soil property values in soil property map 341 and machine speed and using the soil property map 341. Speed map generator 352 may generate the functional predictive speed map 360 using a predictive model 350 that models a relationship between seeding characteristic values in seeding map 343 and machine speed and using the seeding map 343. Speed map generator 352 may generate the functional predictive speed map 360 using another predictive model 350 that models a relationship between other characteristic values on other map 353 and machine speed and using the other map 353.
Thus, as an agricultural harvester is moving through a field performing an agricultural operation, one or more functional predictive speed maps 360 are generated as the agricultural operation is being performed.
At block 394, predictive map generator 212 outputs the functional predictive speed map 360. At block 391 functional predictive speed map generator 212 outputs the functional predictive speed map 360 for presentation to and possible interaction by operator 260. At block 393, predictive map generator 212 may configure the map 360 for consumption by control system 214. At block 395, predictive map generator 212 can also provide the map 360 to control zone generator 213 for generation of control zones. At block 397, predictive map generator 212 configures the map 360 in other ways as well. The functional predictive speed map 360 (with or without the control zones) is provided to control system 214. At block 396, control system 214 generates control signals to control the controllable subsystems 216 based upon the functional predictive speed map 360. The control system 214 may control propulsion subsystem 250 or other subsystems 399.
It can thus be seen that the present system takes an information map that maps an agricultural characteristic such as a vegetative index, crop state, seeding characteristic, soil properties, biomass, predicted yield, topography, or information from a prior operation pass or passes to different locations in a field. The present system also uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a characteristic, indicative of machine speed, and generates a model that models a relationship between the characteristic sensed using the in-situ sensor, or a related characteristic, and the characteristic mapped in the information map. Thus, the present system generates a functional predictive map using a model, in-situ data, and an information map and may configure the generated functional predictive map for consumption by a control system, for presentation to a local or remote operator or other user, or both. For example, the control system may use the map to control one or more systems of a combine harvester.
The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are functional parts of the systems or devices to which the processors and servers belong and are activated by and facilitate the functionality of the other components or items in those systems.
Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, the user actuatable operator interface mechanisms can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.
A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.
Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.
It will be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, some of which are described below, that perform the functions associated with those systems, components, logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions may also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well.
In the example shown in
It will also be noted that the elements of
In some examples, remote server architecture 500 may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).
In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface 15. Interface 15 and communication links 13 communicate with a processor 17 (which can also embody processors or servers from other FIGS.) along a bus 19 that is also connected to memory 21 and input/output (I/O) components 23, as well as clock 25 and location system 27.
I/O components 23, in one example, are provided to facilitate input and output operations. I/O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components 23 can be used as well.
Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17.
Location system 27 illustratively includes a component that outputs a current geographical location of device 16. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.
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.
Note that other forms of the devices 16 are possible.
Computer 810 typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 810. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The system memory 830 includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit 820. By way of example, and not limitation,
The computer 810 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,
Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.
The drives and their associated computer storage media discussed above and illustrated in
A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, computers may also include other peripheral output devices such as speakers 897 and printer 896, which may be connected through an output peripheral interface 895.
The computer 810 is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer 880.
When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.
It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein.
Example 1 is an agricultural work machine, comprising:
Example 2 is the agricultural work machine of any or all previous examples, wherein the predictive map generator configures the functional predictive machine speed map for consumption by a control system that generates control signals to control a subsystem on the agricultural work machine based on the predictive machine speed values on the functional predictive machine speed map.
Example 3 is the agricultural work machine of any or all previous examples, wherein the in-situ sensor on the agricultural work machine is configured to detect, as the value of the second agricultural characteristic, a value of a speed characteristic indicative of the speed of the agricultural harvester corresponding to the geographic location.
Example 4 is the agricultural work machine of any or all previous examples, further comprising a feed rate controller configured to generate a feed rate control signal to control a controllable subsystem of the agricultural harvester based on a target feed rate of material through the agricultural harvester and wherein the in-situ sensor comprises:
a sensor configured to generate a sensor signal indicative of an output of the feed rate controller; and
a processing system that receives the sensor signal and generates processed data indicative of machine speed of the agricultural harvester based on the sensor signal.
Example 5 is the agricultural work machine of any or all previous examples, wherein the information map comprises a vegetative index map of vegetative index (VI) values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a VI-to-speed model generator that generates a predictive speed model that models a relationship between the VI values and the speed characteristic based on the VI value in the VI map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 6 is the agricultural work machine of any or all previous examples, wherein the information map comprises a biomass map of biomass values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a biomass-to-speed model generator that generates a predictive speed model that models a relationship between the biomass values and the speed characteristic based on the biomass value in the biomass map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 7 is the agricultural work machine of any or all previous examples, wherein the information map comprises a topographic map of topographic values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a topographic-to-speed model generator that generates a predictive speed model that models a relationship between the topographic values and the speed characteristic based on the topographic value in the topographic map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 8 is the agricultural work machine of any or all previous examples, wherein the information map comprises a predictive yield map of predictive yield values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a yield-to-speed model generator that generates a predictive speed model that models a relationship between the predictive yield values and the speed characteristic based on the predictive yield value in the predictive yield map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 9 is the agricultural work machine of any or all previous examples, wherein the information map comprises a soil properties map of soil property values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a soil property-to-speed model generator that generates a predictive speed model that models a relationship between the soil property values and the speed characteristic based on the soil property value in the soil property map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 10 is the agricultural work machine of any or all previous examples, wherein the information map comprises a seeding characteristic map of seeding characteristic values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a seeding characteristic-to-speed model generator that generates a predictive speed model that models a relationship between the seeding characteristic values and the speed characteristic based on the seeding characteristic value in the seeding characteristic map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 11 is the agricultural work machine of any or all previous examples, wherein the information map comprises a crop state map of crop state values corresponding to different geographic locations in a field, and wherein the predictive model generator comprises:
a crop state-to-speed model generator that generates a predictive speed model that models a relationship between the crop state values and the speed characteristic based on the crop state value in the crop state map at the geographic location and the value of the speed characteristic detected by the in-situ sensor at the geographic location.
Example 12 is a computer implemented method of generating a functional predictive agricultural map, comprising:
Example 13 is the computer implemented method of any or all previous examples, and further comprising:
configuring the functional predictive machine speed map for a propulsion controller that generates control signals to control a controllable propulsion subsystem on the agricultural work machine based on the functional predictive machine speed map.
