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 .
DETAILED ACTION
Priority
Acknowledgment is made of applicant's claim for domestic benefit based on a provisional application 63/044,016 filed on June 25, 2020.
DETAILED ACTION
Claims 16, 18, 19 – 23, 25 - 30 and 36 - 42 are pending in the application.
Claims 16, 40, and 41 are independent.
Claims 1 – 15, 17, 24, and 31 – 35 are cancelled.
Claims 16 – 35 were subject to a restriction requirement; and given the election by the applicant, claims 31 – 35 are cancelled.
Claims 37 – 42 are new.
This action is Final based on new 35 U.S.C. §103 prior art references that
were necessitated by the applicant' s amendment; see MPEP §706.07(a).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 38 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in the parent disclosure in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor at the time the application was filed, had possession of the claimed invention. The element of “further including inputting an actual action for the respective zone into the predictive turf model for the respective zone, and wherein the actual action is different than the recommended action for the respective zone.” is not disclosed in the original application and therefore is rejected under 35 USC §112(a) as a new matter rejection. Note that specification paragraph 0049 states: “The recommended action 220 may include a variety of different actions to manage or treat the turf in the associated zone 104 . For example, the turf may be managed or treated using irrigation, aeration, fumigation, fertilization, cultivation, mowing, chemical application, blowing, rolling, etc. In one or more embodiments, the actual action carried out may be recorded and/or transmitted to the network to, e.g., be used in updating the suggestion model 145.” Although similar in form to claim 38, paragraph 0049 does not disclose that the actual action is different than the recommended action. See MPEP §201.07. Appropriate action is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 38 is rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. The claims contain the limitation: “…further including inputting an actual action for the respective zone into the predictive turf model for the respective zone, and wherein the actual action is different than the recommended action for the respective zone.” This element is not specifically disclosed in the originally filed specification and disclosure of the original or provisional application and therefore is new matter. See 35 USC 112(a) rejection above. See MPEP §201.07. Appropriate action is required.
Claims 16, 18, 19 – 23, 25 - 30 and 36 - 42 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 16, 40 and 41 recite the limitation (or similar limitation): "monitoring multiple sets of zone sensor data, each set associated with a different zone of a plurality of zones dividing the work region, each zone being sized and shaped to capture defined to capture homogeneous environmental conditions therein…" This element is confusing because the element of how the zones are defined, based on the wording, may not be determined by sensor data. Zone sensors sense data within a zone, and neither the claim or the specification teach sensors that adjust their position. If the zones are predefined as having homogenous environmental conditions, the meaning of having sensors capturing or sensing environmental data within that already predefined zone is confusing. The zones have sets of sensors data and the claim language does not teach redefining zone boundaries if the sensors do not detect homogenous environmental conditions, even though the zones are defined by homogenous environmental conditions. The independent claims require clarifying language of the claim elements concerning the zones (predefined) and sensor environmental data within the zones. The dependent claims depend from independent claims 16, 40 and 41 and therefore are also rejected under 35 USC 112(b).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16, 18, 23, 26 – 28, 36, 37, 39, and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Perry et al. (US PG Pub. No. 20190050948), herein “Perry” in view of Bentwich (US PG Pub. No. 20160157446), herein “Bentwich.”
Regarding claim 16,
Perry teaches a method of managing turf in a work region, the method comprising: (Par. 0003: “A grower is a farmer or other agricultural producer that plants, grows, and harvests crops, such as grain (e.g., wheat), fibers (e.g., cotton), vegetables, fruits, and so forth. Various geographic, weather-related, agronomic, and environmental factors may affect crop production. Some of these factors are within the control of the grower, whereas others are not. For example, the grower can change planting strategies or affect soil composition…” Par. 0004, last 1/3 of the paragraph: “The system applies the crop prediction engine to the accessed field information to identify a second set of farming operations that can produce a second expected crop productivity and modifies the first set of farming operations based on the second set of farming operations to produce a modified set of farming operations. Responsive to the second expected productivity being greater than the first expected productivity, the system presents, within an interface of a device associated with the grower, the modified set of farming operations. The grower performs the modified set of farming operations for the first type of crop on the first portion of land.”)
