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 .
Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
Misnumbered claim 46 has been renumbered to 50. There appears to be 2 separate claims labeled as claim 46, and for interpretation purposes one is interpreted as 46, and the other 50. The total number of claims is 50.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “ to identify irrigation events within the field over a time interval by identifying time periods within the time interval during which the field temperature data is dampened relative to the ambient air temperature data”- is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements- “the first set of temperature sensors configured to measure a field temperature associated with the field; a second set of one or more temperature sensors configured to measure an ambient air temperature associated with the field; and a remote server comprising: a transceiver configured to receive field temperature data from the first set of temperature sensors and ambient air temperature data from the second set of temperature sensors;” which are simply insignificant extra solution activity of data gathering and transmission by outputting data and information, the claim also recites elements- : “and a controller configured” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Additionally the claim recites- “A remote irrigation system, comprising: a first set of one or more temperature sensors deployed in a field, at least one temperature sensor affixed at a height above a minimum waterline of the field and below a maximum waterline of the field” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “and a controller configured” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Additionally the claim recites- “A remote irrigation system, comprising: a first set of one or more temperature sensors deployed in a field, at least one temperature sensor affixed at a height above a minimum waterline of the field and below a maximum waterline of the field” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. Additionally the claim recites- “further comprising: a water pump configured to implement an irrigation event for the field” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. Additionally the claim recites- “wherein the controller is configured to, via the transceiver, modify an operating mode of the water pump by initiating one or more additional irrigation events for the field based on the identified irrigation events” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 1. Additionally the claim recites- “wherein the controller is configured to, via the transceiver, modify an operating mode of the water pump by bypassing one or more preplanned irrigation events for the field based on the identified irrigation events.” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “wherein a height of the minimum waterline is selected such that the at least one temperature sensor is not submerged when an irrigation event is not occurring”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “wherein a height of the maximum waterline is selected such that the at least one temperature sensor is submerged when an irrigation event is occurring”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “identify irrigation events within the filed”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Additionally the claim recites- “wherein the controller is configured to … by applying a machine-learned model to the field temperature data and the ambient air temperature data, the machine-learned model trained on historical field temperature data and associated historical ambient air temperature data, wherein a first set of portions of the historical field temperature data are flagged as corresponding to irrigation events and wherein a second set of portions of the historical field temperature data are flagged as not corresponding to irrigation events, which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “ identify one or more irrigation events from temperature measurements generated by the one or more remote temperature sensors; and detecting a set of irrigation events”- is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements- “accessing, by a central server, historic temperature measurements from one or more remote temperature sensors”, “accessing, by the central server, historic water pump activity from one or more remote water pumps located at the field” which are simply insignificant extra solution activity of data gathering and transmission by outputting data and information, the claim also recites elements- : “generating, by the central server, a training set of data based on the accessed historic temperature measurements and the accessed historic water pump activity; training, by the central server, a machine-learned model using the training set of data, the machine-learned model configured to”, “by applying the machine-learned model to target temperature measurements from the one or more remote temperature sensors” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Additionally the claim recites- “A method for managing an irrigation system of a field, comprising”, and “located above a minimum waterline and below a maximum waterline within the field” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “generating, by the central server, a training set of data based on the accessed historic temperature measurements and the accessed historic water pump activity; training, by the central server, a machine-learned model using the training set of data, the machine-learned model configured to”, “by applying the machine-learned model to target temperature measurements from the one or more remote temperature sensors” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Additionally the claim recites- “A method for managing an irrigation system of a field, comprising”, and “located above a minimum waterline and below a maximum