Example 14 is the computer implemented method of any or all previous examples, wherein detecting, with an in-situ sensor, a value of the second agricultural characteristic comprises detecting a speed characteristic corresponding to the geographic location, and wherein receiving the information map comprises receiving one or more of a vegetative index map, a biomass map, a crop state map, a soil property map, a predictive yield map, a topographic map and a seeding map.
Example 15 is the computer implemented method of any or all previous examples, wherein receiving the information map comprises:
receiving a plurality of different information map layers, each information map layer, of the plurality of different information map layers indicating one or more of vegetative index values, biomass values, predictive yield values, crop state values, soil property values, seeding characteristic values and topographic values at the geographic location.
Example 16 is the computer implemented method of any or all previous examples, wherein receiving an information map comprises:
receiving an information map generated from a prior operation performed in the field.
Example 17 is the computer implemented method of any or all previous examples, further comprising:
controlling an operator interface mechanism to present the functional predictive machine speed map.
Example 18 is an agricultural work machine, comprising:
Example 19 is the agricultural work machine of any or all previous examples, wherein the in-situ sensor comprises:
a machine speed sensor configured to detect the speed of the agricultural work machine.
Example 20 is the agricultural work machine of any or all previous examples wherein the predictive map generator is configured to configure the functional predictive speed map for consumption by a control system to control a propulsion subsystem based on the target machine speeds of the functional predictive speed map.
Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3568157 | Downing et al. | Mar 1971 | A |
| 3580257 | Teague | May 1971 | A |
| 3599543 | Kerridge | Aug 1971 | A |
| 3775019 | Konig et al. | Nov 1973 | A |
| 3856754 | Habermeier et al. | Dec 1974 | A |
| 4129573 | Bellus et al. | Dec 1978 | A |
| 4166735 | Pilgram et al. | Sep 1979 | A |
| 4183742 | Sasse et al. | Jan 1980 | A |
| 4268679 | Lavanish | May 1981 | A |
| 4349377 | Dürr et al. | Sep 1982 | A |
| 4360677 | Doweyko et al. | Nov 1982 | A |
| 4435203 | Funaki et al. | Mar 1984 | A |
| 4493726 | Burdeska et al. | Jan 1985 | A |
| 4527241 | Sheehan et al. | Jul 1985 | A |
| 4566901 | Martin et al. | Jan 1986 | A |
| 4584013 | Brunner | Apr 1986 | A |
| 4687505 | Sylling et al. | Aug 1987 | A |
| 4857101 | Musco et al. | Aug 1989 | A |
| 4911751 | Nyffeler et al. | Mar 1990 | A |
| 5059154 | Reyenga | Oct 1991 | A |
| 5089043 | Hayase et al. | Feb 1992 | A |
| 5246164 | McCann et al. | Sep 1993 | A |
| 5246915 | Lutz et al. | Sep 1993 | A |
| 5250690 | Turner et al. | Oct 1993 | A |
| 5296702 | Beck et al. | Mar 1994 | A |
| 5300477 | Tice | Apr 1994 | A |
| 5416061 | Hewett et al. | May 1995 | A |
| 5477459 | Clegg et al. | Dec 1995 | A |
| 5488817 | Paquet et al. | Feb 1996 | A |
| 5563112 | Barnes III | Oct 1996 | A |
| 5585626 | Beck et al. | Dec 1996 | A |
| 5586033 | Hall | Dec 1996 | A |
| 5592606 | Myers | Jan 1997 | A |
| 5606821 | Sadjadi et al. | Mar 1997 | A |
| 5666793 | Bottinger | Sep 1997 | A |
| 5712782 | Weigelt et al. | Jan 1998 | A |
| 5721679 | Monson | Feb 1998 | A |
| 5767373 | Ward et al. | Jun 1998 | A |
| 5771169 | Wendte | Jun 1998 | A |
| 5789741 | Kinter et al. | Aug 1998 | A |
| 5809440 | Beck et al. | Sep 1998 | A |
| 5841282 | Christy et al. | Nov 1998 | A |
| 5849665 | Gut et al. | Dec 1998 | A |
| 5878821 | Flenker et al. | Mar 1999 | A |
| 5899950 | Milender et al. | May 1999 | A |
| 5902343 | Hale et al. | May 1999 | A |
| 5915492 | Yates et al. | Jun 1999 | A |
| 5957304 | Dawson | Sep 1999 | A |
| 5974348 | Rocks | Oct 1999 | A |
| 5978723 | Hale et al. | Nov 1999 | A |
| 5991687 | Hale et al. | Nov 1999 | A |
| 5991694 | Gudat et al. | Nov 1999 | A |
| 5995859 | Takahashi | Nov 1999 | A |
| 5995894 | Wendte | Nov 1999 | A |
| 5995895 | Watt et al. | Nov 1999 | A |
| 6004076 | Cook et al. | Dec 1999 | A |
| 6016713 | Hale | Jan 2000 | A |
| 6029106 | Hale et al. | Feb 2000 | A |
| 6041582 | Tiede et al. | Mar 2000 | A |
| 6073070 | Diekhans | Jun 2000 | A |
| 6073428 | Diekhans | Jun 2000 | A |
| 6085135 | Steckel | Jul 2000 | A |
| 6119442 | Hale | Sep 2000 | A |
| 6119531 | Wendte et al. | Sep 2000 | A |
| 6128574 | Diekhans | Oct 2000 | A |
| 6141614 | Janzen et al. | Oct 2000 | A |
| 6178253 | Hendrickson et al. | Jan 2001 | B1 |
| 6185990 | Missotten et al. | Feb 2001 | B1 |
| 6188942 | Corcoran et al. | Feb 2001 | B1 |
| 6199000 | Keller et al. | Mar 2001 | B1 |
| 6204856 | Wood et al. | Mar 2001 | B1 |
| 6205381 | Motz et al. | Mar 2001 | B1 |
| 6205384 | Diekhans | Mar 2001 | B1 |
| 6216071 | Motz | Apr 2001 | B1 |
| 6236924 | Motz et al. | May 2001 | B1 |
| 6272819 | Wendte et al. | Aug 2001 | B1 |
| 6327569 | Reep | Dec 2001 | B1 |
| 6374173 | Ehlbeck | Apr 2002 | B1 |
| 6380745 | Anderson et al. | Apr 2002 | B1 |
| 6431790 | Anderegg et al. | Aug 2002 | B1 |
| 6451733 | Pidskalny et al. | Sep 2002 | B1 |
| 6505146 | Blackmer | Jan 2003 | B1 |
| 6505998 | Bullivant | Jan 2003 | B1 |
| 6539102 | Anderson et al. | Mar 2003 | B1 |
| 6549849 | Lange et al. | Apr 2003 | B2 |
| 6584390 | Beck | Jun 2003 | B2 |
| 6591145 | Hoskinson et al. | Jul 2003 | B1 |
| 6591591 | Coers et al. | Jul 2003 | B2 |
| 6592453 | Coers et al. | Jul 2003 | B2 |
| 6604432 | Hamblen et al. | Aug 2003 | B1 |
| 6681551 | Sheidler et al. | Jan 2004 | B1 |
| 6682416 | Behnke et al. | Jan 2004 | B2 |
| 6687616 | Peterson et al. | Feb 2004 | B1 |
| 6729189 | Paakkinen | May 2004 | B2 |
| 6735568 | Buckwalter et al. | May 2004 | B1 |
| 6834550 | Upadhyaya et al. | Dec 2004 | B2 |
| 6838564 | Edmunds et al. | Jan 2005 | B2 |
| 6846128 | Sick | Jan 2005 | B2 |
| 6932554 | Isfort et al. | Aug 2005 | B2 |
| 6999877 | Dyer et al. | Feb 2006 | B1 |
| 7073374 | Berkman | Jul 2006 | B2 |
| 7167797 | Faivre et al. | Jan 2007 | B2 |
| 7167800 | Faivre et al. | Jan 2007 | B2 |
| 7191062 | Chi et al. | Mar 2007 | B2 |
| 7194965 | Hickey et al. | Mar 2007 | B2 |
| 7211994 | Mergen et al. | May 2007 | B1 |
| 7248968 | Reid | Jul 2007 | B2 |
| 7255016 | Burton | Aug 2007 | B2 |
| 7261632 | Pirro et al. | Aug 2007 | B2 |
| 7302837 | Wendte | Dec 2007 | B2 |
| 7308326 | Maertens et al. | Dec 2007 | B2 |
| 7313478 | Anderson et al. | Dec 2007 | B1 |
| 7318010 | Anderson | Jan 2008 | B2 |
| 7347168 | Reckels et al. | Mar 2008 | B2 |
| 7408145 | Holland | Aug 2008 | B2 |
| 7480564 | Metzler et al. | Jan 2009 | B2 |
| 7483791 | Anderegg et al. | Jan 2009 | B2 |
| 7537519 | Huster et al. | May 2009 | B2 |
| 7557066 | Hills et al. | Jul 2009 | B2 |
| 7628059 | Scherbring | Dec 2009 | B1 |
| 7687435 | Witschel et al. | Mar 2010 | B2 |
| 7703036 | Satterfield et al. | Apr 2010 | B2 |
| 7725233 | Hendrickson et al. | May 2010 | B2 |
| 7733416 | Gal | Jun 2010 | B2 |
| 7756624 | Diekhans et al. | Jul 2010 | B2 |
| 7798894 | Isfort | Sep 2010 | B2 |
| 7827042 | Jung et al. | Nov 2010 | B2 |
| 7915200 | Epp et al. | Mar 2011 | B2 |
| 7945364 | Schricker et al. | May 2011 | B2 |
| 7993188 | Ritter | Aug 2011 | B2 |
| 8024074 | Stelford et al. | Sep 2011 | B2 |
| 8060283 | Mott et al. | Nov 2011 | B2 |
| 8107681 | Gaál | Jan 2012 | B2 |
| 8145393 | Foster et al. | Mar 2012 | B2 |
| 8147176 | Coers et al. | Apr 2012 | B2 |
| 8152610 | Harrington | Apr 2012 | B2 |
| 8190335 | Vik et al. | May 2012 | B2 |
| 8195342 | Anderson | Jun 2012 | B2 |
| 8195358 | Anderson | Jun 2012 | B2 |
| 8213964 | Fitzner et al. | Jul 2012 | B2 |
| 8224500 | Anderson | Jul 2012 | B2 |
| 8252723 | Jakobi et al. | Aug 2012 | B2 |
| 8254351 | Fitzner et al. | Aug 2012 | B2 |
| 8321365 | Anderson | Nov 2012 | B2 |
| 8329717 | Minn et al. | Dec 2012 | B2 |
| 8332105 | Laux | Dec 2012 | B2 |
| 8338332 | Hacker et al. | Dec 2012 | B1 |
| 8340862 | Baumgarten et al. | Dec 2012 | B2 |
| 8407157 | Anderson | Mar 2013 | B2 |
| 8428829 | Brunnert et al. | Apr 2013 | B2 |
| 8478493 | Anderson | Jul 2013 | B2 |
| 8488865 | Hausmann et al. | Jul 2013 | B2 |
| 8494727 | Green et al. | Jul 2013 | B2 |
| 8527157 | Imhof et al. | Sep 2013 | B2 |
| 8544397 | Bassett | Oct 2013 | B2 |
| 8577561 | Green et al. | Nov 2013 | B2 |
| 8606454 | Wang et al. | Dec 2013 | B2 |
| 8626406 | Schleicher et al. | Jan 2014 | B2 |
| 8635903 | Oetken et al. | Jan 2014 | B2 |
| 8649940 | Bonefas | Feb 2014 | B2 |
| 8656693 | Madsen et al. | Feb 2014 | B2 |
| 8662972 | Behnke et al. | Mar 2014 | B2 |
| 8671760 | Wallrath et al. | Mar 2014 | B2 |
| 8677724 | Chaney et al. | Mar 2014 | B2 |
| 8738238 | Rekow | May 2014 | B2 |
| 8738244 | Lenz et al. | May 2014 | B2 |
| 8755976 | Peters et al. | Jun 2014 | B2 |
| 8781692 | Kormann | Jul 2014 | B2 |
| 8789563 | Wenzel | Jul 2014 | B2 |
| 8814640 | Behnke et al. | Aug 2014 | B2 |
| 8843269 | Anderson et al. | Sep 2014 | B2 |
| 8868304 | Bonefas | Oct 2014 | B2 |
| 8909389 | Meyer | Dec 2014 | B2 |
| D721740 | Schmaltz et al. | Jan 2015 | S |
| 8942860 | Morselli | Jan 2015 | B2 |
| 8962523 | Rosinger et al. | Feb 2015 | B2 |
| 9002591 | Wang et al. | Apr 2015 | B2 |
| 9008918 | Missotten et al. | Apr 2015 | B2 |
| 9009087 | Mewes et al. | Apr 2015 | B1 |
| 9011222 | Johnson et al. | Apr 2015 | B2 |
| 9014901 | Wang et al. | Apr 2015 | B2 |
| 9043096 | Zielke et al. | May 2015 | B2 |
| 9043129 | Bonefas et al. | May 2015 | B2 |
| 9066465 | Hendrickson et al. | Jun 2015 | B2 |
| 9072227 | Wenzel | Jul 2015 | B2 |
| 9095090 | Casper et al. | Aug 2015 | B2 |
| 9119342 | Bonefas | Sep 2015 | B2 |
| 9127428 | Meier | Sep 2015 | B2 |
| 9131644 | Osborne | Sep 2015 | B2 |
| 9152938 | Lang et al. | Oct 2015 | B2 |
| 9173339 | Sauder et al. | Nov 2015 | B2 |
| 9179599 | Bischoff | Nov 2015 | B2 |
| 9188518 | Snyder et al. | Nov 2015 | B2 |
| 9188986 | Baumann | Nov 2015 | B2 |
| 9226449 | Bischoff | Jan 2016 | B2 |
| 9234317 | Chi | Jan 2016 | B2 |
| 9235214 | Anderson | Jan 2016 | B2 |
| 9301447 | Kormann | Apr 2016 | B2 |
| 9301466 | Kelly | Apr 2016 | B2 |
| 9313951 | Herman et al. | Apr 2016 | B2 |
| 9326443 | Zametzer et al. | May 2016 | B2 |
| 9326444 | Bonefas | May 2016 | B2 |
| 9392746 | Darr et al. | Jul 2016 | B2 |
| 9405039 | Anderson | Aug 2016 | B2 |
| 9410840 | Acheson et al. | Aug 2016 | B2 |
| 9439342 | Pasquier | Sep 2016 | B2 |
| 9457971 | Bonefas et al. | Oct 2016 | B2 |
| 9463939 | Bonefas et al. | Oct 2016 | B2 |