monitoring multiple sets of zone sensor data, each set associated with a different zone of a plurality of zones dividing the work region, each zone being sized and shaped to capture defined to capture homogeneous environmental conditions therein in a same zone; (Par. 0017: “The accessed field information can be collected from one or more of: sensors located at the first portion of land…” Claim 6: “…wherein the growth information is collected from a plurality of fields in a plurality of locations associated with one of: a threshold geographic and/or environmental diversity, and a threshold geographic and/or environmental similarity.” Par. 0164: “FIG. 5 illustrates an example land plot 500 partitioned into one or more planting regions 515. The land plot 500 includes features such as a house 505 and a river 510 that may impact the crop production of neighboring planting regions. As shown in FIG. 5, one or more planting regions may be part of a single field (e.g., one field is split into four planting regions: a first planting region 515A, a second planting region 515B, a third planting region 515C, and a fourth planting region 515D). The one or more planting regions are associated with one or more sets of data describing the conditions, composition, and other characteristics that may impact crop production and that may vary from planting to region to planting region (even among planting regions within a same field). In embodiments where one or more of the planting regions of a field satisfy a threshold similarity, the crop prediction system 125 can apply a prediction model to the cluster of the one or more planting regions. In other embodiments where various planting regions do not satisfy a threshold similarity, the crop prediction system 125 can apply the prediction model to each planting region individually. Although the example of FIG. 5 describes the portions of land as “planting regions”, it should be noted that in other embodiments, the planting regions of FIG. 5 can be referred to as “sub-portions” of a field, “zones”, or any other suitable terminology.” See also claim 1 of Perry. See also Paragraphs 0189 - 0200.)
providing, as input to a predictive turf model for a respective zone of the plurality of zones, a set of zone sensor data associated with the respective zone, the set of zone sensor data from one or more sensors associated with the respective zone as an input to a predictive turf model for each zone; (Par. 0077: “The crop prediction system 125 receives data from the external databases 112, sensor data sources 114, and image data sources 116, and performs machine learning operations on the received data to produce one or more crop prediction models. The data from these data sources can be combined, and a standard feature set can be extracted from the combined data, enabling crop prediction models to be generated across different temporal systems, different spatial coordinate systems, and measurement systems.” Par. 0118: “The training module 410 may periodically update crop prediction models in response to a triggering condition. For example, the training module 410 can update a crop prediction model after a set amount of time has passed since the model was last updated, after a threshold amount of additional training data is received corresponding to the field and crop variant associated with the model (e.g., new data is received for fields with a threshold similarity to the field; new data is received for the crop variant; new data is received from the corresponding grower client device 102 about applied farming operations), or after a threshold amount of field information is obtained for an already-planted crop (e.g., a current crop growth rate, a pest infestation rate, etc.).”)
determining a current estimated turf condition for each the respective zone
based on a current output of the predictive turf model for each the respective zone; (Par. 0139: “The crop prediction module 425 applies the prediction model P to the field parameters and a set of farming operations to generate a prediction of crop production for the field parameters and the set of farming operations. Continuing with the previous examples, for a specified field portion, soil type, historic planted crops, available irrigation, historic sunlight information, crop yield for similar fields, and expected future rainfall, the crop prediction module 425 can apply a corresponding crop prediction model that, when applied, performs one or more machine learning operations on these farming parameters to identify a set of farming operations and a predicted crop production for the field portion if the selected set of farming operations are performed.” Par. 0143 – 0145 – prediction for various soil conditions. See also Par. 0138, 0164, 0166 (a model for the particular field).)
Perry may implicitly teach generating a recommendation as in paragraph 0038 that provides a suggestion. However, Bentwich does teach generating a current recommended action for managing turf in the respective zone based on the estimated turf condition for the respective zone. (Par. 0036: “…it would be possible to divide the field into effective irrigation zones, and monitor soil moisture in each of these zones, knowing that the same soil moisture is expected to be found everywhere within this zone. Irrigation could then be guided accordingly.” Par. 0142: “Optionally, each of the displayed informational graphic illustrations is colored in the same color as the associated soil-topography zone of stage 2010. Optionally, the information graphic illustration illustrates: a representation of the current moisture level of the soil of the respective soil-topography zone in relation to a maximum moisture capacity of the soil of the soil-topography zone; and a representation of a recommended irrigation setting of the irrigation device set of the respective soil-topography zone in relation to a maximum irrigation setting of the irrigation device set.” Par. 0099: “The compute irrigation 400 compares each sensor reading received, with the soil propertied of the soil of the irrigation zone, and the irrigation goal defined by the user, and calculates accordingly the recommended irrigation for that zone. Next step, present to user via app 420, preferably presents a tentative irrigation map, for each of the zones of the field 105 of FIG. 1, preferably via an app on a mobile device, or a computer, or a web browsing device.” See also Par. 0079 and 0080 that teach predictive modelling; and several other paragraphs teach generating a recommended action such as paragraphs 0100, 0122, 0132, 0143, and others that generating a recommendation for a zone.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method that allows uses sensors in multiple zones with environmental similarity of that defines the zones, fields, or plots that creates a predictive model for a field portion or zone wherein the predict a parameter based on the model for the zone/field as in Perry with a method of controlling irrigation by predictive modeling of divided irrigation zones and calculate a current recommend action for a particular zone as in Bentwich in order to create an efficient irrigation plan which ascertains a correct amount of water that represents water savings. (Par. 0010)
Regarding claim 18,
The previously cited references teach the limitations of claim 16 which claim 18 depends. Perry also teaches that updating the predictive turf model based on subsequent zone sensor data and a measured turf condition. (Par. 0117: “…the training module 410 can update a crop prediction model after a set amount of time has passed since.” Par. 0013 (“re-applying the prediction model to the periodically accessed field information.”). See also Par. 0118 – updated model based on sensor training information. Examiner’s Note – Perry teaches 40 instances of “updating” which applies to the models. See also Bentwich Paragraph 0079 and 0080.)
