DETAILED ACTION
The following is an initial Office Action upon examination of the above-identified application on the merits, per a provisional election made during a telephone interview with Patrick Sullivan (Reg. No. 76,483) on 20 July 2026. Claims 1-20 are pending in this application. Claims 9-20 have been withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking. Claims 1-8 were elected without traverse for prosecution as set forth below.
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 .
Election/Restriction
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-8, drawn to an irrigation system for controlling activation and deviations of a sprinkler in first and second areas based, respectively, on a determined first fluid application target rate and a determined second application target rate, classified in A01G 25/16.
II. Claims 9-19, drawn to a method of controlling a liquid emitting component based on a determined target amount of liquid to be applied to a target area and determined amount of time to supply the liquid to the target area, classified in A01G 25/165.
III. Claim 20, drawn to a method for controlling an operation of a liquid emitting component based on a generated grow plan for a target area, a determined current moisture level, and a generated proposed liquid application plan, classified in A01G 25/162.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination of Invention I has separate utility such as providing moisture to soil of different areas in an environment between natural precipitation events based on determined application rates for each area to advantageously prevent non-uniform soil moisture levels. Alternatively, the subcombination of Invention II has separate utility such as dynamic fluid application that targets a particular location with a particular application amount at a specific time to provide an exact amount of liquid during a desired need. See MPEP § 806.05(d).
Inventions I and III are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination of Invention I has separate utility such as providing moisture to soil of different areas in an environment between natural precipitation event based on determined application rates for each area to advantageously prevent non-uniform soil moisture levels. Alternatively, the subcombination of Invention III has separate utility such as creating a liquid application plan to determine when an irrigation system should be activated and how much water should be applied to a target area to achieve target moisture levels stipulated in the liquid application plan. See MPEP § 806.05(d).
Inventions II and III are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination of Invention II has separate utility such as dynamic fluid application that targets a particular location with a particular application amount at a specific time to provide an exact amount of liquid during a desired need. Alternatively, the subcombination of Invention III has separate utility such as creating a liquid application plan to determine when an irrigation system should be activated and how much water should be applied to a target area to achieve target moisture levels stipulated in the liquid application plan. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) 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 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 the 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 one or more of the following reasons apply:
(a) Separate classification thereof (Invention I: A01G 25/16, Invention II: A01G 25/165, and Invention III: A01G 25/162);
(b) A separate status in the art when they are classifiable together; and/or
(c) A different field of search (Invention I: (sprinkler irrigat$5 ) AND (machine ADJ learn$5 (train$5 learn$5) near2 model$5) AND (setpoint set ADJ point threshold limit maximum) WITH (water$5 irrigat$5 appl$7) WITH (amount rate volume) SAME (shutoff shut-off stop$5 halt$6 control$5 ) AND soil AND (different plural$5 numerous second third separate) WITH (region area section portion zone location); Invention II: (water$5 near2 (component device equipment) liquid near2 (applicat$5 emit$6 output) sprinkler irrigat$5) AND (tim$5 interval duration period data schedul$5) AND (liquid substance water$5 fluid) WITH (quantit$5 total volume amount portion); Invention III: (water$5 near2 (component device equipment) liquid near2 (applicat$5 emit$6 output) sprinkler irrigat$5) WITH (schedul$5 plan$5 scheme strateg$5) AND (grow$5) AND (soil adj (kind type) or (plant crop) NEAR2 (kind type) or season) AND (recent present current real-time real adj time) WITH (moisture wet$5).
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
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 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Patrick Sullivan (Reg. No. 76,483) on 20 July 2026 a provisional election was made without traverse to prosecute the invention of Group I, claims 1-8. Affirmation of this election must be made by applicant in replying to this Office action. Claims 9-20 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Claim Objections
Claims 4, 5, and 7 are objected to because of the following informalities:
Claim 4 recites “a supervised machine learned model” in lines 2-3 and claims 4 (line 3), 5 (line 4), and 7 (line 1) recite “the machine-learned model”. The claims recite the use of two different terms for the same limitation. To avoid ambiguity in the claims a single term should be used for each claim limitation. Suggested claim language: “the supervised machine learned model” in claims 4 (line 3), 5 (line 4), and 7 (line 1); and for the purpose of examination the limitation has been interpreted as such. Appropriate correction is required.
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.
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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2005/0187665 A1 (hereinafter Fu) in view of U.S. Patent Publication No. 2003/0230638 A1 (hereinafter Dukes).