| 9485905 | Jung et al. | Nov 2016 | B2 |
| 9489576 | Johnson et al. | Nov 2016 | B2 |
| 9497898 | Hennes | Nov 2016 | B2 |
| 9510508 | Jung | Dec 2016 | B2 |
| 9511633 | Anderson et al. | Dec 2016 | B2 |
| 9511958 | Bonefas | Dec 2016 | B2 |
| 9516812 | Baumgarten et al. | Dec 2016 | B2 |
| 9521805 | Muench et al. | Dec 2016 | B2 |
| 9522791 | Bonefas et al. | Dec 2016 | B2 |
| 9522792 | Bonefas et al. | Dec 2016 | B2 |
| 9523180 | Deines | Dec 2016 | B2 |
| 9529364 | Foster et al. | Dec 2016 | B2 |
| 9532504 | Herman et al. | Jan 2017 | B2 |
| 9538714 | Anderson | Jan 2017 | B2 |
| 9563492 | Bell et al. | Feb 2017 | B2 |
| 9563848 | Hunt | Feb 2017 | B1 |
| 9563852 | Wiles et al. | Feb 2017 | B1 |
| 9578808 | Dybro et al. | Feb 2017 | B2 |
| 9629308 | Schøler et al. | Apr 2017 | B2 |
| 9631964 | Gelinske et al. | Apr 2017 | B2 |
| 9642305 | Nykamp et al. | May 2017 | B2 |
| 9648807 | Escher et al. | May 2017 | B2 |
| 9675008 | Rusciolelli et al. | Jun 2017 | B1 |
| 9681605 | Noonan et al. | Jun 2017 | B2 |
| 9694712 | Healy | Jul 2017 | B2 |
| 9696162 | Anderson | Jul 2017 | B2 |
| 9699967 | Palla et al. | Jul 2017 | B2 |
| 9714856 | Myers | Jul 2017 | B2 |
| 9717178 | Sauder et al. | Aug 2017 | B1 |
| 9721181 | Guan et al. | Aug 2017 | B2 |
| 9723790 | Berry et al. | Aug 2017 | B2 |
| 9740208 | Sugumaran et al. | Aug 2017 | B2 |
| 9767521 | Stuber et al. | Sep 2017 | B2 |
| 9807934 | Rusciolelli et al. | Nov 2017 | B2 |
| 9807940 | Roell et al. | Nov 2017 | B2 |
| 9810679 | Kimmel | Nov 2017 | B2 |
| 9829364 | Wilson et al. | Nov 2017 | B2 |
| 9848528 | Werner et al. | Dec 2017 | B2 |
| 9856609 | Dehmel | Jan 2018 | B2 |
| 9856612 | Oetken | Jan 2018 | B2 |
| 9861040 | Bonefas | Jan 2018 | B2 |
| 9872433 | Acheson et al. | Jan 2018 | B2 |
| 9903077 | Rio | Feb 2018 | B2 |
| 9903979 | Dybro et al. | Feb 2018 | B2 |
| 9904963 | Rupp et al. | Feb 2018 | B2 |
| 9915952 | Dollinger et al. | Mar 2018 | B2 |
| 9922405 | Sauder et al. | Mar 2018 | B2 |
| 9924636 | Lisouski et al. | Mar 2018 | B2 |
| 9928584 | Jens et al. | Mar 2018 | B2 |
| 9933787 | Story | Apr 2018 | B2 |
| 9974226 | Rupp et al. | May 2018 | B2 |
| 9982397 | Korb et al. | May 2018 | B2 |
| 9984455 | Fox et al. | May 2018 | B1 |
| 9992931 | Bonefas et al. | Jun 2018 | B2 |
| 9992932 | Bonefas et al. | Jun 2018 | B2 |
| 10004176 | Mayerle | Jun 2018 | B2 |
| 10015928 | Nykamp et al. | Jul 2018 | B2 |
| 10019018 | Hulin | Jul 2018 | B2 |
| 10019790 | Bonefas et al. | Jul 2018 | B2 |
| 10025983 | Guan et al. | Jul 2018 | B2 |
| 10028435 | Anderson et al. | Jul 2018 | B2 |
| 10028451 | Rowan et al. | Jul 2018 | B2 |
| 10034427 | Krause et al. | Jul 2018 | B2 |
| 10039231 | Anderson et al. | Aug 2018 | B2 |
| 10064331 | Bradley | Sep 2018 | B2 |
| 10064335 | Byttebier et al. | Sep 2018 | B2 |
| 10078890 | Tagestad et al. | Sep 2018 | B1 |
| 10085372 | Noyer et al. | Oct 2018 | B2 |
| 10091925 | Aharoni et al. | Oct 2018 | B2 |
| 10126153 | Bischoff et al. | Nov 2018 | B2 |
| 10129528 | Bonefas et al. | Nov 2018 | B2 |
| 10143132 | Inoue et al. | Dec 2018 | B2 |
| 10152035 | Reid et al. | Dec 2018 | B2 |
| 10154624 | Guan et al. | Dec 2018 | B2 |
| 10165725 | Sugumaran et al. | Jan 2019 | B2 |
| 10178823 | Kovach et al. | Jan 2019 | B2 |
| 10183667 | Anderson et al. | Jan 2019 | B2 |
| 10188037 | Bruns et al. | Jan 2019 | B2 |
| 10201121 | Wilson | Feb 2019 | B1 |
| 10209179 | Hollstein | Feb 2019 | B2 |
| 10231371 | Dillon | Mar 2019 | B2 |
| 10254147 | Vermue et al. | Apr 2019 | B2 |
| 10254765 | Rekow | Apr 2019 | B2 |
| 10255670 | Wu et al. | Apr 2019 | B1 |
| 10275550 | Lee | Apr 2019 | B2 |
| 10295703 | Dybro et al. | May 2019 | B2 |
| 10310455 | Blank et al. | Jun 2019 | B2 |
| 10314232 | Isaac et al. | Jun 2019 | B2 |
| 10315655 | Blank et al. | Jun 2019 | B2 |
| 10317272 | Bhavsar et al. | Jun 2019 | B2 |
| 10351364 | Green et al. | Jul 2019 | B2 |
| 10368488 | Becker et al. | Aug 2019 | B2 |
| 10398084 | Ray et al. | Sep 2019 | B2 |
| 10408545 | Blank et al. | Sep 2019 | B2 |
| 10412889 | Palla et al. | Sep 2019 | B2 |
| 10426086 | Van de Wege et al. | Oct 2019 | B2 |
| 10437243 | Blank et al. | Oct 2019 | B2 |
| 10477756 | Richt et al. | Nov 2019 | B1 |
| 10485178 | Mayerle | Nov 2019 | B2 |
| 10521526 | Haaland et al. | Dec 2019 | B2 |
| 10537061 | Farley et al. | Jan 2020 | B2 |
| 10568316 | Gall et al. | Feb 2020 | B2 |
| 10631462 | Bonefas | Apr 2020 | B2 |
| 10677637 | Von Muenster | Jun 2020 | B1 |
| 10681872 | Viaene et al. | Jun 2020 | B2 |
| 10703277 | Schroeder | Jul 2020 | B1 |
| 10729067 | Hammer et al. | Aug 2020 | B2 |
| 10740703 | Story | Aug 2020 | B2 |
| 10745868 | Laugwitz et al. | Aug 2020 | B2 |
| 10760946 | Meier et al. | Sep 2020 | B2 |
| 10809118 | Von Muenster | Oct 2020 | B1 |
| 10830634 | Blank et al. | Nov 2020 | B2 |
| 10866109 | Madsen et al. | Dec 2020 | B2 |
| 10890922 | Ramm et al. | Jan 2021 | B2 |
| 10909368 | Guo et al. | Feb 2021 | B2 |
| 10912249 | Wilson | Feb 2021 | B1 |
| 20020011061 | Lucand et al. | Jan 2002 | A1 |
| 20020083695 | Behnke et al. | Jul 2002 | A1 |
| 20020091458 | Moore | Jul 2002 | A1 |
| 20020099471 | Benneweis | Jul 2002 | A1 |
| 20020133309 | Hardt | Sep 2002 | A1 |
| 20020173893 | Blackmore et al. | Nov 2002 | A1 |
| 20020193928 | Beck | Dec 2002 | A1 |
| 20020193929 | Beck | Dec 2002 | A1 |
| 20020198654 | Lange et al. | Dec 2002 | A1 |
| 20030004630 | Beck | Jan 2003 | A1 |
| 20030014171 | Ma et al. | Jan 2003 | A1 |
| 20030015351 | Goldman et al. | Jan 2003 | A1 |
| 20030024450 | Juptner | Feb 2003 | A1 |
| 20030060245 | Coers et al. | Mar 2003 | A1 |
| 20030069680 | Cohen et al. | Apr 2003 | A1 |
| 20030075145 | Sheidler et al. | Apr 2003 | A1 |
| 20030174207 | Alexia et al. | Sep 2003 | A1 |
| 20030182144 | Pickett et al. | Sep 2003 | A1 |
| 20030187560 | Keller et al. | Oct 2003 | A1 |
| 20030216158 | Bischoff | Nov 2003 | A1 |
| 20030229432 | Ho et al. | Dec 2003 | A1 |
| 20030229433 | Van den Berg et al. | Dec 2003 | A1 |
| 20030229435 | Van der Lely | Dec 2003 | A1 |
| 20040004544 | William Knutson | Jan 2004 | A1 |
| 20040054457 | Kormann | Mar 2004 | A1 |
| 20040073468 | Vyas et al. | Apr 2004 | A1 |
| 20040193348 | Gray et al. | Sep 2004 | A1 |
| 20050059445 | Niermann et al. | Mar 2005 | A1 |
| 20050066738 | Moore | Mar 2005 | A1 |
| 20050149235 | Seal et al. | Jul 2005 | A1 |
| 20050150202 | Quick | Jul 2005 | A1 |
| 20050197175 | Anderson | Sep 2005 | A1 |
| 20050241285 | Maertens et al. | Nov 2005 | A1 |
| 20050283314 | Hall | Dec 2005 | A1 |
| 20050284119 | Brunnert | Dec 2005 | A1 |
| 20060014489 | Fitzner et al. | Jan 2006 | A1 |
| 20060014643 | Hacker et al. | Jan 2006 | A1 |
| 20060047377 | Ferguson et al. | Mar 2006 | A1 |
| 20060058896 | Pokorny et al. | Mar 2006 | A1 |
| 20060074560 | Dyer et al. | Apr 2006 | A1 |
| 20060155449 | Dammann | Jul 2006 | A1 |
| 20060162631 | Hickey et al. | Jul 2006 | A1 |
| 20060196158 | Faivre et al. | Sep 2006 | A1 |
| 20060200334 | Faivre et al. | Sep 2006 | A1 |
| 20070005209 | Fitzner et al. | Jan 2007 | A1 |
| 20070021948 | Anderson | Jan 2007 | A1 |
| 20070056258 | Behnke | Mar 2007 | A1 |
| 20070068238 | Wendte | Mar 2007 | A1 |
| 20070073700 | Wippersteg et al. | Mar 2007 | A1 |
| 20070089390 | Hendrickson et al. | Apr 2007 | A1 |
| 20070135190 | Diekhans et al. | Jun 2007 | A1 |
| 20070185749 | Anderson et al. | Aug 2007 | A1 |
| 20070199903 | Denny | Aug 2007 | A1 |
| 20070208510 | Anderson et al. | Sep 2007 | A1 |
| 20070233348 | Diekhans et al. | Oct 2007 | A1 |
| 20070233374 | Diekhans et al. | Oct 2007 | A1 |
| 20070239337 | Anderson | Oct 2007 | A1 |
| 20070282523 | Diekhans et al. | Dec 2007 | A1 |
| 20070298744 | Fitzner et al. | Dec 2007 | A1 |
| 20080030320 | Wilcox et al. | Feb 2008 | A1 |
| 20080098035 | Wippersteg et al. | Apr 2008 | A1 |
| 20080140431 | Anderson et al. | Jun 2008 | A1 |
| 20080177449 | Pickett et al. | Jul 2008 | A1 |
| 20080248843 | Birrell et al. | Oct 2008 | A1 |
| 20080268927 | Farley et al. | Oct 2008 | A1 |
| 20080269052 | Rosinger et al. | Oct 2008 | A1 |
| 20080289308 | Brubaker | Nov 2008 | A1 |
| 20080312085 | Kordes et al. | Dec 2008 | A1 |
| 20090044505 | Huster et al. | Feb 2009 | A1 |
| 20090074243 | Missotten et al. | Mar 2009 | A1 |
| 20090143941 | Tarasinski et al. | Jun 2009 | A1 |
| 20090192654 | Wendte et al. | Jul 2009 | A1 |
| 20090216410 | Allen et al. | Aug 2009 | A1 |
| 20090226036 | Gaal | Sep 2009 | A1 |
| 20090259483 | Hendrickson et al. | Oct 2009 | A1 |
| 20090265098 | Dix | Oct 2009 | A1 |
| 20090306835 | Ellermann et al. | Dec 2009 | A1 |
| 20090311084 | Coers et al. | Dec 2009 | A1 |
| 20090312919 | Foster et al. | Dec 2009 | A1 |
| 20090312920 | Boenig et al. | Dec 2009 | A1 |
| 20090325658 | Phelan et al. | Dec 2009 | A1 |
| 20100036696 | Lang et al. | Feb 2010 | A1 |
| 20100042297 | Foster et al. | Feb 2010 | A1 |
| 20100063626 | Anderson | Mar 2010 | A1 |
| 20100063648 | Anderson | Mar 2010 | A1 |
| 20100063651 | Anderson | Mar 2010 | A1 |
| 20100063664 | Anderson | Mar 2010 | A1 |
| 20100063954 | Anderson | Mar 2010 | A1 |
| 20100070145 | Foster et al. | Mar 2010 | A1 |
| 20100071329 | Hindryckx et al. | Mar 2010 | A1 |
| 20100094481 | Anderson | Apr 2010 | A1 |
| 20100121541 | Behnke et al. | May 2010 | A1 |
| 20100137373 | Hungenberg et al. | Jun 2010 | A1 |
| 20100145572 | Steckel et al. | Jun 2010 | A1 |
| 20100152270 | Suty-Heinze et al. | Jun 2010 | A1 |
| 20100152943 | Matthews | Jun 2010 | A1 |
| 20100217474 | Baumgarten et al. | Aug 2010 | A1 |
| 20100268562 | Anderson | Oct 2010 | A1 |
| 20100268679 | Anderson | Oct 2010 | A1 |
| 20100285964 | Waldraff et al. | Nov 2010 | A1 |
| 20100317517 | Rosinger et al. | Dec 2010 | A1 |
| 20100319941 | Peterson | Dec 2010 | A1 |
| 20100332051 | Kormann | Dec 2010 | A1 |
| 20110056178 | Sauerwein et al. | Mar 2011 | A1 |
| 20110059782 | Harrington | Mar 2011 | A1 |
| 20110072773 | Schroeder et al. | Mar 2011 | A1 |
| 20110084851 | Peterson et al. | Apr 2011 | A1 |
| 20110086684 | Luellen et al. | Apr 2011 | A1 |
| 20110160961 | Wollenhaupt et al. | Jun 2011 | A1 |
| 20110213531 | Farley et al. | Sep 2011 | A1 |
| 20110224873 | Reeve et al. | Sep 2011 | A1 |
| 20110227745 | Kikuchi et al. | Sep 2011 | A1 |
| 20110257850 | Reeve et al. | Oct 2011 | A1 |
| 20110270494 | Imhof et al. | Nov 2011 | A1 |
| 20110270495 | Knapp | Nov 2011 | A1 |
| 20110295460 | Hunt et al. | Dec 2011 | A1 |
| 20110307149 | Pighi et al. | Dec 2011 | A1 |
| 20120004813 | Baumgarten et al. | Jan 2012 | A1 |
| 20120029732 | Meyer et al. | Feb 2012 | A1 |
| 20120087771 | Wenzel | Apr 2012 | A1 |
| 20120096827 | Chaney et al. | Apr 2012 | A1 |
| 20120143642 | O'Neil | Jun 2012 | A1 |
| 20120215378 | Sprock et al. | Aug 2012 | A1 |
| 20120215379 | Sprock et al. | Aug 2012 | A1 |