Regarding claim 23,
The previously cited references teach the limitations of claim 18 which claim 23 depends. Bentwich also teaches determining a subsequent estimated turf condition for the respective zone based a subsequent output of the updated predictive turf model for the respective zone and generating a subsequent recommended action for the respective zone based on the associated subsequent estimated turf condition for the respective zone. (Par. 0039 – (model to output subsequent or intermediate data; and next iterations of the model). Par. 0132: “Within informational graphical illustration 930, two iconized elements are displayed: a bar graphic representation 950 and a drop graphic representation 960. The bar graphic representation 950 graphically displays a relation between a current soil moisture level in soil-topography zone 910 relative to a field-capacity value and a refill-point value, both of soil-topography zone 910. The drop graphic representation 960 graphically displays an amount of irrigation recommended by the system for a next irrigation event of soil-topography zone 910 relative to a maximal amount of irrigation in an irrigation event, as determined by the user. Numeral 955 designates the current soil moisture level in soil-topography zone 910. In one embodiment, numeral 955 represents an integration of a plurality of soil moisture measurements taken at different depths, such as two readings taken at two depths, or three readings taken at 3 depths. In another embodiment, numeral 955 is a number of volume units of water within a predetermined length of depth of soil, such as square millimetres or square inches, in a meter or another predetermined length measurement unit, of soil. In another preferred embodiment, numeral 955 represents an integration of soil moisture readings taken by several sensors, in order to increase the measurement accuracy. Numeral 365 designates the amount of irrigation recommended by the system for the next irrigation event of soil-topography zone 910. Bar graphic representation 970, drop graphic representation 980, and numerals 975 and 985 are similar to elements 950, 960, 955 and 965, respectively with the exception that they are related to soil-topography zone 920 and are displayed within informational graphical illustration 940.” See also Par. 0012 and 0040.)
Regarding claim 26,
The previously cited references teach the limitations of claim 16 which claim 26 depends. Perry also teaches that the sets of zone sensor data associated with a zone comprise environmental conditions and trends. (Par. 0145: “In some embodiments, a crop prediction model can be a Bayesian model that includes Gaussian processes or splines, trained on historic soil samples, information describing trends in soil nutrient contents and composition, and information describing soil variation over geographic regions. The resulting Bayesian model can interpolate soil sample information over a portion of land, such as a grower's field, for instance using Markov Chain Monte Carlo sampling or variational inference estimations. After samples and corresponding locations are accessed for a particular portion of land, the Bayesian model can be queried for a soil characteristic or measurement (such as pH) at a particular location within the portion of land, and the Bayesian model can provide an inferred soil characteristic or measurement (or a probable range) in response.” See also Silva and Par. 0017: “In various embodiments, the regions 220 are differentiated from one another by identified conditions in the soil, growth patterns of plants therein, altitudes, or the like.” See also Silva paragraph 0019.)
Regarding claim 27,
The previously cited references teach the limitations of claim 16 which claim 27 depends. Bentwich also teaches that transmitting the recommended action for the respective zone to a user device for display on the user device. (Par. 0132: “The drop graphic representation 960 graphically displays an amount of irrigation recommended by the system for a next irrigation event of soil-topography zone 910 relative to a maximal amount of irrigation in an irrigation event…” See also Par. 0135, 0142.)
Regarding claim 28,
The previously cited references teach the limitations of claim 16 which claim 28 depends. Bentwich also teaches that comprising providing a command to turf maintenance equipment to automatically carry out the recommended action for the respective zone. (Par. 0005: “Topography Integrated Ground water Retention (TIGER) map generator includes: a computerized topographic feature processing functionality providing information relating to at least one of slope, aspect and catchment area features of said area to be irrigated; and a computerized topographic feature utilization functionality employing at least one of slope, aspect and catchment area features of the area to be irrigated for automatically ascertaining water retention at a plurality of different regions within the area to be irrigated; and a computerized computing functionality employing the Topography Integrated Ground water Retention (TIGER) map together with at least current outputs of wetness sensors located at the plurality of different regions within the area to be irrigated to generate a current irrigation plan; and a computerized irrigation control subsystem automatically utilizing the current irrigation map to control irrigation within the area to be irrigated based on the current irrigation instructions and to cause different amounts of water to be provided to the different regions within the area to be irrigated.” Par. 0103: “In accordance with another preferred embodiment, the differential irrigator 100 of FIG. 1 may automatically control differential irrigation of the field 105, through use of a drip irrigation system.” See also Par. 0106.)