waterline within the field” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “identifying one or more irrigation events based at least in part on a comparison of the first temperature data to the ambient air temperature during the first time period”, and “reading an ambient air temperature for the first time period” - is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example the language in the context of this claim encompasses that the user mentally could make a decision, observation, and calculation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements- “receiving first temperature data from a first temperature sensor”, “accessing, by the central server, historic water pump activity from one or more remote water pumps located at the field” which are simply insignificant extra solution activity of data gathering and transmission by outputting data and information. Additionally the claim recites- “the first temperature sensor being deployed in a crop field such that the first temperature sensor is affixed above a minimum waterline and below a maximum waterline for the field, the first temperature data including a plurality of temperature readings over a first time period” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “the first temperature sensor being deployed in a crop field such that the first temperature sensor is affixed above a minimum waterline and below a maximum waterline for the field, the first temperature data including a plurality of temperature readings over a first time period” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 17. Additionally the claim recites- “wherein the model is a regression model or a machine- learned model” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits the mental abstract idea from claim 17. Additionally the claim recites- “wherein the regression model is one of a linear regression, a RANSAC regression, a polynomial regression, a quantile regression, an elastic net regression, a random forest regression, or a gradient boosting regression” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 9-16, 18, and 21-50 are rejected under 35 U.S.C for similar reason as claim 1-8, 17, and 19-20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Nickerson et al. (US20190297797, herein Nickerson), in view of Tucker et al. (US20020117214, herein Tucker)
Regarding claim 1 Nickerson teaches, A remote irrigation system, comprising :a first set of one or more temperature sensors deployed in a field (Fig. 1B, [0034] one or more additional remote environment temperature sensors that are configured to detect the temperature of an environment remote from the flow sensor system 104, [0039] determine one or more conditions and/or aspects of the irrigation system), at least one temperature sensor affixed at a height above a minimum waterline of the field and below a maximum waterline of the field ([0019] the pipe nesting surface is configured to be positioned adjacent with an exterior surface of an irrigation pipe or the like that is configured to allow water to flow as part of an irrigation system. One or more pipe temperature sensors are secured with the housing proximate the pipe nesting surface, [0027] Some previous flow sensors included paddles or other mechanical systems that are positioned in the water flow within interior to the irrigation pipes) (i.e. the minimum waterline is the bottom of the pipe, and maximum waterline is the top of the pipe/housing), the first set of temperature sensors ([0019] pipe temperature sensors) configured to measure a field temperature associated with the field; a second set of one or more temperature sensors ([0019] environment temperature sensors) configured to measure an ambient air temperature associated with the field ([0019] configured to receive pipe temperature data from the pipe temperature sensor and environment temperature data from the environment temperature sensor); and a remote server comprising: a transceiver configured to receive field temperature data from the first set of temperature sensors and ambient air temperature data from the second set of temperature sensors ([0019] The sensor control circuit is communicatively coupled with the pipe temperature sensor and the environment temperature sensor, and configured to receive pipe temperature data from the pipe temperature sensor and environment temperature data from the environment temperature sensor. The sensor control circuit is further configured to detect an occurrence of a threshold temperature difference between the pipe temperature data and the environment temperature data, [0026] A notification may be issued by the flow sensor system 104 (e.g., an visual indicator, an audio output that can be heard by someone within a threshold distance, a wireless communication to remote device (e.g., a user's smart phone, laptop, a remote server, etc.), communication to the irrigation controller, or the like)) ; and a controller configured to identify irrigation events within the field over a time interval by identifying time periods within the time interval during which the field temperature …relative to the ambient air temperature data …(Fig. 9[0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).) .
Nickerson does not teach data is dampened
Tucker teaches data is dampened …([0021] The processor communicates with the pressure sensor and the electronically controlled flow regulating valve, with the sensor generating a PID feedback control signal. The processor is operative to regulate fluid flow through the valve to dampen out pressure oscillations in the line).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching of using irrigation flow sensors with Tucker’s teaching of dampened the sensor data. The combined teaching provides an expected result of using irrigation flow sensors and dampening the sensor data in the system. Therefore, one of ordinary skill in the art would be motivated to improve data accuracy.