| 20120253611 | Zielke et al. | Oct 2012 | A1 |
| 20120263560 | Diekhans et al. | Oct 2012 | A1 |
| 20120265412 | Diekhans et al. | Oct 2012 | A1 |
| 20120271489 | Roberts et al. | Oct 2012 | A1 |
| 20120323452 | Green et al. | Dec 2012 | A1 |
| 20130019580 | Anderson et al. | Jan 2013 | A1 |
| 20130022430 | Anderson et al. | Jan 2013 | A1 |
| 20130046419 | Anderson et al. | Feb 2013 | A1 |
| 20130046439 | Anderson et al. | Feb 2013 | A1 |
| 20130046525 | Ali et al. | Feb 2013 | A1 |
| 20130103269 | Hellgen et al. | Apr 2013 | A1 |
| 20130124239 | Rosa et al. | May 2013 | A1 |
| 20130184944 | Missotten et al. | Jul 2013 | A1 |
| 20130197767 | Lenz | Aug 2013 | A1 |
| 20130205733 | Peters et al. | Aug 2013 | A1 |
| 20130210505 | Bischoff | Aug 2013 | A1 |
| 20130231823 | Wang et al. | Sep 2013 | A1 |
| 20130319941 | Schneider | Dec 2013 | A1 |
| 20130325242 | Cavender-Bares et al. | Dec 2013 | A1 |
| 20130332003 | Murray et al. | Dec 2013 | A1 |
| 20140002489 | Sauder et al. | Jan 2014 | A1 |
| 20140019017 | Wilken et al. | Jan 2014 | A1 |
| 20140021598 | Sutardja | Jan 2014 | A1 |
| 20140050364 | Brueckner et al. | Feb 2014 | A1 |
| 20140067745 | Avey | Mar 2014 | A1 |
| 20140121882 | Gilmore et al. | May 2014 | A1 |
| 20140129048 | Baumgarten et al. | May 2014 | A1 |
| 20140172222 | Nickel | Jun 2014 | A1 |
| 20140172224 | Matthews et al. | Jun 2014 | A1 |
| 20140172225 | Matthews et al. | Jun 2014 | A1 |
| 20140208870 | Quaderer et al. | Jul 2014 | A1 |
| 20140215984 | Bischoff | Aug 2014 | A1 |
| 20140230391 | Hendrickson et al. | Aug 2014 | A1 |
| 20140230392 | Dybro et al. | Aug 2014 | A1 |
| 20140236381 | Anderson et al. | Aug 2014 | A1 |
| 20140236431 | Hendrickson et al. | Aug 2014 | A1 |
| 20140257911 | Anderson | Sep 2014 | A1 |
| 20140262547 | Acheson et al. | Sep 2014 | A1 |
| 20140277960 | Blank et al. | Sep 2014 | A1 |
| 20140297242 | Sauder et al. | Oct 2014 | A1 |
| 20140303814 | Burema et al. | Oct 2014 | A1 |
| 20140324272 | Madsen et al. | Oct 2014 | A1 |
| 20140331631 | Sauder et al. | Nov 2014 | A1 |
| 20140338298 | Jung et al. | Nov 2014 | A1 |
| 20140350802 | Biggerstaff et al. | Nov 2014 | A1 |
| 20140360148 | Wienker et al. | Dec 2014 | A1 |
| 20150049088 | Snyder et al. | Feb 2015 | A1 |
| 20150088785 | Chi | Mar 2015 | A1 |
| 20150095830 | Massoumi et al. | Apr 2015 | A1 |
| 20150101519 | Blackwell et al. | Apr 2015 | A1 |
| 20150105984 | Birrell et al. | Apr 2015 | A1 |
| 20150124054 | Darr et al. | May 2015 | A1 |
| 20150168187 | Myers | Jun 2015 | A1 |
| 20150211199 | Corcoran et al. | Jul 2015 | A1 |
| 20150230403 | Jung et al. | Aug 2015 | A1 |
| 20150242799 | Seki et al. | Aug 2015 | A1 |
| 20150243114 | Tanabe et al. | Aug 2015 | A1 |
| 20150254800 | Johnson et al. | Sep 2015 | A1 |
| 20150264863 | Muench et al. | Sep 2015 | A1 |
| 20150276794 | Pistrol et al. | Oct 2015 | A1 |
| 20150278640 | Johnson et al. | Oct 2015 | A1 |
| 20150285647 | Meyer Zu Helligen et al. | Oct 2015 | A1 |
| 20150293029 | Acheson et al. | Oct 2015 | A1 |
| 20150302305 | Rupp et al. | Oct 2015 | A1 |
| 20150305238 | Klausmann et al. | Oct 2015 | A1 |
| 20150305239 | Jung | Oct 2015 | A1 |
| 20150327440 | Dybro et al. | Nov 2015 | A1 |
| 20150351320 | Takahara et al. | Dec 2015 | A1 |
| 20150370935 | Starr | Dec 2015 | A1 |
| 20150373902 | Pasquier | Dec 2015 | A1 |
| 20150379785 | Brown, Jr. et al. | Dec 2015 | A1 |
| 20160025531 | Bischoff et al. | Jan 2016 | A1 |
| 20160029558 | Dybro et al. | Feb 2016 | A1 |
| 20160052525 | Tuncer et al. | Feb 2016 | A1 |
| 20160057922 | Freiberg et al. | Mar 2016 | A1 |
| 20160066505 | Bakke et al. | Mar 2016 | A1 |
| 20160073573 | Ethington et al. | Mar 2016 | A1 |
| 20160078375 | Ethington et al. | Mar 2016 | A1 |
| 20160078570 | Ethington et al. | Mar 2016 | A1 |
| 20160088794 | Baumgarten et al. | Mar 2016 | A1 |
| 20160106038 | Boyd et al. | Apr 2016 | A1 |
| 20160084813 | Anderson et al. | May 2016 | A1 |
| 20160146611 | Matthews | May 2016 | A1 |
| 20160202227 | Mathur et al. | Jul 2016 | A1 |
| 20160203657 | Bell et al. | Jul 2016 | A1 |
| 20160212939 | Ouchida et al. | Jul 2016 | A1 |
| 20160215994 | Mewes et al. | Jul 2016 | A1 |
| 20160232621 | Ethington et al. | Aug 2016 | A1 |
| 20160247075 | Mewes et al. | Aug 2016 | A1 |
| 20160247082 | Stehling | Aug 2016 | A1 |
| 20160260021 | Marek | Sep 2016 | A1 |
| 20160286720 | Heitmann et al. | Oct 2016 | A1 |
| 20160286721 | Heitmann et al. | Oct 2016 | A1 |
| 20160286722 | Heitmann et al. | Oct 2016 | A1 |
| 20160309656 | Wilken et al. | Oct 2016 | A1 |
| 20160327535 | Cotton et al. | Nov 2016 | A1 |
| 20160330906 | Acheson et al. | Nov 2016 | A1 |
| 20160338267 | Anderson et al. | Nov 2016 | A1 |
| 20160342915 | Humphrey | Nov 2016 | A1 |
| 20160345485 | Acheson et al. | Dec 2016 | A1 |
| 20160360697 | Diaz | Dec 2016 | A1 |
| 20170013773 | Kirk et al. | Jan 2017 | A1 |
| 20170031365 | Sugumaran et al. | Feb 2017 | A1 |
| 20170034997 | Mayerle | Feb 2017 | A1 |
| 20170049045 | Wilken et al. | Feb 2017 | A1 |
| 20170055433 | Jamison | Mar 2017 | A1 |
| 20170082442 | Anderson | Mar 2017 | A1 |
| 20170083024 | Reijersen Van Buuren | Mar 2017 | A1 |
| 20170086381 | Roell et al. | Mar 2017 | A1 |
| 20170089741 | Takahashi et al. | Mar 2017 | A1 |
| 20170089742 | Bruns et al. | Mar 2017 | A1 |
| 20170090068 | Xiang et al. | Mar 2017 | A1 |
| 20170105331 | Herlitzius et al. | Apr 2017 | A1 |
| 20170105335 | Xu | Apr 2017 | A1 |
| 20170112049 | Weisberg et al. | Apr 2017 | A1 |
| 20170112061 | Meyer | Apr 2017 | A1 |
| 20170115862 | Stratton et al. | Apr 2017 | A1 |
| 20170118915 | Middelberg et al. | May 2017 | A1 |
| 20170124463 | Chen et al. | May 2017 | A1 |
| 20170127606 | Horton | May 2017 | A1 |
| 20170160916 | Baumgarten et al. | Jun 2017 | A1 |
| 20170161627 | Xu et al. | Jun 2017 | A1 |
| 20170185086 | Sauder et al. | Jun 2017 | A1 |
| 20170188515 | Baumgarten et al. | Jul 2017 | A1 |
| 20170192431 | Foster et al. | Jul 2017 | A1 |
| 20170208742 | Ingibergsson et al. | Jul 2017 | A1 |
| 20170213141 | Xu et al. | Jul 2017 | A1 |
| 20170215330 | Missotten et al. | Aug 2017 | A1 |
| 20170223947 | Gall et al. | Aug 2017 | A1 |
| 20170227969 | Murray et al. | Aug 2017 | A1 |
| 20170235471 | Scholer et al. | Aug 2017 | A1 |
| 20170245434 | Jung et al. | Aug 2017 | A1 |
| 20170251600 | Anderson et al. | Sep 2017 | A1 |
| 20170270446 | Starr et al. | Sep 2017 | A1 |
| 20170270616 | Basso | Sep 2017 | A1 |
| 20170316692 | Rusciolelli et al. | Nov 2017 | A1 |
| 20170318743 | Sauder et al. | Nov 2017 | A1 |
| 20170322550 | Yokoyama | Nov 2017 | A1 |
| 20170332551 | Todd et al. | Nov 2017 | A1 |
| 20170336787 | Pichlmaier et al. | Nov 2017 | A1 |
| 20170370765 | Meier et al. | Dec 2017 | A1 |
| 20180000011 | Schleusner et al. | Jan 2018 | A1 |
| 20180014452 | Starr | Jan 2018 | A1 |
| 20180022559 | Knutson | Jan 2018 | A1 |
| 20180024549 | Hurd | Jan 2018 | A1 |
| 20180035622 | Gresch et al. | Feb 2018 | A1 |
| 20180054955 | Oliver | Mar 2018 | A1 |
| 20180060975 | Hassanzadeh | Mar 2018 | A1 |
| 20180070534 | Mayerle | Mar 2018 | A1 |
| 20180077865 | Gallmeier | Mar 2018 | A1 |
| 20180084709 | Wieckhorst et al. | Mar 2018 | A1 |
| 20180084722 | Wieckhorst et al. | Mar 2018 | A1 |
| 20180092301 | Vandike et al. | Apr 2018 | A1 |
| 20180092302 | Vandike et al. | Apr 2018 | A1 |
| 20180108123 | Baurer et al. | Apr 2018 | A1 |
| 20180120133 | Blank et al. | May 2018 | A1 |
| 20180121821 | Parsons et al. | May 2018 | A1 |
| 20180124992 | Koch et al. | May 2018 | A1 |
| 20180128933 | Koch et al. | May 2018 | A1 |
| 20180129879 | Achtelik et al. | May 2018 | A1 |
| 20180132422 | Hassanzadeh et al. | May 2018 | A1 |
| 20180136664 | Tomita et al. | May 2018 | A1 |
| 20180146612 | Sauder et al. | May 2018 | A1 |
| 20180146624 | Chen et al. | May 2018 | A1 |
| 20180153084 | Calleija et al. | Jun 2018 | A1 |
| 20180177125 | Takahara et al. | Jun 2018 | A1 |
| 20180181893 | Basso | Jun 2018 | A1 |
| 20180196438 | Newlin et al. | Jul 2018 | A1 |
| 20180196441 | Muench et al. | Jul 2018 | A1 |
| 20180211156 | Guan et al. | Jul 2018 | A1 |
| 20180232674 | Bilde | Aug 2018 | A1 |
| 20180242523 | Kirchbeck et al. | Aug 2018 | A1 |
| 20180249641 | Lewis et al. | Sep 2018 | A1 |
| 20180257657 | Blank et al. | Sep 2018 | A1 |
| 20180271015 | Redden et al. | Sep 2018 | A1 |
| 20180279599 | Struve | Oct 2018 | A1 |
| 20180295771 | Peters | Oct 2018 | A1 |
| 20180310474 | Posselius et al. | Nov 2018 | A1 |
| 20180317381 | Bassett | Nov 2018 | A1 |
| 20180317385 | Wellensiek et al. | Nov 2018 | A1 |
| 20180325012 | Ferrari et al. | Nov 2018 | A1 |
| 20180325014 | Debbaut | Nov 2018 | A1 |
| 20180332767 | Muench et al. | Nov 2018 | A1 |
| 20180338422 | Brubaker | Nov 2018 | A1 |
| 20180340845 | Rhodes et al. | Nov 2018 | A1 |
| 20180359917 | Blank et al. | Dec 2018 | A1 |
| 20180359919 | Blank et al. | Dec 2018 | A1 |
| 20180364726 | Foster et al. | Dec 2018 | A1 |
| 20190021226 | Dima et al. | Jan 2019 | A1 |
| 20190025175 | Laugwitz | Jan 2019 | A1 |
| 20190041813 | Horn et al. | Feb 2019 | A1 |
| 20190050948 | Perry et al. | Feb 2019 | A1 |
| 20190057460 | Sakaguchi et al. | Feb 2019 | A1 |
| 20190066234 | Bedoya et al. | Feb 2019 | A1 |
| 20190069470 | Pfeiffer et al. | Mar 2019 | A1 |
| 20190075727 | Duke et al. | Mar 2019 | A1 |
| 20190085785 | Abolt | Mar 2019 | A1 |
| 20190090423 | Escher et al. | Mar 2019 | A1 |
| 20190098825 | Neitemeier et al. | Apr 2019 | A1 |
| 20190104722 | Slaughter et al. | Apr 2019 | A1 |
| 20190108413 | Chen et al. | Apr 2019 | A1 |
| 20190114847 | Wagner et al. | Apr 2019 | A1 |
| 20190124819 | Madsen et al. | May 2019 | A1 |
| 20190129430 | Madsen et al. | May 2019 | A1 |
| 20190136491 | Martin et al. | May 2019 | A1 |
| 20190138962 | Ehlmann et al. | May 2019 | A1 |
| 20190147094 | Zhan et al. | May 2019 | A1 |
| 20190147249 | Kiepe et al. | May 2019 | A1 |
| 20190156255 | Carroll | May 2019 | A1 |
| 20190174667 | Gresch et al. | Jun 2019 | A1 |
| 20190183047 | Dybro et al. | Jun 2019 | A1 |
| 20190200522 | Hansen et al. | Jul 2019 | A1 |
| 20190230855 | Reed et al. | Aug 2019 | A1 |
| 20190239416 | Green et al. | Aug 2019 | A1 |
| 20190261550 | Damme et al. | Aug 2019 | A1 |
| 20190261559 | Heitmann et al. | Aug 2019 | A1 |
| 20190261560 | Jelenkovic | Aug 2019 | A1 |
| 20190313570 | Owechko | Oct 2019 | A1 |
| 20190327889 | Borgstadt | Oct 2019 | A1 |
| 20190327892 | Fries et al. | Oct 2019 | A1 |
| 20190335662 | Good et al. | Nov 2019 | A1 |
| 20190335674 | Basso | Nov 2019 | A1 |
| 20190343035 | Smith et al. | Nov 2019 | A1 |
| 20190343043 | Bormann et al. | Nov 2019 | A1 |
| 20190343044 | Bormann et al. | Nov 2019 | A1 |
| 20190343048 | Farley et al. | Nov 2019 | A1 |
| 20190351765 | Rabusic | Nov 2019 | A1 |