Regarding claim 36,
The previously cited references teach the limitations of claim 16 which claim 36 depends. Perry also teaches that each zone of the plurality of zones is one or more portions of the work region that are characterized by a single node, the single node providing a representation of a micro-climate of each zone, and wherein each single node includes a respective one or more sensors. (Par. 0019, 0118, 0139, 0164, and claim 6. See also Silva, cited below and Par. 0015, 0045, and Par. 0023: “The yield predictor identifies at least one data collection point 310 per region 220, and may select the data collection point 310 to be the most representative point in the region. Stated differently, a data collection point 310 is selected to represent what the average growth conditions in the region 220 will produce. The yield predictor may impose further constraints on the selection of data collection points 310, such as, for example, a minimum or maximum distance to one or more other points (e.g., another data collection point 310, a border of a region 220, an irrigation system, a fence, a path), a minimum number of data collection points 10 per region 220, the relative locations of previously selected data collection points 310 (e.g., at least x meters away from where a previous measurement was gathered d days ago), and the like.”)
Regarding claim 37,
The previously cited references teach the limitations of claim 36 which claim 37 depends. Perry also teaches that at least one zone of the plurality of zones is defined to include multiple non-contiguous areas within the work region, and wherein a respective single node for the at least one zone is in one of the multiple non-contiguous areas. (Par. 0135: “The crop prediction module 425 receives a request to generate an optimized crop production prediction for a field (which can include multiple fields or plots of land, adjacent or otherwise) and applies one or more crop prediction models data associated with the field to determine a set of farming operations to optimize a crop production for the field.” See figure 5 that shows several non-contiguous areas or portions.)
Regarding claim 39,
The previously cited references teach the limitations of claim 18 which claim 39 depends. Perry also teaches that the actual turf condition for the respective zone includes a disease incidence. (Par. 0158: “Models that map fungicide, herbicide, and nematicide application (including which product to apply, when to apply, where to apply, quantity to apply, and method of application) determined based on one or more of: 1) information about incidence of pests and weeds and disease in nearby geographic regions…”)
Regarding claim 41, it is directed to a system to implement the method set forth in claim 16. Perry and Bentwich teach the method of steps in claim 16. Therefore, Perry and Bentwich teach the system in claim 41.
Claims 19, 20, 21, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Bentwich in further view of Silva et al. (US PG Pub. No. 20210279867), herein “Silva.”
Regarding claim 19,
The previously cited references teach the limitations of claim 18 which claim 19 depends. They do not teach updating the model based on measured conditions of the region. However, Silva teaches that wherein updating the predictive turf model comprises: measuring the actual turf condition for the respective zone of the plurality of zones; and comparing the estimated turf condition for the respective zone and the actual turf condition for the respective zone. (Par. 0039: “At block 460, the yield predictor refines the machine learning models and crop simulation models used in analyzing the crop yields. In some embodiments, the yield predictor performs a regression analysis of growing condition data (from the in-field data collected from the data collection points 310, from image analysis of the fields 110, and from mapped data sources) to the yield related data (i.e., the data directly related to crop production per unit area in a region 220) to identify relationships and relative effects of soil conditions, elevation, irrigation types and volumes, fertilizer types and levels, pest control types and levels, vegetation indices, daily temperatures, sun intensity, etc., on the output of the plants in the region 220. In additional embodiments, the yield predictor uses one or more intermediate models to output intermediate crop development metrics for use by other models when predicting the yield, and refines those models when in-field data are available to compare against. For example, intermediate models may be used to identify one or more of a leaf area index, a development stage for the crop, a dry weight of the crop (e.g., of a grain or legume), a number of seeds/pods/ears/fruiting bodies per plant, etc. Using the identified characteristics from the regression analysis, the yield predictor refines the models used to identify regions 220 and data collection points 310 by adjusting the weights assigned to the various inputs. By refining the models, the yield predictor improves the outputs of the machine learning models so that in a next iteration the models will identify new regions 220 to describe the fields 110, new data collection points 310 in the fields 110, and/or how to model the growth conditions in the field 110.” Par. 0040: “Method 400 may conclude after block 460, or may return to block 420, where the newly refined models identify a new set of regions 220 that more accurately represent the average production of the crop growing in those fields 110 than in previous iterations. Method 400 may iterate several times throughout a growing season at periodic intervals (e.g., every d days), at an operator's request, (using one data set) until the machine learning models converge on a stable set of weights for the input features, or until another end condition is satisfied.” Par. 0021: “For example, the sections of the first region 220a and the second region 220b that border one another may exhibit the same yields as one another, and the sections of the second region 220b and third region 220c that border one another may also exhibit the same yields as one another, which are different than the yields at the border of the first and second regions 220a-b. Stated differently, yields may vary over the course of an individual region 220 more significantly than yields vary between adjacent portions of different regions; however, the yields within the individual region 220 are predicted to be within a given range of the average yield for that region 220 despite any variability therein. As data are collected for the regions 220, the machine learning model may update how different features are weighted and move the borders of the second region 220b (e.g., expanding the first region 220a and shrinking the second region 220b or vice versa) to better reflect the actual growth conditions in future predictions for regional yield.” See also Par. 0054, )
