DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending and examined below. This action is in response to the claims filed 7/9/24.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being clearly anticipated by VANDIKE et al. (US 2022/0113734), herein “Vandike”.
Regarding claims 1 and 9, Vandike discloses a crop state map generation and control system including an agricultural system/computer implemented method of controlling a harvester comprising (Abstract):
one or more processors; and memory storing instruction, executable by the one or more processors, that, when executed by the one or more processors, cause the one or more processors to (¶46 and ¶209):
obtain harvest readiness sensor data, indicative of one or more harvest readiness attributes corresponding to a worksite, from one or more harvest readiness sensors remote from a harvester (¶69 – in-situ sensor data corresponding to the recited harvest readiness sensor data for determining harvest readiness attributes of specific crops corresponding to the recited a worksite where sensors include remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data as well as other sensors for gathering the appropriate data);
determine one or more harvest readiness values corresponding to the worksite, indicative of a readiness for harvesting, based on the harvest readiness sensor data (¶46 and ¶69 – agricultural characteristics corresponding to the recited one or more harvest readiness values based on the sensor data); and
control one or more controllable subsystems of the harvester based on the one or more harvest readiness values (¶69-76 and Fig. 3A – element 310 corresponding to the recited controlling subsystems based on controls derived from the agricultural characteristics corresponding to the recited harvest readiness values).
Regarding claims 2 and 10, Vandike further discloses wherein obtaining the harvest readiness sensor data comprises obtaining the harvest readiness sensor data from one or more harvest readiness sensors remote from the worksite (¶54 – sensor data may come from locations across the field or from different fields corresponding to the recited remote from the worksite for providing contextual information).
Regarding claims 3 and 11, Vandike further discloses wherein obtaining the harvest readiness sensor data comprises obtaining the harvest readiness sensor data from one or more harvest readiness sensor disposed on a drone (¶69 - remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data).
Regarding claims 4 and 13, Vandike further discloses wherein obtaining the harvest readiness sensor data comprises obtaining the harvest readiness sensor data indicative of one or more worksite readiness attributes (¶46 - agricultural characteristics includes characteristics of the field corresponding to the recited worksite readiness attributes).
Regarding claims 5 and 12, Vandike further discloses wherein obtaining the harvest readiness sensor data comprises obtaining the harvest readiness sensor data indicative of one or more crop plant readiness attributes (¶46-47 – agricultural characteristics corresponding to the recited harvest readiness sensor data includes crop characteristics such as a vegetative index, seeing characteristics, etc. corresponding to the recited one or more crop plant readiness attributes).
Regarding claims 6 and 14, Vandike further discloses controlling a propulsion subsystem of the harvester to control a travel speed of the harvester (¶46 and ¶133 - the settings controller 232 controls propulsion subsystem 250 (shown as one of the controllable subsystems 216 in FIG. 2) to control the speed of agricultural harvester).
Regarding claims 7 and 15, Vandike further discloses controlling a steering subsystem of the harvester to control a heading of the harvester (¶46 and ¶64 - control steering subsystem 252 to steer agricultural harvester 100 according to a desired path).
Regarding claims 8 and 16, Vandike further discloses the one or more controllable subsystems include one or more of (¶46 and ¶64 – the “one or more” claim element only requires one of the following to be present to disclose the claim as written):
a first actuator controllable to move a first component of the harvester (¶46 and ¶64 -header actuator corresponding to the recited first actuator to move a first component of the harvester); or
a second actuator controllable to adjust a movement speed of a second component of the harvester (¶32, ¶46, and ¶64 – propulsion subsystem that includes an engine that drives ground engaging components to control the ground speed of the harvester corresponding to the recited movement speed of a second component of the harvester); and
wherein the instructions, when executed by the one or more processors, cause the one or more processors to: control one or more of: the first actuator to move the first component of the harvester (¶46 and ¶64 -header actuator corresponding to the recited first actuator to move a first component of the harvester as controlled by the settings controller); or
the second actuator to adjust a movement speed of the second component of the harvester (¶32, ¶46, and ¶64 – propulsion subsystem that includes an engine that drives ground engaging components to control the ground speed of the harvester corresponding to the recited movement speed of a second component of the harvester).