Regarding claim 2, the combination of Nickerson and Tucker teach The remote irrigation system of claim 1, further comprising: a water pump configured to implement an irrigation event for the field (Nickerson, [0018] The irrigation controller is configured to control irrigation valves of the irrigation system in accordance with one or more defined irrigation schedule, [0026] The irrigation controller configured to receive flow sensor inputs typically is configured to evaluate the flow rates to determine whether the flow is within expected threshold ranges or exceeding one or more expected ranges. The flow sensor system 104 can be configured to communicate pulses or other such indications to effectively mimic other types of invasive flow sensors to provide pulses or other such indications that the irrigation controller is expecting (e.g., pulses corresponding to a flow within expected or normal range (e.g., 1 gpm), or pulses far in excess of a threshold (e.g., 100 gpm). In some embodiments, flow sensor systems 104 are configured to communicate information and/or sensor data to a “smart” irrigation controller 102 that is configured to cooperatively operate with the specific type of flow sensor system 104. The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions).
Tucker further teaches pump ([0071] regulating flow at a pump, [0101] pump 50 which draws water from an open sump 60, pumping the water through a pipeline, or line )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Tucker’s teaching of water being transmitted from a pump. The combined teaching provides an expected result of controlling the water flow in an irrigation system using a water pump. Therefore, one of ordinary skill in the art would be motivated to improve system efficiency by ensuring a steady water supply.
Regarding claim 3, The combination of Nickerson and Tucker teach The remote irrigation system of claim 2, wherein the controller is configured to, via the transceiver, modify an operating mode of the water … by initiating one or more additional irrigation events for the field based on the identified irrigation events (Nickerson, [0018] The irrigation controller is configured to control irrigation valves of the irrigation system in accordance with one or more defined irrigation schedule, [0026] The irrigation controller configured to receive flow sensor inputs typically is configured to evaluate the flow rates to determine whether the flow is within expected threshold ranges or exceeding one or more expected ranges. The flow sensor system 104 can be configured to communicate pulses or other such indications to effectively mimic other types of invasive flow sensors to provide pulses or other such indications that the irrigation controller is expecting (e.g., pulses corresponding to a flow within expected or normal range (e.g., 1 gpm), or pulses far in excess of a threshold (e.g., 100 gpm). In some embodiments, flow sensor systems 104 are configured to communicate information and/or sensor data to a “smart” irrigation controller 102 that is configured to cooperatively operate with the specific type of flow sensor system 104. The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0023] The irrigation controller 102 is configured to sense this change of state or current, and in response, the irrigation controller 102 takes one or more actions, such as temporarily halting the execution of one or more watering schedules and/or determines other appropriate actions).
Regarding claim 4, The combination of Nickerson and Tucker teach The remote irrigation system of claim 2, wherein the controller is configured to, via the transceiver, modify an operating mode of the water pump by bypassing one or more preplanned irrigation events for the field based on the identified irrigation events (Nickerson, [0018] The irrigation controller is configured to control irrigation valves of the irrigation system in accordance with one or more defined irrigation schedule, [0026] The irrigation controller configured to receive flow sensor inputs typically is configured to evaluate the flow rates to determine whether the flow is within expected threshold ranges or exceeding one or more expected ranges. The flow sensor system 104 can be configured to communicate pulses or other such indications to effectively mimic other types of invasive flow sensors to provide pulses or other such indications that the irrigation controller is expecting (e.g., pulses corresponding to a flow within expected or normal range (e.g., 1 gpm), or pulses far in excess of a threshold (e.g., 100 gpm). In some embodiments, flow sensor systems 104 are configured to communicate information and/or sensor data to a “smart” irrigation controller 102 that is configured to cooperatively operate with the specific type of flow sensor system 104. The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0023] The irrigation controller 102 is configured to sense this change of state or current, and in response, the irrigation controller 102 takes one or more actions, such as temporarily halting the execution of one or more watering schedules and/or determines other appropriate actions).
Regarding claim 5, the combination of Nickerson and Tucker teach The remote irrigation system of claim 1, wherein a height of the minimum waterline is selected such that the at least one temperature sensor is not submerged when an irrigation event is not occurring (Nickerson, [0019] the pipe nesting surface is configured to be positioned adjacent with an exterior surface of an irrigation pipe or the like that is configured to allow water to flow as part of an irrigation system. One or more pipe temperature sensors are secured with the housing proximate the pipe nesting surface)(i.e. the minimum waterline is the bottom of the pipe, and maximum waterline is the top of the pipe/housing).