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method that allows uses sensors in multiple zones with environmental similarity of that defines the zones, fields, or plots that creates a predictive model for a field portion or zone wherein the predict a parameter based on the model for the zone/field as in Perry with a method of controlling irrigation by predictive modeling of divided irrigation zones and calculate a current recommend action for a particular zone as in Bentwich with updating or refining a model used to identify regions where data from the regions or fields is collected as in Silva in order to accurately produce crop yield predictions. (Par. 0012 and 0040)
Regarding claim 20,
The previously cited references teach the limitations of claim 19 which claim 20 depends. Silva teaches that the actual turn condition is based on observed turf condition at the respective zone. (Par. 0027: “The yield predictor extrapolates the observed data from the data collection points 310 to the rest of the region 220, and may use kriging or a data-driven model to blend the estimates from neighboring regions 220 to arrive at a yield estimate for a field 110.” Par. 0022 - 0023: “A data collector 330 may be an automated system (such as the first data collector 330a, which is illustrated as a flying drone) or a person (such as the second data collector 330b). The yield predictor identifies at least one data collection point 310 per region 220, and may select the data collection point 310 to be the most representative point in the region.” “See also Par. 0003 – 0005, 0027, 0032, 0039, 0042, and claim 4 of Silva. Examiner’s Note – see also Bentwich in numerous paragraphs that teach user input (observed) and also paragraphs 0026)
Regarding claim 21,
The previously cited references teach the limitations of claim 19 which claim 21 depends. Silva teaches that the actual turf condition is measured by one or more turf condition sensors. (Par. 0045: “In one instance, the vegetation index may have been gathered when farm equipment was present in the field 110, and the outlier location may be an outlier due to the farm equipment obscuring the crop from the view of the remote sensor 130, and can be included in the area of the region 220 used for yield predictions. In another instance, the variance in vegetation index may be due to an ongoing factor that may reduce the area of the region 220 that is used for yield predictions (e.g., the location corresponds to a well head, a pivot for an irrigation system, an area around a salt lick, the footprint of a wind turbine or other permanent fixture in the field 110, etc.). Accordingly, the field analyzer may identify all the locations in the region 220 are outliers as candidate data collection points 310 for the associated region 220; however, these candidate outlier data points are treated differently than the candidate typical data points (identified per block 520) by the field analyzer and the yield predictor.” Par. 0014: “Three remote sensors 130a-c (generally, remote sensor 130), are illustrated in relation to the crop growing area 100 that provide remote sensing of various features of the fields 110 and sectors 120 therein. A remote sensor 130 may be a satellite imager (as in the first remote sensor 130a), a fully or semi-autonomous aerial imager or “drone” (as in the second remote sensor 130b), a piloted aerial imager (as in the third remote sensor 130c), or another system capable of capturing images of the crop growing area 100 that can be used to identify individual fields 110 or sectors 120 and/or conditions affecting or evidencing plant growth therein. These data may be combined to form a composite image from several remote sensors 130 that are collected as substantially the same time, or at substantially different times. For example, a swarm of several drone-type remote sensors 130 may be directed to collect remotely sensed data over the course of one day, which can be combined into a composite image and data set related to the day, which can further be combined with an earlier (or later) collected composite image or data set (e.g., collected by the swarm or a satellite on a different day).” Examiner’s Note – Silva teaches in many instances sensors and imagery for regions of the field which are actual conditions.)
Regarding claim 25,
The previously cited references teach the limitations of claim 16 which claim 25 depends. Silva teaches that further including inputting information pertaining to previous action taken to manage conditions at the associated respective zone into the predictive turf model for the respective zone. (Par. 0040: “Method 400 may conclude after block 460, or may return to block 420, where the newly refined models identify a new set of regions 220 that more accurately represent the average production of the crop growing in those fields 110 than in previous iterations. Method 400 may iterate several times throughout a growing season at periodic intervals (e.g., every d days), at an operator's request, (using one data set) until the machine learning models converge on a stable set of weights for the input features, or until another end condition is satisfied.” See also Par. 0032: “These criteria may be weighted differently from one another in the machine learning model, and can include one or more of: vegetation indexes visible in images of the field 110, soil conditions throughout the field 110 from previously collected data or soil maps, fertilizer application levels throughout the field 110 from previously collected data or operator reporting, pest control application levels throughout the field 110 from previously collected data or operator reporting, and previously collected vegetative growth and yield metrics from an earlier data collection iteration in the field 110.” Par. 0018: “An operator may also provide data related to fertilization levels, pest control levels, and previously collected growth analysis data (e.g., at time to the density of plants per area, density of pods per plant, and density of seeds per pod were x, y, and z, respectively).” Examiner’s Note – See also Scheiner in the conclusion section.)