As per claim 1, Fu substantially teaches the applicant’s claimed invention. Fu teaches the limitations of an irrigation system comprising:
a sprinkler configured to apply a fluid to a soil substrate (pg. 2, par. [0016]; i.e. “Although not shown, a sprinkler system, for example, can be placed on the surface of area 100 (e.g., using hoses and sprinkler heads) or underground in a well known manner, and the sprinkler heads placed at one or more locations in the yard are controlled by water controller 146.”);
one or more computer processing components (pg. 4, par. [0032]-[0034]; i.e. a processor); and
a non-transitory computer readable media with computer-executable instructions stored thereon that, when executed by the one or more computer processing components, cause the one or more computer processing components to perform operations (pg. 4, par. [0032]-[0034]) comprising:
determining a first fluid application target rate for a first area and a second fluid application target for a second area, the first fluid application target being different than the second fluid application target (pgs. 2-3, par. [0016], [0019], and [0020]; i.e. [0020]: “Armed with all of this information, processor 120 can calculate moisture needs for, at minimum, the portions of area 100 proximate to sensors 122 through 134, and water controller 146 can then be utilized to provide moisture to the various portions of the area 100. Taking into consideration the moisture levels, the needs of the various plants/grasses/trees, the weather forecast, the soil information, and any other information obtained by processor 120, the moisture delivery time utilized for the various portions of the yard may be controlled.”).
Fu does not expressly teach a valve for regulating a fluid supply to the sprinkler;
a first area and a second fluid application target rate
determining a first amount of applied fluid at the first area and a second amount of applied fluid at the second area;
based on a determination that the first amount of applied fluid at the first area is greater than the first fluid application target rate, causing the sprinkler to stop applying the fluid in the first area; and
based on a determination that the second amount of applied fluid at the second area is less than the second fluid application target rate, causing the sprinkler to continue applying the fluid in the second area until the second amount of realized fluid application at the second area is greater than the second fluid application target rate.
However Dukes, in an analogous art of irrigation systems (abstract and pg. 1, par. [0003]), teaches the missing limitations of a valve (i.e. a solenoid valve) for regulating a fluid supply to a sprinkler (pg. 2, par. [0024]-[0027]; i.e. sprinkler irrigation structure);
a first area and a second fluid application target rate (pg. 4, par. [0043] and [0050]; [0043]: i.e. “… soil moisture content threshold levels can be set. The soil moisture content level can include a start irrigation threshold and a stop irrigation threshold. Additionally, multiple threshold levels can also be set corresponding to different irrigation rates or volumes, such as a very slow irrigation or low volume, and a relatively fast irrigation or high volume. The soil moisture content threshold levels can be set at optimum levels, or at extreme ranges.” and [0050]: “Each sensor grouping can be linked with a different control device and water delivery mechanism such that each area of a larger region can be irrigated independently of the other areas.”);
determining a first amount of applied fluid at a first area and a second amount of applied fluid at a second area (pg. 4, par. [0043] and [0050]; [0043]: i.e. “… soil moisture content threshold levels can be set. The soil moisture content level can include a start irrigation threshold and a stop irrigation threshold. Additionally, multiple threshold levels can also be set corresponding to different irrigation rates or volumes, such as a very slow irrigation or low volume, and a relatively fast irrigation or high volume. The soil moisture content threshold levels can be set at optimum levels, or at extreme ranges.” and [0050]: “Each sensor grouping can be linked with a different control device and water delivery mechanism such that each area of a larger region can be irrigated independently of the other areas.”);
based on a determination that the first amount of applied fluid at the first area is greater than the first fluid application target rate, causing the sprinkler to stop applying the fluid in the first area (pg. 4, par. [0045] and [0046]; i.e. [0045]: “… multiple TDRS's wherein each measurement from a particular TDRS is compared with corresponding or unique threshold for that sensor, and/or a measure of the representative soil moisture content as disclosed herein. The comparison of step 280 can be used to determine whether irrigation should be begun or ended. For example, the comparison can indicate whether the soil moisture content has risen or dropped outside of the acceptable set threshold levels. Accordingly, the irrigation can start and stop at the predetermined soil moisture content levels.” and [0046]: “… when the TDRS measurements (or the representative moisture content level) have risen above the set threshold soil moisture content levels and indicate that the soil contains too much moisture, the irrigation can stop.”); and
based on a determination that the second amount of applied fluid at the second area is less than the second fluid application target rate, causing the sprinkler to continue applying the fluid in the second area until the second amount of realized fluid application at the second area is greater than the second fluid application target rate (pg. 4, par. [0045] and [0046]; i.e. [0045]: “… multiple TDRS's wherein each measurement from a particular TDRS is compared with corresponding or unique threshold for that sensor, and/or a measure of the representative soil moisture content as disclosed herein. The comparison of step 280 can be used to determine whether irrigation should be begun or ended. For example, the comparison can indicate whether the soil moisture content has risen or dropped outside of the acceptable set threshold levels. Accordingly, the irrigation can start and stop at the predetermined soil moisture content levels.” and [0046]: “… when the TDRS measurements (or the representative moisture content level) have fallen below the set threshold soil moisture content levels and indicate that the soil is too dry, the irrigation can start.”) for the purpose of watering dry soil (pg. 4, par. [0046]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu to include the addition of the limitations of a valve for regulating a fluid supply to a sprinkler; a first area and a second fluid application target rate; determining a first amount of applied fluid at a first area and a second amount of applied fluid at a second area; based on a determination that the first amount of applied fluid at the first area is greater than the first fluid application target rate, causing the sprinkler to stop applying the fluid in the first area; and based on a determination that the second amount of applied fluid at the second area is less than the second fluid application target rate, causing the sprinkler to continue applying the fluid in the second area until the second amount of realized fluid application at the second area is greater than the second fluid application target rate
to advantageously provide sufficient water resources for efficient agricultural production at high volumes (Dukes: pg. 1, par. [0006]).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Dukes in further view of U.S. Patent Publication No. 2022/0051118 A1 (hereinafter Rooney).
