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 .
Status of Claims
Claims 1-2, 21-23, 41-44, 46-48, 51-53, and 55-58 are pending in this application.
Claims 3-20, 24-40, 45, 49-50, 54, and 59-60 are cancelled.
Claims 1-2, 21-23, 41-44, 46-48, 51-53, and 55-56 are amended.
Claims 1-2, 21-23, 41-44, 46-48, 51-53, and 55-58 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 18 September 2025 is being considered by the examiner.
Response to Amendments
Applicant’s amendments, filed 22 December 2025, with respect to the objection of claim 51 has been fully considered, and the objection has been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 21-23, 41-44, 46, 51-53, and 55-58 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US Patent 11,263,707 B2) in view of Guan et al (US Publication 2020/0334518 A1).
Regarding claim 1, Perry teaches a method to determine a time to perform an agricultural operation, the method comprising: obtaining agricultural operation data for a first field and a second field, the agricultural operation data representing farmer decisions to perform agricultural operations (Perry: Col. 1 Lines 14-16, Col. 10 Lines 16-39; application of machine learning operations to data from disparate sources; databases including inputs from growers or grower client devices describing crops planted, actions taken during past years or seasons, planting dates, planting depths); filtering the agricultural operation data to remove data associated with crop growing cycles outside an expected crop growing cycle duration based on a respective type of a crop of the first field and the second field (Perry: Col. 2 Lines 25-31; removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges), the filtering to create filtered agricultural operation data (Perry: Col. 9 Lines 51-58; an agronomist can modify field information on which the crop prediction system is applied such as a field location, a crop type, an expected rainfall); accessing geospatial data, the geospatial data including at least climate data (Perry: Col. 9 Line 62 - Col. 10 Line 1, Col. 10 Lines 40-42; weather databases describing historic weather patterns, including rainfall, temperature, sunlight, humidity, flooding) and satellite imaging data (Perry: Col. 5 Lines 37-40; accessed field information can be collected from one or more of: sensors located at the first portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems); ……… ; and training a machine learning model with the training data set to generate a first time to perform an agricultural operation in the first field based on current agricultural and geospatial conditions and the previous farmer decisions (Perry: Col. 2 Lines 16-24, Col. 3 Line 63- Col. 4 Line 3; prediction model is trained on crop growth information and maps, for sets of land characteristics, one or more farming operations to crop productivities by performing one or more machine learning operations; farming operations identifies one or more of: a harvest date), the first time to perform the agricultural operation in the first field different than a second time to perform the agricultural operation in the second field (Perry: Col. 22 Lines 53-60; a type of treatment to apply; a quantity of treatment; a location to apply treatment; a date to apply treatment).
Perry doesn’t explicitly teach combining the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field.
However Guan, in the same field of endeavor, teaches combining the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field (Guan: Para. 174, 175, 180, 191, 193; crop and environmental cross section embedding may be combined with the learned management practice embedding; combined with the additional data embedding; additional data embedding encodes information relevant to total effects on crop yield based on all additional data; second past yield map may identify a total profit for each location; seed cost, fertilization costs, estimated labor costs, watering costs).
It would have been obvious to one having ordinary skill in the art to modify the optimized agricultural production through machine learning (Perry: Col. 1 Lines 14-16) with training data to estimate profit (Guan: Para. 174-175) with a reasonable expectation of success because separating out the timing information from the application amount information allows the deep neural network to identify effects of applying water at different times as well as effects of applying water at different rates which would reduce watering costs (Guan: Para. 163, 193).
Regarding claim 2, Perry teaches the method of claim 1, further including: accessing the current agricultural and geospatial conditions corresponding to the first field (Perry: Col. 7 Lines 48-65; past and present geographic information, past and present agricultural information, past and present agronomic information); and executing the machine learning model to generate the first time to perform the agricultural operation at the first field (Perry: Col. 7 Lines 45-58; machine learning operations for predictions of crop production; set of farming operations expected to result in the measure of expected crop production when performed in a specified manner, at a specified time/location).