Claims 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Bentwich in further view of McEntire et al. (US Patent No. 11,704,581), herein “McEntire,” supported by four provisional patent applications filed before the effective filing date of the instant application.
Regarding claim 29,
The previously cited references teach the limitations of claim 16 which claim 29 depends. They do not teach the elements of claim 29 wherein a recommended action comprises forming a ruleset and executing the ruleset and model via knowledge engine. However, McEntire does teach generating the recommended action comprises: forming a ruleset (list or response surfaces for each interaction or instruction blocks, See figure 14.) comprising a set of rules ordered by priority, the set of rules producing deductions as outputs; (Col. 61, lines 49 – 63: “ The illustrative method continues with relative accuracy testing using cross validation at decision diamond 14250 and tuning of the RS weighting coefficients at process block 14260 and model refitting at process block 14270 to achieve the desired accuracy and model estimation expectations for each of the interactions in the list. The illustrative regression process examines the estimated model output for the main-effect and the main co-occurring features from one or more set of interventions that have exhibited the highest co-occurrence frequency from the RF model. Once a pruned list of low order interactions is built by ranking and reduction analysis, the method iterates through at least one list of Response Surfaces starting with the surface that trends to be the closest to the main-effect, moving through the list adding and removing certain interventions from the list.” See also Col. 60 lines 43 – 65; Col. 61, line 37 – Col. 62, line 36)
forming one or more models that take the set of zone sensor data, the deductions, and initial conditions as inputs and provide new deductions as output; (Col. 23, lines 12 – 27: “For example, the crop prediction engine 4000 may prescribe nutrients, seeds or other farm management supplies and based on such predictions autonomously place purchase orders directly to manufacturers and suppliers. Additionally, a client device may be used to directly obtain bids from crop purchasers and/or crop brokers 3030 who can view estimated production volumes and prices directly. The prediction engine 4000 inputs data from multiple client devices to source information and derive the optimal soil and seed application costs for desired crop production in preparation for planting by allowing the farmer, agronomist, or crop consultant to rely on trained AI models to understand the multiplicity of soil and seed characteristics decisions. Thus, recommendations for optimal crop efficiency with precision application may be obtained through the crop prediction engine 4000 described herein.” Col. 35, lines 12 – 26: “At process block 5300, program instructions representing an illustrative training model are used by the crop prediction engine 4000. In the illustrative embodiment, a random forest (RF) training model is implemented as a computational model. In operation, each of the RF trees is built into a set of tree estimators and calibrated using first out-of-bag training data, which assesses initial conditions, parameter settings, and first pass model quality using techniques like R-squared error minimization prior to application of actual training data sets. Tuning of the RF training model may be iterative and may use different feature weights based on the desired properties inherent to the set of training data as known to one of skill in the art. Once the RF training model has been tuned, a data set of multi-dimensional set of features is applied to each tree in the random forest.” See also Col. 2, lines 43 – 67. See also Col. 22, line 10 – Col. 23, line 61; and Col. 61, line 37 – Col. 62, line 62; Col. 63, line 46 – Col. 66, line 36, and figures 4, 14, and 16, respectively, that describes the interactive (ruleset) of the crop prediction engine that uses modeling that results in recommendations)
and executing the ruleset and the one or more models via a knowledge engine to determine the recommended action. (Col. 66, lines 50 – 62: “The final step performed by the programming software running on the Data Science Computing Cluster 4300 with support from the NFS software 4301 is to use all observations to formulate Reduced Order Surrogate model (RoSM) and subsequently a GAM model to determine the optimum seed and chemical nutrients to apply to achieve the maximum crop performance 14000. This “Prescription” is then sent to the Application Back-End 4001 for further processing and subsequent downloading via the Transactional Data Transport Services 4002 to the client browser 4003 for final output display and/or printed reports or manufacturer supply orders for seed and nutrient recommendations and purchases.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method that allows uses sensors in multiple zones with environmental similarity of that defines the zones, fields, or plots that creates a predictive model for a field portion or zone wherein the predict a parameter based on the model for the zone/field as in Perry with a method of controlling irrigation by predictive modeling of divided irrigation zones and calculate a current recommend action for a particular zone as in Bentwich with using interactive programming, containing rules, with machine learning that determines recommendations of chemical application for plants or crops as in McEntire in order to build a generalized model running as a tool on scalable, client/server web based computing platforms in order to optimize agricultural efficacy. (Col. 4, lines 55 – 58).