As per claim 2, Fu teaches determining the first amount of applied fluid and the second amount of applied fluid is based on feedback data from a sensor (pg. 3, par. [0021]; i.e. “Further, the moisture sensors can be monitored on a real-time basis so that, once a moisture level at a particular moisture sensor reads a predetermined moisture level, this information is used by the processor and water controller to cut off the delivery of moisture to the appropriate sprinkler heads.”).
Fu does not expressly teach an optical sensor.
Fu in view of Dukes does not expressly teach an optical sensor.
However Rooney, in an analogous art of agriculture management systems (pg. 1,
par. [0002]), teaches the missing limitation of an optical sensor (pg. 5, par. [0045]: “Sensor units can also be deployed on overhead mobile platforms, e.g., aerial drones, manned and unmanned aircraft, satellites for obtaining images and other data related to the physical site, e.g., weather stations, soil moisture and temperature sensors, imaging spectrometers, thermal cameras or minirhizotrons. Sensor units can also be fixed on stationary devices deployed at the physical site. For sub-surface (of the growing medium) measurements, probe sensor units can be inserted into the growing medium, as described in more detail below with reference to FIG. 2. The combination of above-ground, surface-level, and below-ground geo-spaciotemporal data can be aggregated into “data cores” for analytical and management purposes.”) for the purpose of measuring characteristics of a physical site (pg. 5, par. [0045]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes to include the addition of the limitation of an optical sensor to reduce or eliminate measurement inaccuracy by minimally disturbing measured characteristics of a physical location (Rooney: pg. 3, par. [0026]).
As per claim 3, Fu teaches one or more weather sensors configured to measure weather conditions (pg. 3. par. [0024]: “although not shown, if desired, weather equipment (temperature sensors, humidity sensors, barometric pressure sensors, etc.) can be situated in the monitoring area and coupled to the processor and provide weather data thereto.”).
Claims 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Fu in view of Dukes in further view of Rooney and U.S. Patent Publication No. 2021/0073540 A1 (hereinafter Tran).
As per claim 4, Fu in view of Dukes in further view of Rooney does not expressly teach the one or more computer processing components are configured to perform the operations using a supervised machine learned model, and wherein the machine-learned model is trained to determine each of the first and second fluid application rates based on a dataset comprising historical weather data measured by the one or more weather sensors .
However Tran, in an analogous art of irrigation systems (pg. 1, par. [0004] and [0013]), teaches the missing limitation of perform operations using a supervised machine learned model (pg. 19, par. [0215]; i.e. “As well as the different techniques in machine learning, there are three different types: supervised, unsupervised, and reinforcement learning. Supervised learning, which involves feeding a machine labeled data, is the most commonly used and also has the most practical applications by far.”), and wherein the machine-learned model is trained to determine each of first and second fluid application rates based on a dataset comprising historical weather data measured by one or more weather sensors (pg. 39, par. [0377] and pgs. 40-41, par. [0387] and [0388]; i.e. [0377]: “The machine learning system can deploy any of the following: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation.” and [0387]: “… the engine accesses field information describing characteristics of the crop growth and generates a prediction model trained on crop growth information and mapping data and during live operation determines a set of farming operations that maximize crop productivity, for example the engine identifies one or more of: a type or variant of crop to plant if any, an intercrop to plant, a cover crop to plant, a portion of the first portion of land on which to plant a crop, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a portion of the first portion of land on which to apply a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a portion of the first portion of land on which to apply an agricultural chemical, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation if any, a date to apply irrigation, and a rate of application for irrigation.”) for the purpose of dispensing water to crops in a field (pg. 1, par. [0006]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes in further view of Rooney to include the addition of the limitation of perform operations using a supervised machine learned model, and wherein the machine-learned model is trained to determine each of first and second fluid application rates based on a dataset comprising historical weather data measured by one or more weather sensors to advantageously enable growers to manage their fields for better yields and profitability (Tran: pgs. 1-2, par. [0023]).