Regarding claim 6, the combination of Nickerson and Tucker teach The remote irrigation system of claim 1, wherein a height of the maximum waterline is selected such that the at least one temperature sensor is submerged when an irrigation event is occurring (Nickerson, [0019] the pipe nesting surface is configured to be positioned adjacent with an exterior surface of an irrigation pipe or the like that is configured to allow water to flow as part of an irrigation system. One or more pipe temperature sensors are secured with the housing proximate the pipe nesting surface, [0027] Some previous flow sensors included paddles or other mechanical systems that are positioned in the water flow within interior to the irrigation pipes)
Regarding claim 7, the combination of Nickerson and Tucker teach The remote irrigation system of claim 1, wherein the controller is configured to identify irrigation events within the field by applying a machine-learned model to the field temperature data and the ambient air temperature data (Nickerson, [0056] the learning mode may be implemented at different times for different sensors, or multiple sensors may operate simultaneously. In some embodiments, the sensor control circuit 120 is configured to operate in the learn state and determine one or more predefined acoustic patterns while in the learn state as a function of detected acoustic data, temperature data and/or other sensor data while in the learn state. Additionally or alternatively, flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like), the machine-learned model trained on historical field temperature data and associated historical ambient air temperature data ([0073] The memory 1214 can store code, software, executables, scripts, data, patterns, thresholds, lists, programs, log or history data, and the like. While FIG. 12 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly, [0056] flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like, [0034] One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.)), wherein a first set of portions of the historical field temperature data are flagged as corresponding to irrigation events and wherein a second set of portions of the historical field temperature data are flagged as not corresponding to irrigation events ([0034] the sensors to detect the relevant conditions, [0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.)
Claim(s) 8-11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Nickerson et al. (US20190297797, herein Nickerson), in view of Ouammi et al. (US20210400885, herein Ouammi)
Regarding claim 8, Nickerson teaches A method for managing an irrigation system of a field (Fig. 1B, [0034] one or more additional remote environment temperature sensors that are configured to detect the temperature of an environment remote from the flow sensor system 104, [0039] determine one or more conditions and/or aspects of the irrigation system), comprising: accessing, by a central server, historic temperature measurements ([0073] The memory 1214 can store code, software, executables, scripts, data, patterns, thresholds, lists, programs, log or history data, and the like. While FIG. 12 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly, [0056] flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like, [0034] One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.)), from one or more remote temperature sensors located above a minimum waterline and below a maximum waterline within the field ([0019] the pipe nesting surface is configured to be positioned adjacent with an exterior surface of an irrigation pipe or the like that is configured to allow water to flow as part of an irrigation system. One or more pipe temperature sensors are secured with the housing proximate the pipe nesting surface)(i.e. the minimum waterline is the bottom of the pipe, and maximum waterline is the top of the pipe/housing); accessing, by the central server, historic water …activity from one or more remote water pumps located at the field; generating, by the central server, a training set of data based on the accessed historic temperature measurements and the accessed historic water pump activity; training, by the central server, a machine-learned model using the training set of data ([0073] The memory 1214 can store code, software, executables, scripts, data, patterns, thresholds, lists, programs, log or history data, and the like. While FIG. 12 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly, [0056] flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like, [0034] One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.)), the machine-learned model configured to identify one or more irrigation events from temperature measurements generated by the one or more remote temperature sensors; and detecting a set of irrigation events by applying the machine-learned model to target temperature measurements from the one or more remote temperature sensors ([0034] the sensors to detect the relevant conditions, [0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).)
Nickerson does not teach pump
Ouammi teaches pump ([0092] The reservoir may be used to balance the water flow pumped from the water source and intended for crop irrigation, [0042] The greenhouse 100 may be connected to a water pump)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Ouammi’s teaching of water being transmitted from a pump. The combined teaching provides an expected result of controlling the water flow in an irrigation system using a water pump. Therefore, one of ordinary skill in the art would be motivated to improve system efficiency by ensuring a steady water supply.