Regarding claim 30,
The previously cited references teach the limitations of claim 29 which claim 30 depends. McEntire also teaches displaying the ruleset to a user device to provide an explanation as to why the recommended action was selected. (Col. 66, lines 1 – 23: “The Application Back-End 4001 software programming enables the cloud computing hardware 4100 along with the System Software NFS Layers 4101 to send the geographical representation of each Voxel. In the illustrative embodiment, the geographical representation is sent through one or more load balancers 4110, one or more Internet Networks 3000 and terminating at one or more local gateways 4050 prior to output display on one or more client browsers 4003. The output displayed may include: input variables, output dependent variables and other predicted crop or farm operational responses 4430. These output responses 4430 may be displayed visually, by text lists or be embedded into downloadable reports or supplier order forms. Calculated and predicted outputs sent to the application computing cluster from block 8500 for mapping and display may include: AGT data-set clusters 4430i, crop-yield predictions 4430h, soil carbon predictions 4430f, ROI on operations and harvest analysis 4430e, digital elevation and moisture indexes 4430g, various interactive maps 4430a, nutrient recommendations 4430b, seed recommendations 4430c, digital prescriptions 4430d, and ETL results for big data repository and data-completeness verification purposes.” See also Col. 67, lines 37 – 60.)
Claim 42 is rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Bentwich in further view of Dail et al. (US PG Pub. No. 20190156437), herein “Dail.”
Regarding claim 42,
The previously cited references teach the limitations of claim 41 which claim 42 depends. They do not teach that the model has a risk component with the probability of disease. However, Dail teaches that wherein the parameter includes risk indicator comprising at least one of a percentage or score representative of an amount of risk the respective zone (crop) is at for developing disease. (Par. 0043: “Agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from field manager computing device 104, external data 110 from external data server computer 108, and sensor data from remote sensor 112. Agricultural intelligence computer system 130 may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic such as FPGAs or ASICs, or any combination thereof to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts to application controller 114, in the manner described further in other sections of this disclosure.” Par. 0156: “Agricultural intelligence computer system 130 may additionally determine a benefit of applying a fungicide, such as by comparing the correlation between risk value and total yield with correlations between crops with similar risk values which received fungicide and total yield. If agricultural intelligence computer system 130 determines that the risk value will exceed a threshold within the next fourteen days, agricultural intelligence computer system 130 may generate a recommendation to apply fungicide to the crop, thereby reducing the probability of disease. By modeling the crop damaging factors occurring in the future, agricultural intelligence computer system 130 is able to generate recommendations that, if implemented, prevent damage to the crop.”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system and method that allows uses sensors in multiple zones with environmental similarity of that defines the zones, fields, or plots that creates a predictive model for a field portion or zone wherein the predict a parameter based on the model for the zone/field as in Perry with a method of controlling irrigation by predictive modeling of divided irrigation zones and calculate a current recommend action for a particular zone as in Bentwich with using a model to determine the risk of disease as in Dail in order to generate a recommendation to prevent damage to the crop.
Election/Restrictions
Restriction to claim 40 was required under 35 U.S.C. 121:
Invention I: Claim 40 is directed to a method of managing turf in a work region, the method comprising: monitoring multiple sets of zone sensor data, each set associated with a different zone of a plurality of zones dividing the work region, each zone defined to capture homogeneous environmental conditions therein…” Although similar to claim 16, claim 40 contains the element: providing, as input to a predictive turf model for a respective zone of the plurality of zones, (a) a set of zone sensor data associated with the respective zone, the set of zone sensor data from one or more sensors associated with the respective zone, (b) a previous action taken in the respective zone, and ( c) an outcome of the previous action taken in the respective zone; determining a current estimated turf condition for the respective zone based on a current output of the predictive turf model for the respective zone;”
Invention II, drawn to claims that depend from claim 16, is directed to a method of managing turf in a work region, the method comprising monitoring multiple sets of zone sensor data, each set associated with a different zone of a plurality of zones dividing the work region, each zone defined to capture homogeneous environmental conditions therein…” Claim 16 contains a set of zone sensor data that does not incorporate those elements of claim 40 (a) – (c) above.
Inventions I and II are related as subcombinations disclosed as usable together in a single combination and there would be a search burden as installing a device is a different search and classification. The inventions are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one Group is separately usable. In the instant case, Group I has separate utility such as using different sets of data, a previous action taken in a zone, and an outcome of a previous action taken that are input to the model. See MPEP § 806.05(d)
The examiner has required restriction between the combinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claims depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all these inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because at least the following reasons apply:
• the inventions have acquired a separate status in the art in view of their different classification;
• the inventions have acquired a separate status in the art due to their recognized divergent subject matter;
• the inventions require a different field of search (e.g., searching different classes /subclasses or electronic resources, or employing different search strategies or search queries);
• the prior art applicable to one invention would not likely be applicable to another invention; and/or
• the inventions are likely to raise different non-prior art issues under 35 U.S.C. §101 and/or 35 U.S.C. §112, first paragraph.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103(a) of the other invention.
Allowable Subject Matter
Claim 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §112(b) rejections above (for claim 16). Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose the elements of claims 16, 18, 19, 21, and that the one or more turf condition sensors comprise a light reflectance sensor and wherein the actual turf condition is determined based on an amount of reflected light in one or more wavelengths compared to one or more types of plant stress response.