As per claim 5, Fu in view of Dukes does not expressly teach the one or more computer processing components are further to configured to receive data from one or more soil moisture sensors, and wherein the data from the one or more soil moisture sensors is used by the machine-learned model to train the feedback data of the optical sensors.
However Rooney, in an analogous art of agriculture management systems (pg. 1,
par. [0002]), teaches the missing limitation of receive data from one or more soil moisture sensors, and wherein the data from the one or more soil moisture sensors is used by a machine-learned model to train the feedback data of the optical sensors (pg. 2, par. [0016]-[0018], pg. 5, par. [0045], and pg. 10, par. [0093]-[0095]; i.e. [0045]: “Sensor units can be deployed to the physical site through ground-based unmanned vehicles (UVs) or manned vehicles, unmanned or manned aerial vehicles. Sensor units can also be deployed on overhead mobile platforms, e.g., aerial drones, manned and unmanned aircraft, satellites for obtaining images and other data related to the physical site, e.g., weather stations, soil moisture and temperature sensors, imaging spectrometers, thermal cameras or minirhizotrons. Sensor units can also be fixed on stationary devices deployed at the physical site.”, [0093]: “… a machine learning model implemented by the analytics engine 115 is a neural network having a plurality of layers, including an input layer, an output layer, and one or more hidden layers. Input to the neural network can be the sensor profiles represented as a vector, array, or tensor of characteristics. Output of the neural network can be a vector of predicted characteristics corresponding to the input sensor profiles.” and [0095]: “The newly obtained sensor profiles can be labeled with the inferred characteristics predicted to correspond with the candidate locations and used as part of additional training data for updating parameter values of machine learning model(s) of the analytics engine 115. In effect, the analytics engine 115 of the system 100 can be improved over time from additional sensor profiles from the sensor processing engine 105. The analytics engine 115 can inform the plurality of sensor units 110 of candidate locations likely to improve the quality of site characterization, obviating the need to measure the location at each coordinate of a physical site, while still providing the granular sensor data used to generate the predicted characteristics and subsequent recommendations of the recommendation engine 120.”) for the purpose of measuring characteristics of a physical site (pg. 5, par. [0045]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes to include the addition of the limitation of receive data from one or more soil moisture sensors, and wherein the data from the one or more soil moisture sensors is used by a machine-learned model to train the feedback data of the optical sensors to reduce or eliminate measurement inaccuracy by minimally disturbing measured characteristics of a physical location (Rooney: pg. 3, par. [0026]).
As per claim 6, Fu in view of Dukes in further view of Rooney does not expressly teach a plurality of sprinklers, the plurality of sprinklers comprising a rotary sprinkler, a non-rotary sprinkler, or a precision-type sprinkler.
However Tran, in an analogous art of irrigation systems (pg. 1, par. [0004] and [0013]), teaches the missing limitation of a plurality of sprinklers, the plurality of sprinklers comprising a rotary sprinkler, a non-rotary sprinkler, or a precision-type sprinkler (pg. 1, par. [0012] and pg. 3, par. [0053]; i.e. [0012]; i.e. “master irrigation autonomous vehicle and a number of autonomous irrigation vehicles each moving similar to a bird in a flock and following the master irrigation vehicles in watering a large farm.” and [0053]: “… a frame, water valves attached to the frame to dispense water to crops in the field, cameras and position sensors to capture field data, and a processor to control movement of the frame to move around a field for irrigation.”) for the purpose of dispensing water to crops in a field (pg. 1, par. [0006]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes in further view of Rooney to include the addition of the limitation of a plurality of sprinklers, the plurality of sprinklers comprising a rotary sprinkler, a non-rotary sprinkler, or a precision-type sprinkler to advantageously enable growers to manage their fields for better yields and profitability (Tran: pgs. 1-2, par. [0023]).
As per claim 7, Fu in view of Dukes in further view of Rooney does not expressly teach the machine-learned model is further configured to generate the first fluid application target rate, the second fluid application target rate, and an operational start time based at least in part on a current moisture condition of the first area and the second area.