| 20190354081 | Blank et al. | Nov 2019 | A1 |
| 20190364733 | Laugen et al. | Dec 2019 | A1 |
| 20190364734 | Kriebel et al. | Dec 2019 | A1 |
| 20200000006 | McDonald et al. | Jan 2020 | A1 |
| 20200008351 | Zielke et al. | Jan 2020 | A1 |
| 20200015416 | Barther et al. | Jan 2020 | A1 |
| 20200019159 | Kocer et al. | Jan 2020 | A1 |
| 20200024102 | Brill et al. | Jan 2020 | A1 |
| 20200029488 | Bertucci et al. | Jan 2020 | A1 |
| 20200034759 | Dumstorff et al. | Jan 2020 | A1 |
| 20200037491 | Schoeny et al. | Feb 2020 | A1 |
| 20200053961 | Dix et al. | Feb 2020 | A1 |
| 20200064144 | Tomita et al. | Feb 2020 | A1 |
| 20200064863 | Tomita et al. | Feb 2020 | A1 |
| 20200074023 | Nizami et al. | Mar 2020 | A1 |
| 20200084963 | Gururajan et al. | Mar 2020 | A1 |
| 20200084966 | Corban et al. | Mar 2020 | A1 |
| 20200090094 | Blank | Mar 2020 | A1 |
| 20200097851 | Alvarez et al. | Mar 2020 | A1 |
| 20200113142 | Coleman et al. | Apr 2020 | A1 |
| 20200125822 | Yang et al. | Apr 2020 | A1 |
| 20200128732 | Chaney | Apr 2020 | A1 |
| 20200128733 | Vandike et al. | Apr 2020 | A1 |
| 20200128734 | Brammeier et al. | Apr 2020 | A1 |
| 20200128735 | Bonefas et al. | Apr 2020 | A1 |
| 20200128737 | Anderson et al. | Apr 2020 | A1 |
| 20200128738 | Suleman et al. | Apr 2020 | A1 |
| 20200128740 | Suleman | Apr 2020 | A1 |
| 20200133262 | Suleman et al. | Apr 2020 | A1 |
| 20200141784 | Lange et al. | May 2020 | A1 |
| 20200146203 | Deng | May 2020 | A1 |
| 20200150631 | Frieberg et al. | May 2020 | A1 |
| 20200154639 | Takahara et al. | May 2020 | A1 |
| 20200163277 | Cooksey et al. | May 2020 | A1 |
| 20200183406 | Borgstadt | Jun 2020 | A1 |
| 20200187409 | Meyer Zu Helligen | Jun 2020 | A1 |
| 20200196526 | Koch et al. | Jun 2020 | A1 |
| 20200202596 | Kitahara et al. | Jun 2020 | A1 |
| 20200221632 | Strnad et al. | Jul 2020 | A1 |
| 20200221635 | Hendrickson et al. | Jul 2020 | A1 |
| 20200221636 | Boydens et al. | Jul 2020 | A1 |
| 20200265527 | Rose et al. | Aug 2020 | A1 |
| 20200278680 | Schultz et al. | Sep 2020 | A1 |
| 20200317114 | Hoff | Oct 2020 | A1 |
| 20200319632 | Desai et al. | Oct 2020 | A1 |
| 20200319655 | Desai et al. | Oct 2020 | A1 |
| 20200323133 | Anderson et al. | Oct 2020 | A1 |
| 20200323134 | Darr et al. | Oct 2020 | A1 |
| 20200326674 | Palla et al. | Oct 2020 | A1 |
| 20200326727 | Palla et al. | Oct 2020 | A1 |
| 20200333278 | Locken et al. | Oct 2020 | A1 |
| 20200337232 | Blank et al. | Oct 2020 | A1 |
| 20200352099 | Meier et al. | Nov 2020 | A1 |
| 20200359547 | Sakaguchi et al. | Nov 2020 | A1 |
| 20200359549 | Sakaguchi et al. | Nov 2020 | A1 |
| 20200363256 | Meier et al. | Nov 2020 | A1 |
| 20200375083 | Anderson et al. | Dec 2020 | A1 |
| 20200375084 | Sakaguchi et al. | Dec 2020 | A1 |
| 20200378088 | Anderson | Dec 2020 | A1 |
| 20200404842 | Dugas et al. | Dec 2020 | A1 |
| 20210015041 | Bormann et al. | Jan 2021 | A1 |
| 20210129853 | Appleton et al. | May 2021 | A1 |
| 20210176916 | Sidon et al. | Jun 2021 | A1 |
| 20210176918 | Franzen et al. | Jun 2021 | A1 |
| 20210289687 | Heinold et al. | Sep 2021 | A1 |
| 20210321567 | Sidon et al. | Oct 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 108898 | Oct 2018 | AR |
| 20100224431 | Apr 2011 | AU |
| MU6800140 | Dec 1989 | BR |
| PI0502658 | Feb 2007 | BR |
| PI0802384 | Mar 2010 | BR |
| PI1100258 | Mar 2014 | BR |
| 102014007178 | Aug 2016 | BR |
| 1165300 | Apr 1984 | CA |
| 2283767 | Mar 2001 | CA |
| 2330979 | Aug 2001 | CA |
| 2629555 | Nov 2009 | CA |
| 135611 | May 2011 | CA |
| 2451633 | Oct 2001 | CN |
| 101236188 | Aug 2008 | CN |
| 100416590 | Sep 2008 | CN |
| 101303338 | Nov 2008 | CN |
| 101363833 | Feb 2009 | CN |
| 201218789 | Apr 2009 | CN |
| 101839906 | Sep 2010 | CN |
| 101929166 | Dec 2010 | CN |
| 102080373 | Jun 2011 | CN |
| 102138383 | Aug 2011 | CN |
| 102277867 | Dec 2011 | CN |
| 202110103 | Jan 2012 | CN |
| 202119772 | Jan 2012 | CN |
| 202340435 | Jul 2012 | CN |
| 103088807 | May 2013 | CN |
| 103181263 | Jul 2013 | CN |
| 203053961 | Jul 2013 | CN |
| 203055121 | Jul 2013 | CN |
| 203206739 | Sep 2013 | CN |
| 102277867 | Oct 2013 | CN |
| 203275401 | Nov 2013 | CN |
| 203613525 | May 2014 | CN |
| 203658201 | Jun 2014 | CN |
| 103954738 | Jul 2014 | CN |
| 203741803 | Jul 2014 | CN |
| 204000818 | Dec 2014 | CN |
| 204435344 | Jul 2015 | CN |
| 204475304 | Jul 2015 | CN |
| 105205248 | Dec 2015 | CN |
| 204989174 | Jan 2016 | CN |
| 105432228 | Mar 2016 | CN |
| 105741180 | Jul 2016 | CN |
| 106053330 | Oct 2016 | CN |
| 106163261 | Nov 2016 | CN |
| 106198877 | Dec 2016 | CN |
| 106198879 | Dec 2016 | CN |
| 106226470 | Dec 2016 | CN |
| 106248873 | Dec 2016 | CN |
| 106290800 | Jan 2017 | CN |
| 106327349 | Jan 2017 | CN |
| 106644663 | May 2017 | CN |
| 206330815 | Jul 2017 | CN |
| 206515118 | Sep 2017 | CN |
| 206515119 | Sep 2017 | CN |
| 206616118 | Nov 2017 | CN |
| 206696107 | Dec 2017 | CN |
| 206696107 | Dec 2017 | CN |
| 107576674 | Jan 2018 | CN |
| 107576674 | Jan 2018 | CN |
| 206906093 | Jan 2018 | CN |
| 206941558 | Jan 2018 | CN |
| 206941558 | Jan 2018 | CN |
| 107736088 | Feb 2018 | CN |
| 107795095 | Mar 2018 | CN |
| 207079558 | Mar 2018 | CN |
| 107941286 | Apr 2018 | CN |
| 107957408 | Apr 2018 | CN |
| 108009542 | May 2018 | CN |
| 108304796 | Jul 2018 | CN |
| 207567744 | Jul 2018 | CN |
| 108614089 | Oct 2018 | CN |
| 208013131 | Oct 2018 | CN |
| 108881825 | Nov 2018 | CN |
| 208047351 | Nov 2018 | CN |
| 109357804 | Feb 2019 | CN |
| 109485353 | Mar 2019 | CN |
| 109633127 | Apr 2019 | CN |
| 109763476 | May 2019 | CN |
| 109961024 | Jul 2019 | CN |
| 110262287 | Sep 2019 | CN |
| 110720302 | Jan 2020 | CN |
| 111201879 | May 2020 | CN |
| 210585958 | May 2020 | CN |
| 111406505 | Jul 2020 | CN |
| 111667017 | Sep 2020 | CN |
| 247426 | Dec 1986 | CS |
| 248318 | Feb 1987 | CS |
| 17266 | Feb 2007 | CZ |
| 20252 | Nov 2009 | CZ |
| 441597 | Mar 1927 | DE |
| 504035 | Jul 1930 | DE |
| 2354828 | May 1975 | DE |
| 152380 | Nov 1981 | DE |
| 3728669 | Mar 1989 | DE |
| 4431824 | May 1996 | DE |
| 19509496 | Sep 1996 | DE |
| 19528663 | Feb 1997 | DE |
| 19718455 | Nov 1997 | DE |
| 19705842 | Aug 1998 | DE |
| 19828355 | Jan 2000 | DE |
| 10050224 | Apr 2002 | DE |
| 10120173 | Oct 2002 | DE |
| 202004015141 | Dec 2004 | DE |
| 102005000770 | Jul 2006 | DE |
| 102005000771 | Aug 2006 | DE |
| 102008021785 | Nov 2009 | DE |
| 102009041646 | Mar 2011 | DE |
| 102010004648 | Jul 2011 | DE |
| 102010038661 | Feb 2012 | DE |
| 102011005400 | Sep 2012 | DE |
| 202012103730 | Oct 2012 | DE |
| 102011052688 | Feb 2013 | DE |
| 102012211001 | Jan 2014 | DE |
| 102012220109 | May 2014 | DE |
| 102012223768 | Jun 2014 | DE |
| 102013212151 | Dec 2014 | DE |
| 102013019098 | Jan 2015 | DE |
| 102014108449 | Feb 2015 | DE |
| 2014201203 | Jul 2015 | DE |
| 102014208068 | Oct 2015 | DE |
| 102015006398 | May 2016 | DE |
| 102015109799 | Dec 2016 | DE |
| 112015002194 | Jan 2017 | DE |
| 102017204511 | Sep 2018 | DE |
| 102019206734 | Nov 2020 | DE |
| 102019114872 | Dec 2020 | DE |
| 0070219 | Oct 1984 | EP |
| 0355049 | Feb 1990 | EP |
| 845198 | Jun 1998 | EP |
| 0532146 | Aug 1998 | EP |
| 1444879 | Aug 2004 | EP |
| 1219159 | Jun 2005 | EP |
| 1219153 | Feb 2006 | EP |
| 1692928 | Aug 2006 | EP |
| 1574122 | Feb 2008 | EP |
| 1943877 | Jul 2008 | EP |
| 1598586 | Sep 2009 | EP |
| 1731983 | Sep 2009 | EP |
| 2146307 | Jan 2010 | EP |
| 0845198 | Feb 2010 | EP |
| 2186389 | May 2010 | EP |
| 2267566 | Dec 2010 | EP |
| 3491192 | Dec 2010 | EP |
| 2057884 | Jan 2011 | EP |
| 2146307 | May 2012 | EP |
| 2446732 | May 2012 | EP |
| 2524586 | Nov 2012 | EP |
| 2529610 | Dec 2012 | EP |
| 2243353 | Mar 2013 | EP |
| 2174537 | May 2013 | EP |
| 2592919 | May 2013 | EP |
| 1674324 | May 2014 | EP |
| 2759829 | Jul 2014 | EP |
| 2764764 | Aug 2014 | EP |
| 2267566 | Dec 2014 | EP |
| 2191439 | Mar 2015 | EP |
| 2586286 | Mar 2015 | EP |
| 2592919 | Sep 2015 | EP |
| 2921042 | Sep 2015 | EP |
| 2944725 | Nov 2015 | EP |
| 2764764 | Dec 2015 | EP |
| 2510777 | Mar 2016 | EP |
| 2997805 | Mar 2016 | EP |
| 3000302 | Mar 2016 | EP |
| 2868806 | Jul 2016 | EP |
| 3085221 | Oct 2016 | EP |
| 3095310 | Nov 2016 | EP |
| 3097759 | Nov 2016 | EP |
| 2452551 | May 2017 | EP |
| 3175691 | Jun 2017 | EP |
| 3195719 | Jul 2017 | EP |
| 3195720 | Jul 2017 | EP |
| 3259976 | Dec 2017 | EP |
| 3262934 | Jan 2018 | EP |
| 3491192 | Jan 2018 | EP |
| 3287007 | Feb 2018 | EP |
| 3298876 | Mar 2018 | EP |
| 3300579 | Apr 2018 | EP |
| 3315005 | May 2018 | EP |
| 3316208 | May 2018 | EP |
| 2829171 | Jun 2018 | EP |
| 2508057 | Jul 2018 | EP |
| 2508057 | Jul 2018 | EP |
| 3378298 | Sep 2018 | EP |
| 3378299 | Sep 2018 | EP |
| 2997805 | Oct 2018 | EP |
| 3384754 | Oct 2018 | EP |
| 3446559 | Feb 2019 | EP |
| 3289853 | Mar 2019 | EP |
| 3456167 | Mar 2019 | EP |
| 3466239 | Apr 2019 | EP |
| 3469878 | Apr 2019 | EP |
| 3289852 | Jun 2019 | EP |
| 3491192 | Jun 2019 | EP |
| 3494770 | Jun 2019 | EP |
| 3498074 | Jun 2019 | EP |
| 3000302 | Aug 2019 | EP |
| 3533314 | Sep 2019 | EP |
| 3569049 | Nov 2019 | EP |
| 3000307 | Dec 2019 | EP |
| 3586592 | Jan 2020 | EP |
| 3593613 | Jan 2020 | EP |
| 3593620 | Jan 2020 | EP |
| 3613272 | Feb 2020 | EP |
| 3613272 | Feb 2020 | EP |
| 3243374 | Mar 2020 | EP |
| 3626038 | Mar 2020 | EP |
| 3259976 | Apr 2020 | EP |
| 3635647 | Apr 2020 | EP |
| 3378298 | May 2020 | EP |
| 3646699 | May 2020 | EP |
| 3662741 | Jun 2020 | EP |
| 3685648 | Jul 2020 | EP |
| 2995191 | Oct 2020 | EP |
| 2116215 | Jul 1998 | ES |
| 2311322 | Feb 2009 | ES |
| 5533 | Nov 1913 | FI |
| 1451480 | Jan 1966 | FR |
| 2817344 | May 2002 | FR |
| 2901291 | Nov 2007 | FR |
| 2901291 | Nov 2007 | FR |
| 901081 | Jul 1962 | GB |
| 201519517 | May 2017 | GB |
| 01632DE2014 | Aug 2016 | IN |
| 1632DE2014 | Aug 2016 | IN |
| 201641027017 | Oct 2016 | IN |
| 202041039250 | Sep 2020 | IN |
| 7079681 | Nov 1982 | JP |
| S60253617 | Dec 1985 | JP |
| S63308110 | Dec 1988 | JP |
| H02196960 | Aug 1990 | JP |
| H02215311 | Aug 1990 | JP |
| H0779681 | Mar 1995 | JP |
| H1066436 | Mar 1998 | JP |
| H10191762 | Jul 1998 | JP |
| 2000352044 | Dec 2000 | JP |
| 2001057809 | Mar 2001 | JP |
| 2002186348 | Jul 2002 | JP |
| 2005227233 | Aug 2005 | JP |
| 2006166871 | Jun 2006 | JP |
| 2011205967 | Oct 2011 | JP |
| 2015070812 | Apr 2015 | JP |
| 2015151826 | Aug 2015 | JP |
| 2015219651 | Dec 2015 | JP |
| 2016071726 | May 2016 | JP |
| 2016160808 | Sep 2016 | JP |
| 6087258 | Mar 2017 | JP |
| 2017136035 | Aug 2017 | JP |
| 2017137729 | Aug 2017 | JP |
| 2017195804 | Nov 2017 | JP |
| 2018068284 | May 2018 | JP |
| 2018102154 | Jul 2018 | JP |
| 2018151388 | Sep 2018 | JP |
| 2018151845 | Sep 2018 | JP |
| 2019004796 | Jan 2019 | JP |