Regarding claim 9, the combination of Nickerson and Ouammi teach The method of claim 8, wherein the machine-learned model is further configured to predict characteristics for a set of future irrigation events, …to be used during the set of future irrigation events (Nickerson, [0040] compare the change in the acoustic data with a set of multiple predefined acoustic patterns, and determine an estimate flow rate of the water within the irrigation pipe based on the change in acoustic data being consistent with one of the predefined acoustic patterns of the set of the multiple predefined acoustic patterns. In other implementations, the change in acoustic data may be evaluated relative to a stabilized number (e.g., an average maximum and/or minimum volume, frequency or the like) or average sampling of predefined acoustic patterns, [0053] the flow sensor system communicates information that may represents different estimated rates of flow and/or be used to estimate rates of flow. The sensor control circuit 120, in some embodiments, in activating the flow notification can be configured to output one or more different output values based on an estimated flow rate. For example, one output value may be representative of an excessive water flow condition corresponding to a higher than normal amount of water flow, a different output value may be representative of a low water flow condition corresponding to a lower than normal amount of water flow, and/or other output values may indicate other estimated flow rates. The estimate flow rate may be determined as a function of a determined relationship between one or more detected acoustic patterns with one or more predefined acoustic patterns, determined relationship between detected temperature differences and one or more temperature difference threshold, or other such methods. ) .
Ouammi further teaches and to predict a total volume of water ([0092] The reservoir may be used to balance the water flow pumped from the water source and intended for crop irrigation. The reservoir may be designed to keep a constant water level defined by the water reference signal, by first irrigating when needed and then by storing the water when the energy is available, [0084] Since greenhouse tomatoes may need at least 0.37 meters squared (m2) of soil per plant with a total planted area of 80 m2, and taking into account that a tomato plant may need 2.7 liters (l) of water per day, the expected irrigation water load to be provided by the local pump was equal to 0.025 meters cubed (m3) in the first interval (15 min) of each hour. The dry running protection of the local water pump was also planned. Therefore, a protection system was utilized to help ensure a minimum level of 20 percent (%) of the reservoir capacity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Ouammi’s teaching of predicted a total water amount. The combined teaching provides an expected result of controlling the water flow in an irrigation system by predicting a total water amount. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy.
Regarding claim 10, the combination of Nickerson and Ouammi teach The method of claim 9,
Ouammi further teaches wherein the predicted total volume of water is determined based on an aggregation of a total time for which the one or more remote water pumps are predicted to be active and based on a determined flow rate of water through the one or more remote water pumps when the water pumps are active ([0092] The reservoir may be used to balance the water flow pumped from the water source and intended for crop irrigation. The reservoir may be designed to keep a constant water level defined by the water reference signal, by first irrigating when needed and then by storing the water when the energy is available, [0084] Since greenhouse tomatoes may need at least 0.37 meters squared (m2) of soil per plant with a total planted area of 80 m2, and taking into account that a tomato plant may need 2.7 liters (l) of water per day, the expected irrigation water load to be provided by the local pump was equal to 0.025 meters cubed (m3) in the first interval (15 min) of each hour. The dry running protection of the local water pump was also planned. Therefore, a protection system was utilized to help ensure a minimum level of 20 percent (%) of the reservoir capacity, [0027] FIG. 14 illustrates an example 4-day model predictive control of a water reservoir, according to some embodiments, [0042] thermal, electrical, and water loads representing actuating devices demand, such as the fogging system, the dehumidifier, the CO2 injector, the artificial lighting system, the pumps, and the heating and cooling system, may be present with the example illustrated in FIG. 1.).
Regarding claim 11, the combination of Nickerson and Ouammi teach The method of claim 8,
Ouammi further teaches wherein the machine-learned model is further configured to predict emissions for the field based at least in part on the identified irrigation events ([0003] The method may include acquiring new environmental data from one or more sensors after initiating the state of the greenhouse. The method may include updating one or more model predictions based on the acquired data, [0004] the state may be of at least one of: an energy storage unit, a water reservoir, a renewable energy power generation, an inside temperature of the greenhouse, a carbon-dioxide level within the greenhouse).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Ouammi’s teaching of predicted carbon dioxide levels. The combined teaching provides an expected result of controlling the water flow in an irrigation system by predicting greenhouse emissions. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy.