Claim 38 is objected to as being dependent upon a rejected base claim, but this claim was rejected under 35 U.S.C. §112(a) and 35 U.S.C. §112(b) above for new matter and therefore not allowable. The prior art does not disclose the elements of claim 16 and including inputting an actual action for the respective zone into the predictive turf model for the respective zone, and wherein the actual action is different than the recommended action for the respective zone.
Response to Arguments
Applicant’s arguments with respect to all claims have been considered but are moot because the arguments do not apply in light of the new reference being used in the current rejection necessitated by amendment. Specifically, the new reference, Perry teaches those elements that were amended in independent claim 16 as rejected herein. Examiner is not persuaded that Bentwich fails to teach a current recommendation. Claim 16 and the new claim(s) cited new prior art that was necessitated by amendment and therefore this action is made final.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jagyasi et al. (US PG Pub. No. 20170223900) may also teach that the elements of the independent claim of generating a recommended action for each zone based on the associated estimated turf condition for each zone in paragraph 0007: “…generic forecasting generation module (216); selecting at least one feature out of the feature set for generating a plurality of adaptive forecasting model based for ecological forecasting on the selected feature out of the feature set using an adaptive forecasting generation module (218); and recommending a plurality of control measures to a user based on said generated adaptive forecasting model using a recommendation generation module (220).”
Scheiner et al. (US PG Pub. No. 20230165181) may also teach the elements of claim 25 in Par. 0275: “The analyzer/control system may also recommend adding a run or varying the protocol of a run to increase the sampling density of a certain area of the growing site in order to verify the effectivity of a specific treatment action previously carried out.”
Cited in the previous office action and cited above for other dependent claims, Silva teaches the first elements of claim 16. Silva teaches a method of managing turf (crop or plants in a crop) in a work region, (Par. 0012: “Knowing the field conditions and how representative plants are growing allows farmers to predict the yield for a crop and to make adjustments to thereby improve yields and make arrangements for the harvest of the crop ( e.g., determine silo space needed, plan for time of harvest, locate distributors, plan for next seeding).” Examiner’s Note – Silva teaches a method of managing plants in a crop or fields for farms, but the same method could be used for a turf; and specification paragraph 0032 states that the turf may be subject to “cultivation” which is a process used on a farm or agriculture field that contains beans, corn or other tillable crops.)
the method comprising: monitoring multiple sets of zone sensor data, (data collection points and sensors) each set associated with a different zone of a plurality of zones dividing the work region, (Par. 0036: “In various embodiments, the information for the pathing 320 is sent to the data collector 330 to navigate to an initial or subsequent data collection point 310 and to collect specific data at that data collection point 310. For example, the data collector 330 may be instructed to take a photograph of the plants located at the data collection point 310, which the yield predictor analyzes to extract various data about how the plants are growing at the data collection point 310 (e.g., leaf size, number of leaves, number of fruiting bodies, size of fruiting bodies, estimated content of fruiting bodies, density of plants per area, plant height). In other embodiments, the data collector 330 may specify data values regarding the plants (e.g., leaf size, number of leaves, number of fruiting bodies, size of fruiting bodies, estimated content of fruiting bodies, density of plants per area, plant height), the soil (e.g., pH, grain size, moisture content, temperature), air (e.g., temperature, pollen/particulate count, humidity), and treatments applied to the data collection point 310 (e.g., fertilizers, pesticides), and the like.” Par. 0016: “FIG. 2 illustrates cluster identification in a crop growing area 100, according to embodiments of the present disclosure. As illustrated, the fields 110a-c are subdivided into several regions 220a-g (generally, region 220) that represent similar growing conditions; ignoring the non-analyzed areas 210 (including the various sectors 120 in the crop growing area 100).” See also Par. 0013, 0019, and 0021.)
each zone (region 220x) being sized and shaped to capture homogeneous environmental conditions in a same zone; (Par. 0012: “The present disclosure therefore provides systems and methods for identifying regions in which plants are expected to experience similar growing conditions,…” Par. 0016: “FIG. 2 illustrates cluster identification in a crop growing area 100, according to embodiments of the present disclosure. As illustrated, the fields 110a-c are subdivided into several regions 220a-g (generally, region 220) that represent similar growing conditions; ignoring the non-analyzed areas 210 (including the various sectors 120 in the crop growing area 100).” Par. 0019: “The yield predictor includes a machine learning model that is trained to use the characteristics of the field 110 to identify clusters of characteristics that define regions 220 in which similar growing conditions are expected. In various embodiments, the region-identifying machine learning model may be further constrained by the yield predictor to produce regions of a certain number, size, or general shape, to thereby aid data collection and differentiation. For example, a field 110 may be subdivided into two or more regions 220 of at least n hectares that describe the total area of the field 110, where different regions 220 describe portions of the field 110 where the plants are expected to have different growth patterns.” See also Par. 0018.)
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 5pm EST; Off every other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini S. Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
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