| 2019129744 | Aug 2019 | JP |
| 2019146506 | Sep 2019 | JP |
| 2019216744 | Dec 2019 | JP |
| 2020018255 | Feb 2020 | JP |
| 2020031607 | Mar 2020 | JP |
| 2020113062 | Jul 2020 | JP |
| 2020127405 | Aug 2020 | JP |
| 100974892 | Aug 2010 | KR |
| 100974892 | Aug 2010 | KR |
| 20110018582 | Feb 2011 | KR |
| 101067576 | Sep 2011 | KR |
| 101067576 | Sep 2011 | KR |
| 101134075 | Apr 2012 | KR |
| 101447197 | Oct 2014 | KR |
| 101653750 | Sep 2016 | KR |
| 20170041377 | Apr 2017 | KR |
| 200485051 | Nov 2017 | KR |
| 200485051 | Nov 2017 | KR |
| 101873657 | Aug 2018 | KR |
| GT06000012 | Jan 2008 | MX |
| 178299 | Apr 2000 | PL |
| 130713 | Nov 2015 | RO |
| 1791767 | Jan 1993 | RU |
| 2005102554 | Jul 2006 | RU |
| 2421744 | Jun 2011 | RU |
| 2421744 | Jun 2011 | RU |
| 2447640 | Apr 2012 | RU |
| 2502047 | Dec 2013 | RU |
| 2502047 | Dec 2013 | RU |
| 164128 | Aug 2016 | RU |
| 2017114139 | Apr 2017 | RU |
| 2017114139 | Oct 2018 | RU |
| 2017114139 | May 2019 | RU |
| 834514 | May 1981 | SU |
| 887717 | Dec 1981 | SU |
| 1052940 | Nov 1983 | SU |
| 1134669 | Jan 1985 | SU |
| 1526588 | Dec 1989 | SU |
| 1540053 | Jan 1991 | SU |
| 1761864 | Sep 1992 | SU |
| 1986005353 | Sep 1986 | WO |
| 2001052160 | Jul 2001 | WO |
| 2002015673 | Feb 2002 | WO |
| 2003005803 | Jan 2003 | WO |
| 2007050192 | May 2007 | WO |
| 2009156542 | Dec 2009 | WO |
| 2010003421 | Jan 2010 | WO |
| 2011104085 | Sep 2011 | WO |
| 2012041621 | Apr 2012 | WO |
| 2012110508 | Aug 2012 | WO |
| 2012110544 | Aug 2012 | WO |
| 2013063106 | May 2013 | WO |
| 2013079247 | Jun 2013 | WO |
| 2013086351 | Jun 2013 | WO |
| 2013087275 | Jun 2013 | WO |
| 2014046685 | Mar 2014 | WO |
| 2014093814 | Jun 2014 | WO |
| 2014195302 | Dec 2014 | WO |
| 2015038751 | Mar 2015 | WO |
| 2015153809 | Oct 2015 | WO |
| 16020595 | Feb 2016 | WO |
| 2016020595 | Feb 2016 | WO |
| 2016118686 | Jul 2016 | WO |
| 2017008161 | Jan 2017 | WO |
| 2017060168 | Apr 2017 | WO |
| 2017077113 | May 2017 | WO |
| 2017096489 | Jun 2017 | WO |
| 2017099570 | Jun 2017 | WO |
| 2017116913 | Jul 2017 | WO |
| 2017170507 | Oct 2017 | WO |
| 2017205406 | Nov 2017 | WO |
| 2017205410 | Nov 2017 | WO |
| 2018043336 | Mar 2018 | WO |
| 2018073060 | Apr 2018 | WO |
| 2018081759 | May 2018 | WO |
| 2018112615 | Jun 2018 | WO |
| 2018116772 | Jun 2018 | WO |
| 2018142768 | Aug 2018 | WO |
| 2018200870 | Nov 2018 | WO |
| 2018206587 | Nov 2018 | WO |
| 2018220159 | Dec 2018 | WO |
| 2018226139 | Dec 2018 | WO |
| 2018235486 | Dec 2018 | WO |
| 2018235942 | Dec 2018 | WO |
| WO18235486 | Dec 2018 | WO |
| 2019034213 | Feb 2019 | WO |
| 2019079205 | Apr 2019 | WO |
| 2019081349 | May 2019 | WO |
| 2019091535 | May 2019 | WO |
| 2019109191 | Jun 2019 | WO |
| 2019124174 | Jun 2019 | WO |
| 2019124217 | Jun 2019 | WO |
| 2019124225 | Jun 2019 | WO |
| 2019124273 | Jun 2019 | WO |
| 2019129333 | Jul 2019 | WO |
| 2019129334 | Jul 2019 | WO |
| 2019129335 | Jul 2019 | WO |
| 2019215185 | Nov 2019 | WO |
| 2019230358 | Dec 2019 | WO |
| 2020026578 | Feb 2020 | WO |
| 2020026650 | Feb 2020 | WO |
| 2020026651 | Feb 2020 | WO |
| 2020031473 | Feb 2020 | WO |
| 2020038810 | Feb 2020 | WO |
| 2020039312 | Feb 2020 | WO |
| 2020039671 | Feb 2020 | WO |
| 2020044726 | Mar 2020 | WO |
| 2020082182 | Apr 2020 | WO |
| 2020100810 | May 2020 | WO |
| 2020110920 | Jun 2020 | WO |
| 2020195007 | Oct 2020 | WO |
| 2020206941 | Oct 2020 | WO |
| 2020206942 | Oct 2020 | WO |
| 2020210607 | Oct 2020 | WO |
| 2020221981 | Nov 2020 | WO |
| 2021262500 | Dec 2021 | WO |
| Entry |
|---|
| English translation of EP-3613272-A1. |
| English translation of EP-3446559-A1. |
| English translation of CN-106163261-A. |
| English translation of CN-111667017-A. |
| English translation of JP-2018151845-A. |
| Leu et al., Grazing Corn Residue Using Resources and Reducing Costs, Aug. 2009, 4 pages. |
| “No-Till Soils”, Soil Heath Brochure, 2 pages, last accessed Jul. 14, 2020. |
| Strickland et al., “Nitrate Toxicity in Livestock” Oklahoma State University, Feb. 2017, 2 pages. |
| Strickland et al., “Nitrate Toxicity in Livestock” Oklahoma State University, 8 pages, Feb. 2017. |
| Brownlee, “Neural Networks are Function Approximation Algorithms”, Mar. 18, 2020, 13 pages. |
| Thompson, “Morning glory can make it impossible to harvest corn”, Feb. 19, 2015, 3 pages. |
| Tumlison, “Monitoring Growth Development and Yield Estimation of Maize Using Very High-Resolution Uavimages in Gronau, Germany”, Feb. 2017, 63 pages. |
| Hunt, “Mapping Weed Infestations Using Remote Sensing”, 8 pages, Jul. 19, 2005. |
| Wright, et al., “Managing Grain Protein in Wheat Using Remote Sensing”, 12 pages, 2003. |
| “Malting Barley in Pennsylvania”, Agronomy Facts 77, 6 pages, Code EE0179 Jun. 2016. |
| “Green stem syndrome in soybeans”, Agronomy eUpdate Issue 478 Oct. 10, 2014, 3 pages. |
| “Keep Weed Seed Out of Your Harvest”, Aug. 8, 2019, 1 pages. |
| Hodrius et al., “The Impact of Multi-Sensor Data Assimilation on Plant Parameter Retrieval and Yield Estimation for Sugar Beet”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-7/W3, 2015, 36th International Symposium on Remote Sensing of Environment, May 11-15, 2015, Berlin, Germany, 7 pages. |
| Fernandez-Quintanilla et al., “Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops?”, Feb. 2018, 35 pages. |
| Anonymously, “Improved System and Method for Controlling Agricultural Vehicle Operation Using Historical Data”, Dec. 16, 2009, 8 pages. |
| Anonymously, “System and Method for Controlling Agricultural Vehicle Operation Using Historical Data”, Jun. 30, 2009, 8 pages. |
| “Leafsnap, a new mobile app that identifies plants by leaf shape, is launched by Smithsonian and collaborators”, May 2, 2011, 5 pages. |
| Insect Gallery, Department of Entomology, Kansas State University, Oct. 19, 2020, 8 pages. |
| Licht, “Influence of Corn Seeding Rate, Soil Attributes, and Topographic Characteristics on Grain Yield, Yield Components, and Grain Composition”, 2015, 107 pages. |
| “Notice of Retraction Virtual simulation of plant with individual stem based on crop growth model”Mar. 5, 2017, 7 pages. |
| Ma et al., “Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis”, Dec. 19, 2019, 15 pages. |
| Leland, “Who Did that? Identifying Insect Damage”, Apr. 1, 2015, 4 pages. |
| “How to improve maize protein content” https://www.yara.co.uk/crop-nutrition/forage-maize/improving-maize-protein-content, Sep. 30, 2020, 10 pages. |
| Hafemeister, “Weed control at harvest, combines are ideal vehicles for spreading weed seeds”, Sep. 25, 2019, 3 pages. |
| “Harvesting Tips”, Northern Pulse Growers Association, 9 pages, Jan. 31, 2001. |
| Wortmann et al., “Harvesting Crop Residues”, Aug. 10, 2020, 8 pages. |
| “Harvesting”, Oklahoma State University, Canola Swathing Guide, 2010, 9 pages, last accessed Jul. 14, 2020. |
| Hanna, “Harvest Tips for Lodged Corn”, Sep. 6, 2011, 3 pages. |
| “Green Weeds Complicate Harvest”, Crops, Slider, Sep. 26, 2012, 2 pages. |
| “Agrowatch Green Vegetation Index”, Retrieved Dec. 11, 2020, 4 pages. |
| “Grazing Corn Residues” (http://www.ca.uky.edu), 3 pages, Aug. 24, 2009. |
| Jarnevich et al., Forecasting Weed Distributions Using Climate Data: A GIS Early Warning Tool, Downloaded on Jul. 13, 2020, 12 pages. |
| Combine Cutting and Feeding Mechanisms in the Southeast, By J-K Park, Agricultural Research Service, U.S. Dept. of Agriculture, 1963, 1 page. |
| Hartzler, “Fate of weed seeds in the soil”, 4 pages, Jan. 31, 2001. |
| Digman, “Combine Considerations for a Wet Corn Harvest”, Extension SpecialistUW—Madison, 3 pages, Oct. 29, 2009. |
| S-Series Combine and Front End Equipment Optimization, John Deere Harvester Works, 20 pages Date: Oct. 9, 2017. |
| Determining yield monitoring system delay time with geostatistical and data segmentation approaches (https://www.ars.usda.gov/ARSUserFiles/50701000/cswq-0036-128359.pdf) Jul. 2002, 13 pages. |
| Precision Agriculture: Yield Monitors (dated Nov. 1998—metadata; last accessed Jul. 16, 2020) (https://extensiondata.missouri.edu/pub/pdf/envqual/wq0451.pdf) 4 pages. |
| Paul et al., “Effect of soil water status and strength on trafficability” (1979) (https://www.nrcresearchpress.com/doi/pdfplus/10.4141/cjss79-035), 12 pages, Apr. 23, 1979. |
| Sick, “Better understanding corn hybrid characteristics and properties can impact your seed decisions” (https://emergence.fbn.com/agronomy/corn-hybrid-characteristics-and-properties-impact-seed-decisions) By Steve Sick, FBN Breeding Project Lead | Sep. 21, 2018, 8 pages. |
| Robertson et al., “Maize Stalk Lodging: Morphological Determinants of Stalk Strength” Mar. 2017, 10 pages. |
| Martin, et al., “Breakage Susceptibility and Hardness of Corn Kernels of Various Sizes and Shapes”, May 1987, 10 Pages. |
| Extended European Search Report and Written Opinion issued in European Patent Application No. 20208171.7, dated May 11, 2021, in 05 pages. |
| Cordoba, M.A., Bruno, C.I. Costa, J.L. Peralta, N.R. and Balzarini, M.G., 2016, Protocol for multivariate homegeneous zone delineation in precision agriculture, biosystems engineering, 143, pp. 95-107. |
| Pioneer Estimator, “Corn Yield Estimator” accessed on Feb. 13, 2018, 1 page. retrieved from: https://www.pioneer.com/home/site/us/tools-apps/growing-tools/corn-yield-estimator/. |
| Guindin, N. “Estimating Maize Grain Yield From Crop Biophysical Parameters Using Remote Sensing”, Nov. 4, 2013, 19 pages. |
| EP Application No. 19203883.4-1004 Office Action dated May 3, 2021, 4 pages. |
| Iowa State University Extension and Outreach, “Harvest Weed Seed Control”, Dec. 13, 2018, 6 pages. https://crops.extension.iastate.edu/blog/bob-hartzler/harvest-weed-seed-control. |
| Getting Rid Of WeedsThrough Integrated Weed Management, accessed on Jun. 25, 2021, 10 pages. https://integratedweedmanagement.org/index.php/iwm-toolbox/the-harrington-seed-destructor. |
| The Importance of Reducing Weed Seeds, Jul. 2018, 2 pages. https://www.aphis.usda.gov/plant_health/soybeans/soybean-handouts.pdf. |
| Alternative Crop Guide, Published by the Jefferson Institute, “Buckwheat”, Revised Jul. 2002. 4 pages. |
| Application and Drawings for U.S. Appl. No. 16/175,993, filed Oct. 31, 2018, 28 pages. |
| Application and Drawings for U.S. Appl. No. 16/380,623, filed Apr. 10, 2019, 36 pages. |
| Application and Drawings for U.S. Appl. No. 16/783,511, filed Feb. 6, 2020, 55 pages. |
| “Automated Weed Detection With Drones” dated May 25, 2017, retrieved at: <<https://www.precisionhawk.com/blog/media/topic/automated-weed-identification-with-drones>>, retrieved on Jan. 21, 2020, 4 pages. |
| F. Forcella, “Estimating the Timing of Weed Emergence”, Site-Specific Management Guidelines, retrieved at: <<http://www.ipni.net/publication/ssmg.nsf/0/D26EC9A906F9B8C9852579E500773936/$FILE/SSMG-20.pdf>>, retrieved on Jan. 21, 2020, 4 pages. |