Regarding claim 13, the combination of Nickerson and Ouammi teach The method of claim 8, wherein the machine-learned model is further trained using historic ambient air temperature data for the field accessed from one or more air temperature sensors, and wherein the machine learned model is configured to identify irrigation events based further on ambient air temperature data (Nickerson, [0073] The memory 1214 can store code, software, executables, scripts, data, patterns, thresholds, lists, programs, log or history data, and the like. While FIG. 12 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly, [0056] flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like, [0034] One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).
Regarding claim 14, the combination of Nickerson and Ouammi teach The method of claim 8, further comprising measuring one or more ecosystem attributes at least in part by applying one or more ecosystem attribute models to one or more of the detected set of irrigation events (Nickerson, [0016] the sensors include an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor. In some applications the sensor control circuit is configured to identify, based on at least the acoustic data, a pattern in the acoustic data corresponding to one or more conditions. For example, the sensor control circuit may identify one of a low water flow condition and an excessive water flow condition, with the low water flow condition corresponds to a lower than normal amount of water flow, and the excessive water flow condition corresponds to a higher than normal amount of water flow, [0028] The detected data and/or measurements may include one or more of temperature data, temperature differences, acoustic or sound data, and/or other relevant information, [0056] the sensor control circuit 120 is configured to operate in the learn state and determine one or more predefined acoustic patterns while in the learn state as a function of detected acoustic data, temperature data and/or other sensor data while in the learn state. Additionally or alternatively, flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. ).
Regarding claim 15, the combination of Nickerson and Ouammi teach The method of claim 8, Ouammi further teaches further comprising measuring greenhouse gas emissions based at least in part on the detected set of irrigation events ([0003] The method may include acquiring new environmental data from one or more sensors after initiating the state of the greenhouse. The method may include updating one or more model predictions based on the acquired data, [0004] the state may be of at least one of: an energy storage unit, a water reservoir, a renewable energy power generation, an inside temperature of the greenhouse, a carbon-dioxide level within the greenhouse).
Regarding claim 16, the combination of Nickerson and Ouammi teach The method of claim 8, further comprising: accessing historic accelerometer data for one or more time points from one or more accelerometers deployed in the field; mapping the accelerometer data to the historic temperature measurements corresponding to the one or more time points; training the machine-learned model based additionally on the mapping of the accelerometer data to the historic temperature measurements; receiving target accelerometer data from the one or more accelerometers deployed in the field; and detecting the set of irrigation events by further applying the machine-learned model to the target accelerometer data (Nickerson, [0016] the sensors include an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor. In some applications the sensor control circuit is configured to identify, based on at least the acoustic data, a pattern in the acoustic data corresponding to one or more conditions. For example, the sensor control circuit may identify one of a low water flow condition and an excessive water flow condition, with the low water flow condition corresponds to a lower than normal amount of water flow, and the excessive water flow condition corresponds to a higher than normal amount of water flow, ([0073] The memory 1214 can store code, software, executables, scripts, data, patterns, thresholds, lists, programs, log or history data, and the like. While FIG. 12 illustrates the various components being coupled together via a bus, it is understood that the various components may actually be coupled to the control circuit and/or one or more other components directly, [0056] flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102. In other embodiments, the irrigation controller 102 is operated in the learn mode and sensor data provided by the flow sensor system 104 is used by the irrigation controller to obtain and/or define the state information, temperature patterns, and/or predefined acoustic patterns, thresholds, and the like, [0034] One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.)
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Nickerson et al. (US20190297797, herein Nickerson), in view of Ouammi et al. (US20210400885, herein Ouammi), and in further view of Guan et al. (US20220061236, herein Guan).
Regarding claim 12, the combination of Nickerson and Ouammi teach The method of claim 8, The combination of Nickerson and Ouammi do not teach wherein the machine-learned model comprises a neural network, and wherein training the machine-learned model comprises iteratively training the neural network until the neural network can predict irrigation events corresponding to historic water pump activity from the historic temperature measurements with an above-threshold accuracy.