Regarding claim 21, Perry teaches an apparatus to determine a time to perform an agricultural operation, the apparatus comprising: display circuitry (Perry: Col. 2 Lines 19-24; user interface displayed by a client device of the user to display a crop growth program); machine-readable instructions (Perry: Col. 5 Lines 28-30; crop growth program can include a set of instructions); and programmable circuitry to at least one of instantiate or execute the machine-readable instructions to: obtain agricultural operation data for a first field and a second field, the agricultural operation data representing farmer decisions to perform agricultural operations (Perry: Col. 1 Lines 14-16, Col. 10 Lines 16-39; application of machine learning operations to data from disparate sources; databases including inputs from growers or grower client devices describing crops planted, actions taken during past years or seasons, planting dates, planting depths); filter the agricultural operation data to remove data associated with crop growing cycles outside an expected crop growing cycle duration based on a respective type of a crop of the first field and the second field (Perry: Col. 2 Lines 25-31; removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges), the filtering to create filtered agricultural operation data (Perry: Col. 9 Lines 51-58; an agronomist can modify field information on which the crop prediction system is applied such as a field location, a crop type, an expected rainfall); access geospatial data, the geospatial data including at least climate data (Perry: Col. 9 Line 62 - Col. 10 Line 1, Col. 10 Lines 40-42; weather databases describing historic weather patterns, including rainfall, temperature, sunlight, humidity, flooding) and satellite imaging data (Perry: Col. 5 Lines 37-40; accessed field information can be collected from one or more of: sensors located at the first portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems); ………. ; and train a machine learning model with the training data set to generate a first time to perform an agricultural operation in the first field based on existing agricultural and geospatial conditions and the previous farmer decisions based on the previous agricultural and geospatial conditions (Perry: Col. 2 Lines 16-24, Col. 3 Line 63- Col. 4 Line 3; prediction model is trained on crop growth information and maps, for sets of land characteristics, one or more farming operations to crop productivities by performing one or more machine learning operations; farming operations identifies one or more of: a harvest date), the first time to perform the agricultural operation in the first field different than a second time to perform the agricultural operation in the second field (Perry: Col. 22 Lines 53-60; a type of treatment to apply; a quantity of treatment; a location to apply treatment; a date to apply treatment).
Perry doesn’t explicitly teach combine the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field.
However Guan, in the same field of endeavor, teaches combine the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field (Guan: Para. 174, 175, 180, 191, 193; crop and environmental cross section embedding may be combined with the learned management practice embedding; combined with the additional data embedding; additional data embedding encodes information relevant to total effects on crop yield based on all additional data; second past yield map may identify a total profit for each location; seed cost, fertilization costs, estimated labor costs, watering costs).
It would have been obvious to one having ordinary skill in the art to modify the optimized agricultural production through machine learning (Perry: Col. 1 Lines 14-16) with training data to estimate profit (Guan: Para. 174-175) with a reasonable expectation of success because separating out the timing information from the application amount information allows the deep neural network to identify effects of applying water at different times as well as effects of applying water at different rates which would reduce watering costs (Guan: Para. 163, 193).
Regarding claim 22, Perry teaches the apparatus of claim 21, wherein the programmable circuitry is to: access the current agricultural and geospatial conditions corresponding to the first field (Perry: Col. 7 Lines 48-65; past and present geographic information, past and present agricultural information, past and present agronomic information); and executing the machine learning model to generate the first time to perform the agricultural operation at the first field (Perry: Col. 7 Lines 45-58; machine learning operations for predictions of crop production; set of farming operations expected to result in the measure of expected crop production when performed in a specified manner, at a specified time/location).
Regarding claim 23, Perry teaches the apparatus of claim 22, wherein the programmable circuitry is to direct an automated agricultural vehicle to perform the agricultural operation at the first field (Perry: Col. 5 Lines 30-36, 62-67; a set of instructions associated with planting, growing, and harvesting a different crop type or crop variety within each of the plurality of sub-portions; selected set of farming operations can be provided directly to a recipient smart equipment; recipient smart equipment includes a harvesting system).
Regarding claim 41, Perry teaches a non-transitory computer-readable medium comprising instructions which, when executed, cause processor circuitry to: (Perry: Col. 34 Lines 21-22; memory holds instructions and data used by the processor) obtain agricultural operation data for a first field and a second field, the agricultural operation data representing farmer decisions to perform agricultural operations (Perry: Col. 1 Lines 14-16, Col. 10 Lines 16-39; application of machine learning operations to data from disparate sources; databases including inputs from growers or grower client devices describing crops planted, actions taken during past years or seasons, planting dates, planting depths); filter the agricultural operation data to remove data associated with crop growing cycles outside an expected crop growing cycle duration based on a respective type of a crop of the first field and the second field (Perry: Col. 2 Lines 25-31; removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges), the filtering to create filtered agricultural operation data (Perry: Col. 9 Lines 51-58; an agronomist can modify field information on which the crop prediction system is applied such as a field location, a crop type, an expected rainfall); access geospatial data, the geospatial data including at least climate data (Perry: Col. 9 Line 62 - Col. 10 Line 1, Col. 10 Lines 40-42; weather databases describing historic weather patterns, including rainfall, temperature, sunlight, humidity, flooding) and satellite imaging data (Perry: Col. 5 Lines 37-40; accessed field information can be collected from one or more of: sensors located at the first portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems); …….. ; and train a machine learning model with the training data set to generate a first time to perform an agricultural operation in the first field based on current agricultural and geospatial conditions and the previous farmer decisions based on the previous agricultural and geospatial conditions (Perry: Col. 2 Lines 16-24, Col. 3 Line 63- Col. 4 Line 3; prediction model is trained on crop growth information and maps, for sets of land characteristics, one or more farming operations to crop productivities by performing one or more machine learning operations; farming operations identifies one or more of: a harvest date), the first time to perform the agricultural operation in the first field different than a second time to perform the agricultural operation in the second field (Perry: Col. 22 Lines 53-60; a type of treatment to apply; a quantity of treatment; a location to apply treatment; a date to apply treatment).