However Tran, in an analogous art of irrigation systems (pg. 1, par. [0004] and [0013]), teaches the missing limitation of the machine-learned model is further configured to generate the first fluid application target rate, the second fluid application target rate, and an operational start time based at least in part on a current moisture condition of the first area and the second area (pg. 15, par. [0193], pgs. 38-39, par. [0373], and pg. 40, par. [0383] and [0387]; i.e. [0193]: “ It measures moisture content of soil to lets the process know when to water, determines if plant is getting adequate light, and determines PH level in soil, depending on whether acidic or alkaline is suitable for the plants. The unit is lightweight and portable for home plants, garden, lawn and farm. Other in- or on-ground sensors can be deployed to detect crop conditions, weather data, and many other details, which can then be transmitted to decision analytics platforms via the Internet of Things (where computing devices embedded in everyday objects are connected to the Internet to enable analytics)”, [0373]: “… uses AI for improved irrigation scheduling and efficiency. While soil moisture data (from either sensors or models) have long been used as a scheduling aid, AI provides machine learning of how soil moisture responds to irrigation events in scenarios with different crops, soils, environmental conditions, etc. Tied to an irrigation control system, the AI machine can automatically implement control strategies that help minimize water usage, manage nutrient losses, or achieve more desirable or uniform soil moisture throughout the field.”, and [0387]: “… the engine accesses field information describing characteristics of the crop growth and generates a prediction model trained on crop growth information and mapping data and during live operation determines a set of farming operations that maximize crop productivity, for example the engine identifies one or more of: a type or variant of crop to plant if any, an intercrop to plant, a cover crop to plant, a portion of the first portion of land on which to plant a crop, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a portion of the first portion of land on which to apply a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a portion of the first portion of land on which to apply an agricultural chemical, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation if any, a date to apply irrigation, and a rate of application for irrigation.”) for the purpose of dispensing water to crops in a field (pg. 1, par. [0006]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes in further view of Rooney to include the addition of the machine-learned model is further configured to generate the first fluid application target rate, the second fluid application target rate, and an operational start time based at least in part on a current moisture condition of the first area and the second area to advantageously enable growers to manage their fields for better yields and profitability (Tran: pgs. 1-2, par. [0023]).
As per claim 8, Fu in view of Dukes in further view of Rooney does not expressly teach the first amount of applied fluid and the second amount of applied fluid is applied by the sprinkler at the operational start time without intervention or external instructions from an operator.
However Tran, in an analogous art of irrigation systems (pg. 1, par. [0004] and [0013]), teaches the missing limitation of the first amount of applied fluid and the second amount of applied fluid is applied by the sprinkler at the operational start time without intervention or external instructions from an operator (pg. 40, par. [0383]; i.e. [0373]: “… uses AI for improved irrigation scheduling and efficiency. While soil moisture data (from either sensors or models) have long been used as a scheduling aid, AI provides machine learning of how soil moisture responds to irrigation events in scenarios with different crops, soils, environmental conditions, etc. Tied to an irrigation control system, the AI machine can automatically implement control strategies that help minimize water usage, manage nutrient losses, or achieve more desirable or uniform soil moisture throughout the field.”) for the purpose of dispensing water to crops in a field (pg. 1, par. [0006]).
Therefore, it would be been obvious to a person of ordinary in the art before the
effective filing date of the claimed invention to modify the teaching of Fu in view of Dukes in further view of Rooney to include the addition of the first amount of applied fluid and the second amount of applied fluid is applied by the sprinkler at the operational start time without intervention or external instructions from an operator to advantageously enable growers to manage their fields for better yields and profitability (Tran: pgs. 1-2, par. [0023]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to irrigation systems.
U.S. Patent Publication No. 2020/0383284 A1 discloses an artificially intelligent irrigation system on a property may include an irrigation management server with information for the artificially intelligent irrigation system.
U.S. Patent Publication No. 2021/0073925 A1 discloses developing an irrigation plan.
U.S. Patent Publication No. 2022/0030784 A1 discloses a ponding monitoring and detection system monitors, detects, and predicts ponding in an irrigated field in essentially real-time.
U.S. Patent Publication No. 2022/0338429 A1 discloses a dual machine irrigation system.
U.S. Patent Publication No. 2023/0397551 A1 discloses a computing device and method for processing geospatial data associated with an irrigation system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER L NORTON whose telephone number is (571)272-3694. The examiner can normally be reached Monday - Friday 9:00 am - 5:30 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JENNIFER L NORTON/Primary Examiner, Art Unit 2117
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