Guan teaches the machine-learned model comprises a neural network, and wherein training the machine-learned model comprises iteratively training the neural network until the neural network can predict irrigation events corresponding to historic water pump activity from the historic temperature measurements with an above-threshold accuracy ([0181] A machine learning problem can be either classification (to predict categorical memberships) or regression (to predict numerical values), [0181] Traditional machine learning models including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), or Support Vector Machines (SVM) have been practiced to process multi-temporal satellite data to classify the crop cover, [0105] In particular, ubiquitous satellite-derived measurements will be used to constrain model simulation for each field parcel, which will enable the location-specific simulation to achieve high accuracy. Both greenhouse gas (GHG) emissions (carbon footprint) and water quantity/quality (water footprint) are explicitly simulated in the IMAPS modeling framework, making it an ideal platform to assess the sustainability and guide the BMP design from field to watershed/basin to continental scales, [0030] algorithm involves calculating thresholds based on descriptors of the geometric shape of time series of remotely sensed or environmental data, [0097] Historical planting information is incorporated into the prior-knowledge model to improve the performance, especially in the pre and early season when remote sensing images do not contain distinguishable crop signals, [0057] FIG. 20 shows the BlueBird Neural Network architecture for real-time classification of crop cover types, [0170] where It n was the irrigation particle n at time period t (mm/d); Imax was the maximum allowed irrigation amount (mm/d), usually determined by the capacity of pumping well (gallon per minute, gpm) and the field area (Sfield, acre) (Eq. 3); β was the parameter needed to be calibrated for irrigation ranges, [0285] The method may also include wherein the environmental variables include one or more such as: temperature, humidity, precipitation, and/or vapor pressure deficit. ).
Claim(s) 17-28, 30-50 are rejected under 35 U.S.C. 103 as being unpatentable over Nickerson et al. (US20190297797, herein Nickerson), in view of Guan et al. (US20220061236, herein Guan).
Regarding claim 17, Nickerson teaches A method, comprising: receiving first temperature data from a first temperature sensor (Fig. 1B, [0034] one or more additional remote environment temperature sensors that are configured to detect the temperature of an environment remote from the flow sensor system 104, [0039] determine one or more conditions and/or aspects of the irrigation system), the first temperature sensor being deployed in a crop field such that the first temperature sensor is affixed above a minimum waterline and below a maximum waterline for the field ([0019] the pipe nesting surface is configured to be positioned adjacent with an exterior surface of an irrigation pipe or the like that is configured to allow water to flow as part of an irrigation system. One or more pipe temperature sensors are secured with the housing proximate the pipe nesting surface)(i.e. the minimum waterline is the bottom of the pipe, and maximum waterline is the top of the pipe/housing, [0003] Many types of irrigation systems enable automated irrigation of plant life. With some plant life and/or in some geographic regions irrigating can be costly. The amount of water applied to the plant life can be critical. Accordingly, some systems utilize sensor data to aid in controlling the irrigation system and/or the quantity of water applied ), the first temperature data including a plurality of temperature readings over a first time period; reading an ambient air temperature for the first time period ([0019] configured to receive pipe temperature data from the pipe temperature sensor and environment temperature data from the environment temperature sensor); and identifying one or more irrigation events based at least in part on a comparison of the first temperature data to the ambient air temperature during the first time period (Fig. 9[0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).
Nickerson does not clearly teach irrigation events… time period
Guan teaches irrigation events… time period ([0169] Daily irrigation events with different amounts from random distribution with the given ranges were the particles of particle filtering (Eq. 2). The first particle with 0 mm was always set to represent no irrigation for the targeted day. All the particles with different irrigation amounts would be incorporated into the advanced agroecosystem model, ecosys, to get ET simulations for different particles, [0164] The whole process can perform as a closed-loop control system for each time period during the crop growing season).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Guan’s teaching of irrigation events over time periods. The combined teaching provides an expected result of controlling the water flow in an irrigation system monitoring irrigation events over time periods. Therefore, one of ordinary skill in the art would be motivated to improve system efficiency optimizing water usage.