Perry doesn’t explicitly teach combine the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field.
However Guan, in the same field of endeavor, teaches combine the filtered agricultural operation data with the geospatial data for the first field and the second field to create a training data set representing previous farmer decisions based on (1) previous agricultural conditions, (2) geospatial conditions, and (3) conditions not related to the first field or the second field (Guan: Para. 174, 175, 180, 191, 193; crop and environmental cross section embedding may be combined with the learned management practice embedding; combined with the additional data embedding; additional data embedding encodes information relevant to total effects on crop yield based on all additional data; second past yield map may identify a total profit for each location; seed cost, fertilization costs, estimated labor costs, watering costs).
It would have been obvious to one having ordinary skill in the art to modify the optimized agricultural production through machine learning (Perry: Col. 1 Lines 14-16) with training data to estimate profit (Guan: Para. 174-175) with a reasonable expectation of success because separating out the timing information from the application amount information allows the deep neural network to identify effects of applying water at different times as well as effects of applying water at different rates which would reduce watering costs (Guan: Para. 163, 193).
Regarding claim 42, Perry teaches the non-transitory computer-readable medium of claim 41, wherein the processor circuitry is to: access the current agricultural and geospatial conditions corresponding to the first field (Perry: Col. 7 Lines 48-65; past and present geographic information, past and present agricultural information, past and present agronomic information); and executing the machine learning model to generate the first time to perform the agricultural operation at the first field (Perry: Col. 7 Lines 45-58; machine learning operations for predictions of crop production; set of farming operations expected to result in the measure of expected crop production when performed in a specified manner, at a specified time/location).
Regarding claim 43, Perry teaches the non-transitory computer-readable medium of claim 42, wherein the processor circuitry is to direct an automated agricultural vehicle to perform the agricultural operation at the first field (Perry: Col. 5 Lines 30-36, 62-67; a set of instructions associated with planting, growing, and harvesting a different crop type or crop variety within each of the plurality of sub-portions; selected set of farming operations can be provided directly to a recipient smart equipment; recipient smart equipment includes a harvesting system).
Regarding claim 44, Perry teaches the non-transitory computer-readable medium of claim 42, wherein the processor circuitry is to provide to a third party a name of the agricultural operation, the first time to perform the agricultural operation, and a location of the first field (Perry: Col. 23 Lines 39-53, Col. 33 Lines 38-42; crop prediction system then provides the modified set of farming operations to the requesting entity such that the modified set of farming operations may be performed by the requesting entity or another entity; “optimizing crop production” can refer to optimizing the use of a technology or service provided by such a third party).
Regarding claim 46, Perry teaches the non-transitory computer-readable medium of claim 42, wherein the first field includes at least one of a plurality of plots of land or a plurality of subplots of land (Perry: Col. 5 Lines 30-35; identifies a plurality of sub-portions of the first portion of land).
Regarding claim 51, Perry teaches the non-transitory computer-readable medium of claim 51, wherein a result of the agricultural operation includes sensor data collected by one or more sensors on an agricultural vehicle, the sensor data including a measured quality of the agricultural operation at the first time and the first field (Perry: Col. 8 Lines 52-60, Col. 11 Lines 18-24; one or more quality metric is collected, measured or observed during harvest, for example dry matter content of corn may be measured using near-infrared spectroscopy on a combine; provide the collected temperature and sunlight information in association with information identifying the field).
Regarding claim 52, Perry teaches the non-transitory computer-readable medium of claim 51, wherein the processor circuitry is to filter the agricultural operation data by filtering the agricultural operation data associated with the sensor data outside expected sensor ranges or expected sensor variation over time (Perry: Col. 17 Lines 27-31; the normalization module can “clean” various types of data, for instance by upscaling/downscaling image data, by removing outliers from quantitative or measurement data).