| Chauhan et al., “Emerging Challenges and Opportunities for Education and Research in Weed Science”, frontiers in Plant Science. Published online Sep. 5, 2017, 22 pages. |
| Apan, A., Wells ,N., Reardon-Smith, K, Richardson, L, McDougall, K, and Basnet, B.B., 2008. Predictive mapping of blackberry in the Condamine Catchment using logistic regression and spatial analysis. In Proceedings of the 2008 Queensland Spatial Conference: Global Warning: What's Happening in Paradise. Spatial Sciences Institute, 11 pages. |
| Jarnevich, C.S., Holcombe, T.R., Barnett, D.T., Stohlgren, T.J. and Kartesz, J.T., 2010. Forecasting weed distributions using climate data: a GIS early warning tool. Invasive Plant Science and Management. 3(4), pp. 365-375. |
| Sa et al., “WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming”, Sep. 6, 2018, 25 pages. |
| Pflanz et al., “Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier”, Published Sep. 24, 2018, 28 pages. |
| Provisional Application and Drawings for U.S. Appl. No. 62/928,964, filed Oct. 31, 2019, 14 pages. |
| Application and Drawings for U.S. Appl. No. 16/783,475, filed Feb. 6, 2020, 55 pages. |
| U.S. Appl. No. 17/067,483 Application and Drawings as filed on Oct. 9, 2020, 63 pages. |
| U.S. Appl. No. 17/066,442 Application and Drawings as filed on Oct. 8, 2020, 65 pages. |
| U.S. Appl. No. 16/380,550, filed Apr. 10, 2019, Application and Drawings, 47 pages. |
| U.S. Appl. No. 17/066,999 Application and Drawings as filed on Oct. 9, 2020, 67 pages. |
| U.S. Appl. No. 17/066,444 Application and Drawings as filed on Oct. 8, 2020, 102 pages. |
| Extended Search Report for European Patent Application No. 20167930.5 dated Sep. 15, 2020, 8 pages. |
| Extended Search Report for European Patent Application No. 19205901.2 dated Mar. 17, 2020, 6 pages. |
| Notice of Allowance for U.S. Appl. No. 16/171,978, dated Dec. 15, 2020, 21 pages. |
| Zhigen et al., “Research of the Combine Harvester Load Feedback Control System Using Multi-Signal Fusion Method and Fuzzy Algorithm,” 2010, Publisher: IEEE, 5 pages. |
| Dan et al., “On-the-go Throughput Prediction in a Combine Harvester Using Sensor Fusion,” 2017, Publisher: IEEE, 6 pages. |
| Fernandez-Quintanilla et al., “Is the current state of the art of weed monitoring sutible for site-specific weed management in arable crops?”, First Published May 1, 2018, 4 pages. |
| Dionysis Bochtis et al. “Field Operations Planning for Agricultural Vehicles: A Hierarchical Modeling Framework.” Agricultural Engineering International: the CIGR Ejournal. Manuscript PM 06 021. vol. IX. Feb. 2007, pp. 1-11. |
| U.S. Appl. No. 16/432,557, filed Jun. 5, 2019, 61 pages. |
| European Search Report issued in counterpart European Patent Application No. 19205142.3 dated Feb. 28, 2020 (6 pages). |
| Mei-Ju et al., “Two paradigms in cellular Internet-of-Things access for energy-harvesting machine-to-machine devices: push-based versus pull-based,” 2016, vol. 6, 9 pages. |
| Yi et al., “An Efficient MAC Protocol With Adaptive Energy Harvesting for Machine-to-Machine Networks,” 2015, vol. 3, Publisher: IEEE, 10 pages. |
| Application and Drawings for U.S. Appl. No. 16/171,978, filed Oct. 26, 2018, 53 pages. |
| European Search Report issued in European Patent Application No. 19203883.4 dated Mar. 23, 2020 (10 pages). |
| Notice of Allowance for U.S. Appl. No. 16/171,978 dated Oct. 28, 2020, 5 pages. |
| Notice of Allowance for U.S. Appl. No. 16/171,978, dated Aug. 7, 2020, 9 pages. |
| K.R. Manjunath et al., “Developing Spectral Library of Major Plant Species of Western Himalayas Using Ground Observations”, J. Indian Soc Remote Sen (Mar. 2014) 42(a):201-216, 17 pages. |
| U.S. Appl. No. 16/380,564 Application and Drawings as filed on Apr. 10, 2019, 55 pages. |
| S. Veenadhari et al., “Machine Learning Approach For Forecasting Crop Yield Based on Climatic Parameters”, 2014 International Conference on Computer Communication and Informatics (ICCCI-2014) Jan. 3-6, 2014, Coimbatore, India, 5 pages. |
| Non-Final Office Action for U.S. Appl. No. 16/380,531 dated Oct. 21, 2020, 10 pages. |
| U.S. Appl. No. 16/380,531 Application and Drawings as filed on Apr. 10, 2019, 46 pages. |
| Apan et al., “Predictive Mapping of Blackberry in the Condamine Catchment Using Logistic Regressiona dn Spatial Analysis”, Jan. 2008, 12 pages. |
| Robson, “Remote Sensing Applications for the Determination of Yield, Maturity and Aflatoxin Contamination in Peanut”, Oct. 2007, 275 pages. |
| Bhattarai et al., “Remote Sensing Data to Detect Hessian Fly Infestation in Commercial Wheat Fields”, Apr. 16, 2019, 8 pages. |
| Towery, et al., “Remote Sensing of Crop Hail Damage”, Jul. 21, 1975, 31 pages. |
| Sa et al., “WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming”, Sep. 7, 2018, 25 pages. |
| Mathyam et al., “Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management”, Dec. 2011, 30 pages. |
| Martinez-Feria et al., “Evaluating Maize and Soybean Grain Dry-Down In The Field With Predictive Algorithms and Genotype-by-Environmental Analysis”, May 9, 2019, 13 pages. |
| “GIS Maps for Agriculture”, Precision Agricultural Mapping, Retrieved Dec. 11, 2020, 6 pages. |
| Paul, “Scabby Wheat Grain? Increasing Your Fan Speed May Help”, https://agcrops.osu.edu/newsletter/corn-newsletter/2015-20/scabby-wheat-grain-increasing-yourfan-speed-may-help, C.O.R.N Newsletter//2015-20, 3 pages. |
| Clay et al., “Scouting for Weeds”, SSMG-15, 4 pages, 2002. |
| Taylor et al., “Sensor-Based Variable Rate Application for Cotton”, 8 pages, 2010. |
| Christiansen et al., “Designing and Testing a UAV Mapping System for Agricultural Field Surveying”, Nov. 23, 2017, 19 pages. |
| Haung et al., “AccurateWeed Mapping and Prescription Map Generation Based on Fully Convolutional Networks Using UAV Imagery”, Oct. 1, 2018, 12 pages. |
| Morrison, “Should You Use Tillage to Control Resistant Weeds”, Aug. 29, 2014, 9 pages. |
| Morrison, “Snow Trapping Snars Water”, Oct. 13, 2005, 3 pages. |
| “Soil Zone Index”, https://www.satimagingcorp.com/applications/natural-resources/agricultu . . . , Retrieved Dec. 11, 2020, 5 pages. |
| Malvic, “Soybean Cyst Nematode”, University of Minnesota Extension, Oct. 19, 2020, 3 pages. |
| Unglesbee, “Soybean Pod Shatter—Bad Enough to Scout Before Harvest?—DTN”, Oct. 17, 2018, 4 pages. |
| Tao, “Standing Crop Residue Can Reduce Snow Drifting and Increase Soil Moisture”, 2 pages, last accessed Jul. 14, 2020. |
| Berglund, et al., “Swathing and Harvesting Canola”, Jul. 2019, 8 pages. |
| Bell et al., “Synthetic Aperture Radar and Optical Remote Sensing of Crop Damage Attributed to Severe Weather in the Central United States”, Jul. 25, 2018, 1 page. |
| Rosencrance, “Tabletop Grapes in India to Be Picked by Virginia Tech Robots”, Jul. 23, 2020, 8 pages. |
| Lofton, et al., The Potential of Grazing Grain Sorghum Residue Following Harvest, May 13, 2020, 11 pages. |
| Beal et al., “Time Shift Evaluation to Improve Yield Map Quality”, Published in Applied Engineering in Agriculture vol. 17(3): 385-390 (© 2001 American Society of Agricultural Engineers ), 9 pages. |
| “Tips and Tricks of Harvesting High Moisture Grain”, https://www.koenigequipment.com/blog/tips-and-tricks-of-harvesting-highmoisture-grain, 7 pages, last accessed Jul. 14, 2020. |
| Ransom, “Tips for Planting Winter Wheat and Winter Rye (for Grain) (Aug. 15, 2019)”, 2017, 3 pages. |
| AgroWatch Tree Grading Maps, “The Grading Maps and Plant Count Reports”, https://www.satimagingcorp.com/applications/natural-resources/agricultu . . . , Retrieved Dec. 11, 2020, 4 pages. |
| Ackley, “Troubleshooting Abnormal Corn Ears”, Jul. 23, 2020, 25 pages. |
| Smith, “Understanding Ear Flex”, Feb. 25, 2019, 17 pages. |
| Carroll et al., “Use of Spectral Vegetation Indicies Derived from Airborne Hyperspectral Imagery For Detection of European Corn Borer Infestation in Iowa Corn Plots”, Nov. 2008, 11 pages. |
| Agriculture, “Using drones in agriculture and capturing actionable data”, Retrieved Dec. 11, 2020, 18 pages. |
| Bentley et al., “Using Landsat to Identify Thunderstorm Damage in Agricultural Regions”, Aug. 28, 2001, 14 pages. |
| Duane Grant and the Idaho Wheat Commission, “Using Remote Sensing to Manage Wheat Grain Protein”, Jan. 2, 2003, 13 pages. |
| Zhang et al., “Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional scale”, Apr. 10, 2015, 14 pages. |
| Booker, “Video: Canadian cage mill teams up with JD”, Dec. 19, 2019, 6 pages. |
| AgTalk Home, “Best Combine to Handle Weeds”, Posted Nov. 23, 2018, 9 pages. |
| “Volunteer corn can be costly for soybeans”, Jun. 2, 2016, 1 page. |
| Pflanz, et al., “Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier”, Published Sep. 24, 2018, 17 pages. |
| Hartzler, “Weed seed predation in agricultural fields”, 9 pages, 2009. |
| Sa et al., “Weedmap: A Large-Scale Sematnic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Netowrk for Precision Farming”, Sep. 6, 2018, 25 pages. |
| Nagelkirk, Michigan State University-Extension, “Wheat Harvest: Minimizing the Risk of Fusarium Head Scab Losses”, Jul. 11, 2013, 4 pages. |
| Saskatchewan, “Wheat: Winter Wheat”, (https://www.saskatchewan.ca/business/agriculture-natural-resources-and-industry/agribusiness-farmers-and-ranchers/crops-and-irrigation/field-crops/cereals-barley-wheat-oats-triticale/wheat-winter-wheat) 5 pages, last accessed Jul. 14, 2020. |
| Quora, “Why would I ever use sport mode in my automatic transmission car? Will this incrase fuel efficiency or isit simply a feature that makes form more fun when driving?”, Aug. 10, 2020, 5 pages. |
| Wade, “Using a Drone's Surface Model to Estimate Crop Yields & Assess Plant Health”, 19.10.2015, 14 pages. |
| Mathyam et al., “Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Stress”, Dec. 2011, 30 pages. |
| “Four Helpful Weed-Management Tips for Harvest Time”, 2 pages, Sep. 4, 2019. |
| Franz et al., “The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield”, University of Nebraska—Lincoln, Mar. 23, 2020, 44 pages. |
| Peiffer et al., The Genetic Architecture of Maize Stalk Strength:, Jun. 20, 2013, 14 pages. |
| Notice of Allowance for U.S. Appl. No. 16/432,557 dated Mar. 22, 2021, 9 pages. |
| Zhao, L., Yang, J., Li, P. and Zhang, L., 2014. Characteristics analysis and classification of crop harvest patterns by exploiting high-frequency multipolarization SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9), pp. 3773-3783. |
| Feng-jie, X., Er-da, W. and Feng-yuan, X., Crop area yield risk evaluation and premium rates calculation—Based on nonparametric kernel density estimation. In 2009 International Conference on Management Science and Engineering, 7 pages. |