Regarding claim 17, Vandike further discloses an agricultural system comprising: one or more processors; and memory storing instruction, executable by the one or more processors, that, when executed by the one or more processors, cause the one or more processors to (Abstract, ¶46, and ¶209):
obtain crop plant readiness sensor data, indicative of one or more crop plant readiness attributes corresponding to a worksite, from one or more sensors remote from a harvester (¶69 – in-situ sensor data including crop characteristics such as a vegetative index, seeing characteristics, etc. corresponding to the recited one or more crop plant readiness attributes for determining harvest readiness attributes of specific crops corresponding to the recited a worksite where sensors include remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data as well as other sensors for gathering the appropriate data);
determine one or more crop plant readiness values corresponding to the worksite, indicative of a readiness of crop plants for harvesting, based on the crop plant readiness sensor data (¶46-47 – crop characteristics such as a vegetative index, seeing characteristics, etc. corresponding to the recited one or more crop plant readiness attributes); and
control one or more controllable subsystems of the harvester based on the one or more crop plant readiness values (¶46, ¶69-76, and Fig. 3A – element 310 corresponding to the recited controlling subsystems based on controls derived from the crop characteristics such as a vegetative index, seeing characteristics, etc. corresponding to the recited one or more crop plant readiness attributes).
Regarding claim 18, Vandike further discloses obtain worksite readiness sensor data, indicative of one or more worksite readiness attributes corresponding to the worksite, from the one or more sensors remote from the harvester; determine one or more worksite readiness values corresponding to the worksite, indicative of a readiness of the worksite for harvesting, based on the worksite readiness sensor data (¶46 and ¶69 – in-situ sensor data including agricultural characteristics such as characteristics of the field corresponding to the recited worksite readiness attributes where sensors include remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data as well as other sensors for gathering the appropriate data); and
control one or more controllable subsystems of the harvester based further on the worksite readiness values (¶46, ¶69-76, and Fig. 3A – element 310 corresponding to the recited controlling subsystems based on controls derived from the agricultural characteristics includes characteristics of the field corresponding to the recited worksite readiness attributes).
Regarding claim 19, Vandike further discloses wherein the one or more sensors include at least one sensor disposed on a drone (¶69 - remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data).
Regarding claim 20, Vandike further discloses wherein the one or more sensors include at least one sensor remote from the worksite (¶54 – sensor data may come from locations across the field or from different fields corresponding to the recited remote from the worksite for providing contextual information).
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Faivre et al. (US 2006/0196158) discloses a method of predicting suitability for a crop harvesting operation including predicting crop maturity and moisture, and soil moisture and temperature, as well as other variables, based on predicted weather conditions, measured soil conditions, and crop season parameters (¶13).
Meyer (US 2012/0029732) discloses a drone based harvest monitoring system including a sensor for monitoring a plant population in front of a harvester and a transfer process of the crop from the harvester to a transport vehicle is arranged on an unmanned aircraft. The aircraft moves in the vicinity of the harvester in the harvesting mode and communicates in a wireless fashion with a control unit that controls an actuator for influencing an operating parameter of the harvester and/or the transport vehicle (in real time based on signals of the sensor in the harvesting mode. (Abstract)
Anderson et al. (US 2019/0113936) discloses a UAV assisted worksite operation system including a worksite operation can include collecting agricultural field crop data, field data, forestry data, golf course data, and turf data. In one example, agricultural field crop data can include emerged plant populations, plant maturity data, plant health data, and plant yield data. Field data can include a soil surface roughness, a residue cover, a soil type, a soil organic matter, and soil moisture, among other things. Forestry data can include such things as a canopy height, under-canopy vegetation, and under-canopy topography, for example. Additionally, golf course and turf data can include a turf height, turf health, a sand trap condition, and a water feature condition, among other things. (¶88)
Sood et al. (US 2020/0134485) discloses a machine learning based agricultural operation planning system including a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan. (Abstract)
Jarugumilli et al. (US 2022/0138868) discloses a system for enhancing harvest yield including retrieving data specific to a harvest project and, for each of multiple stages for a site of the harvest project, determining, via a decision service, multiple potential allocations of the multiple pickers to fields of the site based on the retrieved data and one or more applicable constraints, advancing one or more of the potential allocations based on a determined parameter, and imposing at least one constraint consistent with ones of the one or more advanced potential allocations. The method then includes determining, via the decision service, at least one allocation of the multiple pickers to the multiple fields based on the retrieved data and one or more applicable constraints, compiling and storing a harvest plan for the harvest project, and implementing the harvest plan (Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J Reda whose telephone number is (408)918-7573. The examiner can normally be reached on Monday - Friday 7-4 ET.
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/MATTHEW J. REDA/Primary Examiner, Art Unit 3665