Regarding claim 53, Perry teaches the non-transitory computer-readable medium of claim 51, wherein the processor circuitry is to enhance the agricultural operation data by synthesizing the result of the agricultural operation by combining the sensor data from the one or more sensors of the agricultural vehicle (Perry: Col. 11 Lines 18-24; smart tractor can collect information such as temperature and sunlight information while operating within a field, and can communicatively couple with the crop prediction system to provide the collected temperature and sunlight information in association with information identifying the field).
Regarding claim 55, Perry teaches the non-transitory computer-readable medium of claim 51, wherein the processor circuitry is to filter the agricultural operation data by removing the agricultural operation data associated with a geographic area outside the first field (Perry: Col. 5 Lines 52-59; the system applies the prediction model by identifying a cluster of the sub-portions with a threshold similarity, applying the prediction model to accessed field data with the cluster of the sub-portions of land).
Regarding claim 56, Perry teaches the non-transitory computer-readable medium of claim 51, wherein the processor circuitry is to filter the agricultural operation data by removing the agricultural operation data associated with a time outside of expected crop growing seasons (Perry: Col. 11 Lines 28-36, Col. 24 Lines 6-11; sensor data from sensor data sources may be taken at one or more times during a growing season or across multiple growing seasons; applying a crop prediction model at various points throughout a growing season).
Regarding claim 57, Perry teaches the non-transitory computer-readable medium of claim 51, wherein the geospatial data additionally includes at least one of soil data or agronomic data (Perry: Col. 3 Lines 16-20; accessing field information collected from the sensors includes one or more of: soil temperature, air temperature, soil moisture).
Regarding claim 58, Perry teaches the non-transitory computer-readable medium of claim 57, wherein the agronomic data includes at least one of machine health data, farming regulation data, silage capacity data, crop market price data, crop input cost data, available labor data, available equipment data, crop insurance data, or operation cost data (Perry: Col. 26 Lines 47-52; ensemble model can also select an optimized wheat protein content, for instance based on current market prices, costs of requisite treatments).
Claims 47-48 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US Patent 11,263,707 B2) in view of Guan et al (US Publication 2020/0334518 A1) and in further view of Katsumata et al. (Foreign Reference JP2019153109A).
Regarding claim 47, Perry and Guan don’t explicitly teach wherein the processor circuitry is to display a plurality of expected times to perform the agricultural operation by overlaying colored sections on an image of the plurality of plots of land, the colored sections including colors to represent the plurality of expected times.
However Katsumata, in the same field of endeavor, teaches wherein the processor circuitry is to display a plurality of expected times to perform the agricultural operation by overlaying colored sections on an image of the plurality of plots of land, the colored sections including colors to represent the plurality of expected times (Katsumata: Para. 51, 95; display the harvest period rate by color coding for each area; a screen for predicting the appropriate harvest time as of X month X day of Farm A).
It would have been obvious to one having ordinary skill in the art to modify the optimized agricultural production through machine learning (Perry: Col. 1 Lines 14-16) with training data to estimate profit (Guan: Para. 174-175) and colored map of harvest time (Katsumata: Para. 51, 95) with a reasonable expectation of success because dividing the area of the farmland and displaying the harvest period rate by color coding for each area helps a famer identify which areas have the same harvest time to better allocated time and resources (Katsumata: Para. 51, 91).
Regarding claim 48, Perry and Guan don’t explicitly teach wherein the processor circuitry is to display the plurality of expected times includes at least one of a histogram of days until the agricultural operation, a priority ranking of the plurality of plots of land, the colored sections overlayed on the image of the plurality of plots of land, or a chart of the plurality of expected times.
However Katsumata, in the same field of endeavor, teaches wherein the processor circuitry is to display the plurality of expected times includes at least one of a histogram of days until the agricultural operation, a priority ranking of the plurality of plots of land, the colored sections overlayed on the image of the plurality of plots of land, or a chart of the plurality of expected times (Katsumata: Para. 51, 95; display the harvest period rate by color coding for each area; a screen for predicting the appropriate harvest time as of X month X day of Farm A).
It would have been obvious to one having ordinary skill in the art to modify the optimized agricultural production through machine learning (Perry: Col. 1 Lines 14-16) with training data to estimate profit (Guan: Para. 174-175) and colored map of harvest time (Katsumata: Para. 51, 95) with a reasonable expectation of success because dividing the area of the farmland and displaying the harvest period rate by color coding for each area helps a famer identify which areas have the same harvest time to better allocated time and resources (Katsumata: Para. 51, 91).
Response to Arguments
Applicant’s arguments with respect to claims 1-2, 21-23, 41-44, 46-48, and 51-58 have been considered but are moot because the arguments do not apply to the references being used in the current rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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