| Liu, R. and Bai, X., May 2014. Random fuzzy production and distribution plan of agricultural products and its PSO algorithm. In 2014 IEEE International Conference on Progress in Informatics and Computing (pp. 32-36). IEEE. |
| Notice of Allowance for U.S. Appl. No. 16/171,978 dated Mar. 31, 2021, 6 pages. |
| Application and Drawings for U.S. Appl. No. 17/067,383, filed Oct. 9, 2020, 61 pages. |
| Martin et al., “Breakage Susceptibility and Harness of Corn Kernels of Various Sizes and Shapes”, May 1987, 10 pages. |
| Jones et al., “Brief history of agricultural systems modeling” Jun. 21, 2016, 15 pages. |
| Dan Anderson, “Brief history of agricultural systems modeling” 1 pages. Aug. 13, 2019. |
| A.Y. Şeflek, “Determining the Physico-Mechanical Characteristics of Maize Stalks Fordesigning Harvester”, The Journal of Animal & Plant Sciences, 27(3): 2017, p. 855-860 ISSN: 1018-7081, Jun. 1, 2017. |
| Carmody, Paul, “Windrowing and harvesting”, 8 pages Date: Feb. 3, 2010. |
| Dabney, et al., “Forage Harvest Representation in RUSLE2”, Published Nov. 15, 2013, 17 pages. |
| John Deere S-Series Combines S760, S770, S780, S790 Brochure, 44 pages, Nov. 15, 2017. |
| Sekhon et al., “Stalk Bending Strength is Strongly Assoicated with Maize Stalk Lodging Incidence Across Multiple Environments”, Jun. 20, 2019, 23 pages. |
| Thomison et al. “Abnormal Corn Ears”, Apr. 28, 2015, 1 page. |
| Anderson, “Adjust your Combine to Reduce Damage to High Moisture Corn”, Aug. 13, 2019, 11 pages. |
| Sumner et al., “Reducing Aflatoxin in Corn During Harvest and Storage”, Reviewed by John Worley, Apr. 2017, 6 pages. |
| Sick, “Better understanding corn hybrid characteristics and properties can impact your seed decisions”, 8 pages, Sep. 21, 2018. |
| TraCI/Change Vehicle State—SUMO Documentation, 10 pages, Retrieved Dec. 11, 2020. |
| Arnold, et al., Chapter 8. “Plant Growth Component”, Jul. 1995, 41 pages. |
| Humburg, Chapter: 37 “Combine Adjustments to Reduce Harvest Losses”, 2019, South Dakota Board of Regents, 8 pages. |
| Hoff, “Combine Adjustments”, Cornell Extension Bulletin 591, Mar. 1943, 10 pages. |
| University of Wisconsin, Corn Agronomy, Originally written Feb. 1, 2006 | Last updated Oct. 18, 2018, 2 pages. |
| University of Nebraska—Lincoln, “Combine Adjustments for Downed Corn—Crop Watch”, Oct. 27, 2017, 5 pages. |
| “Combine Cleaning: Quick Guide To Removing Resistant Weed Seeds (Among Other Things)”, Nov. 2006, 5 pages. |
| Dekalb, “Corn Drydown Rates”, 7 pages, Aug. 4, 2020. |
| Mahmoud et al. Iowa State University, “Corn Ear Orientation Effects on Mechanical Damage and Forces on Concave”, 1975, 6 pages. |
| Sindelar et al., Kansas State University, “Corn Growth & Development” Jul. 17, 2017, 9 pages. |
| Pannar, “Manage the Growth Stages of the Maize Plant With Pannar”, Nov. 14, 2016, 7 pages. |
| He et al., “Crop residue harvest impacts wind erodibility and simulated soil loss in the Central Great Plains”, Sep. 27, 2017, 14 pages. |
| Blanken, “Designing a Living Snow Fence for Snow Drift Control”, Jan. 17, 2018, 9 pages. |
| Jean, “Drones give aerial boost to ag producers”, Mar. 21, 2019, 4 pages. |
| Zhao et al., “Dynamics modeling for sugarcane sucrose estimation using time series satellite imagery”, Jul. 27, 2017, 11 pages. |
| Brady, “Effects of Cropland Conservation Practices on Fish and Wildlife Habitat”, Sep. 1, 2007, 15 pages. |
| Jasa, et al., “Equipment Adjustments for Harvesting Soybeans at 13%-15% Moisture”, Sep. 15, 2017, 2 pages. |
| Bendig et al., “Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging”, Oct. 21, 2014, 18 pages. |
| Robertson, et al., “Maize Stalk Lodging: Morphological Determinants of Stalk Strength”, Mar. 3, 2017, 10 pages. |
| MacGowan et al. Purdue University, Corn and Soybean Crop Deprediation by Wildlife, Jun. 2006, 14 pages. |
| Martinez-Feria et al., Iowa State University, “Corn Grain Dry Down in Field From Maturity to Harvest”, Sep. 20, 2017, 3 pages. |
| Wrona, “Precision Agriculture's Value” Cotton Physiology Today, vol. 9, No. 2, 1998, 8 pages. |
| Zhang et al., “Design of an Optical Weed Sensor Using Plant Spectral Characteristics” Sep. 2000, 12 pages. |
| Hunt, et al., “What Weeds Can Be Remotely Sensed?”, 5 pages, May 2016. |
| Pepper, “Does An Adaptive Gearbox Really Learn How You Drive?”, Oct. 30, 2019, 8 pages. |
| Eggerl, “Optimization of Combine Processes Using Expert Knowledge and Methods of Artificial Intelligence”, Oct. 7, 1982, 143 pages. |
| Sheely et al., “Image-Based, Variable Rate Plant Growth Regulator Application in Cotton at Sheely Farms in California”, Jan. 6-10, 2003 Beltwide Cotton Conferences, Nashville, TN, 17 pages. |
| Kovacs et al., “Physical characteristics and mechanical behaviour of maize stalks for machine development”, Apr. 23, 2019, 1-pages. |
| Anonymously, “Optimizing Crop Profit Across Multiple Grain Attributes and Stover”, ip.com, May 26, 2009, 17 pages. |
| Breen, “Plant Identification: Examining Leaves”, Oregon State University, 2020, 8 pages. |
| Caglayan et al., A Plant Recognition Approach Using Shape and Color Features in Leaf Images, Sep. 2013, 11 pages. |
| Casady et al., “Precision Agriculture Yield Monitors” University of Missouri-System, 4 pages, 1998. |
| Apan et al., “Predicting Grain Protein Content in Wheat Using Hyperspectral Sensing of In-season Crop Canopies and Partial Least Squares Regression” 18 pages, 2006. |
| Xu et al., “Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data”, Apr. 24, 2020, 21 pages. |
| Day, “Probability Distributions of Field Crop Yields,” American Journal of Agricultural Economics, vol. 47, Issue 3, Aug. 1965, Abstract Only, 1 page. |
| Butzen, “Reducing Harvest Losses in Soybeans”, Pioneer, Jul. 23, 2020, 3 pages. |
| Martin et al., “Relationship between secondary variables and soybean oil and protein concentration”, Abstract Only, 1 page., 2007. |
| Torres, “Precision Planting of Maize” Dec. 2012, 123 pages. |
| Martin et al. Breakage Susceptibility and Hardness of Corn Kernels of Various Sizes and Shapes, vol. 3(); May 1087, 10 pages. https://pdfs.semanticscholar.org/e579/1b5363b6a78efd44adfb97755a0cdd14f7ca.pdf. |
| Hoff, “Combine Adjustments” (https://smallfarmersjournal.com/combine-adjustments/), Mar. 1943, 9 pages. |
| Optimizing Crop Profit Across Multiple Grain Attributes and Stover, Electronic Publication Date May 26, 2009, 17 pages. |
| Unglesbee, Soybean Pod Shatter—Bad Enough to Scout Before Harvest—DTN, Oct. 17, 2018. 11 pages. Susceptibility to shatter (https://agfax.com/2018/10/17/soybean-pod-shatter-bad-enough-to-scout-before-harvest-dtn/). |
| GIS Maps for Agricultural, accessed on May 10, 2022, 7 pages. https://www.satimagingcorp.com/services/geographic-information-systems/gis-maps-agriculture-mapping. |
| https://wingtra.com/drone-mapping-applications/use-of-drones-in-agriculture, accessed on May 10, 2022, 19 pages. |
| Energy Requirement Model for a Combine Harvester; Part 1: Development of Component Models, Published online Dec. 22, 2004, 17 pages. |
| Energy Requirement Model for a Combine Harvester, Part 2: Integration of Component Models, Published online Jan. 18, 2005, 11 pages. |
| Pioneer on reducing soybean harvest losses including combine adjustments (last accessed Jul. 23, 2020) (https://www.pioneer.com/us/agronomy/reducing_harvest_losses_in_soybeans.html), 5 pages. |
| Prosecution History for U.S. Appl. No. 16/380,691 including: Notice of Allowance dated Mar. 10, 2021 and Application and Drawings filed Apr. 10, 2019, 46 pages. |
| U.S. Appl. No. 16/831,216 Application and Drawings filed Mar. 26, 2020, 56 pages. |
| Notice of Allowance for U.S. Appl. No. 16/380,531 dated Apr. 5, 2021, 5 pages. |
| 7 Combine Tweaks to Boost Speed (https://www.agriculture.com/machinery/harvest-equipment/7-combine-tweaks-to-boost-speed_203-ar33059) 8 pages, Aug. 19, 2018. |
| Managing corn harvest this fall with variable corn conditions (https://www.ocj.com/2019/10/managing-corn-harvest-this-fall-with-variable-corn-conditions/), 4 pages, Oct. 10, 2019. |
| Reducing Aflatoxin in Corn During Harvest and Storage (https://extension.uga.edu/publications/detail.html?number=B1231&title=Reducing%20Aflatoxin%20in%20Corn%20During%20Harvest%20and%20Storage), 9 pages, Published with Full Review on Apr. 19, 2017. |
| Variable Rate Applications to Optimize Inputs (https://www.cotton.org/tech/physiology/cpt/miscpubs/upload/CPT-v9No2-98-REPOP.pdf), 8 pages, Nov. 2, 1998. |
| Robin Booker, Video: Canadian cage mill teams up with JD (https://www.producer.com/2019/12/video-canadian-cage-mill-teams-up-with-jd/) , 6 pages, Dec. 19, 2019. |
| Jarnevich, et al. “Forecasting Weed Distributions using Climate Data: A GIS Early Warning Tool”, Invasive Plant Science and Management, 11 pages, Jan. 20, 2017. |
| Burks, “Classification of Weed Species Using Color Texture Features and Discriminant Analysis” (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.468.5833&rep=rep1&type=pdf), 8 pages, 2000. |
| John Deere, https://www.youtube.com/watch?v=1Gq77CfdGI4&list=PL1KGsSJ4CWk4rShNb3-sTMOliL8meHBL5 (last accessed Jul. 14, 2020), Jun. 15, 2020, 5 pages. |
| Combine Adjustments (http://corn.agronomy.wisc.edu/Management/L036.aspx), 2 pages, Originally written Feb. 1, 2006; last updated Oct. 18, 2018. |
| Ardekani, “Off- and on-ground GPR techniques for field-scale soil moisture mapping” Jun. 2013, 13 pages. |
| Does an Adaptive Gearbox Really Learn How You Drive? (https://practicalmotoring.com.au/voices/does-an-adaptive-gearbox-really-learn-how-you-drive/), Oct. 30, 2019, 8 pages. |
| https://www.researchgate.net/publication/222527694_Energy_Requirement_Model_for_a_Combine_Harvester_Part_I_Development_of_Component_Models, Abstract Only, Jan. 2005. |
| http://canola.okstate.edu/cropproduction/harvesting, 8 pages, Aug. 2011. |
| “Tips and Tricks of Harvesting High Moisture Grain”, https://www.koenigequipment.com/blog/tips-and-tricks-of-harvesting-highmoisture-grain, 5 pages, last accessed Feb. 11, 2021. |
| Hoff, Combine Adjustements, Mar. 1943, 8 pages. |
| Haung et al., “Accurate Weed Mapping and Prescription Map Generation Based onFully Convolutional Networks Using UAV Imagery”, 14 pages, Oct. 1, 2018. |
| Thompson, “Morning glory can make it impossible to harvest corn”, Feb. 19, 2015, 4 pages. |
| Lamsal et al. “Sugarcane Harvest Logistics in Brazil” Iowa Research Online, Sep. 11, 2013, 27 pages. |
| Jensen, “Algorithms for Operational Planning of Agricultural Field Operations”, Mechanical Engineering Technical Report ME-TR-3, Nov. 9, 2012, 23 pages. |
| Chauhan, “Remote Sensing of Crop Lodging”, Nov. 16, 2020, 16 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20220110246 A1 | Apr 2022 | US |