Regarding claim 18, the combination of Nickerson and Guan teach The method of claim 17, wherein comparing the first temperature data to the ambient air temperature comprises: mapping the first temperature data to the ambient air temperature; and applying a model to the mapped data, the model configured to identify irrigation events based on the first temperature data and the ambient air temperature (Nickerson, Fig. 9 [0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).
Regarding claim 19, the combination of Nickerson and Guan teach The method of claim 18,
Guan further teaches wherein the model is a regression model or a machine- learned model ([0181] A machine learning problem can be either classification (to predict categorical memberships) or regression (to predict numerical values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Guan’s teaching using a regression model. The combined teaching provides an expected result of controlling the water flow in an irrigation system using a regression model. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy.
Regarding claim 20, the combination of Nickerson and Guan teach The method of claim 17,
Guan teaches wherein the regression model is one of a linear regression, a RANSAC regression, a polynomial regression, a quantile regression, an elastic net regression, a random forest regression, or a gradient boosting regression ([0181] A machine learning problem can be either classification (to predict categorical memberships) or regression (to predict numerical values), [0181] Traditional machine learning models including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), or Support Vector Machines (SVM) have been practiced to process multi-temporal satellite data to classify the crop cover).
Regarding claim 21, the combination of Nickerson and Guan teach The method of claim 17, wherein at least one irrigation event has an associated start time and end time, the method further comprising: reading a flow rate of water associated with the at least one irrigation event; determining, from the start time and end time (Nickerson, Fig. 9[0017] Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition, [0019] the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0034] sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc., [0041] Such a sequence of acoustic patterns indicates a start of irrigation relative to the pipe being monitored. The sensor control circuit 120 may be configured to discard or filter out acoustic data during such irrigation initiation, and/or filter out audio data a threshold period of time after the detection of the irrigation initiation sequence to acquire acoustic data corresponding to the flow of fluid during irrigation, which can be evaluated relative to one or more other acoustic patterns.). ,
Nickerson does not teach a total time of pump activity; and determining, based on the flow rate and the total time of pump activity, a total volume of water for irrigation.
Guan teaches a total time of pump activity; and determining, based on the flow rate and the total time of pump activity, a total volume of water for irrigation ([0169]-[0170] Finally, the irrigation amount could be estimated as the weighted average of all the particles with their associated weights (Eq. 6)… where It n was the irrigation particle n at time period t (mm/d); Imax was the maximum allowed irrigation amount (mm/d), usually determined by the capacity of pumping well (gallon per minute, gpm) and the field area (Sfield, acre) .
Regarding claim 22, the combination of Nickerson and Guan teach The method of claim 17, further comprising: determining, based on the first temperature data, a dry period for the field; and aggregating a total dry period for the field over the season (Nickerson, [0047] a lack of flow or no flow can be identified when the temperature of the pipe returns to within a minimal temperature difference from the environment. Similarly, a slow flow may be identified based on a failure of the temperature difference to return with a threshold after a threshold period of time. In some applications, the flow sensor system 104 may include one or more heating and/or cooling elements that when activated are configured to locally modify the temperature of the irrigation pipe 202 and/or water within the pipe, [0034] the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).).
Regarding claim 23, the combination of Nickerson and Guan teach The method of claim 22, where the dry period is determined by a series of temperatures not indicative of the one or more irrigation events (Nickerson, [0047] a lack of flow or no flow can be identified when the temperature of the pipe returns to within a minimal temperature difference from the environment. Similarly, a slow flow may be identified based on a failure of the temperature difference to return with a threshold after a threshold period of time. In some applications, the flow sensor system 104 may include one or more heating and/or cooling elements that when activated are configured to locally modify the temperature of the irrigation pipe 202 and/or water within the pipe).
Regarding claim 24, the combination of Nickerson and Guan teach The method of claim 17, wherein reading the ambient air temperature comprises reading second temperature data from a second temperature sensor (Nickerson, [0019] environment temperature sensors, [0019] one or more environment temperature sensors are secured with the housing proximate an exterior housing surface of the housing. The sensor control circuit is communicatively coupled with the pipe temperature sensor and the environment temperature sensor, and configured to receive pipe temperature data from the pipe temperature sensor and environment temperature data from the environment temperature sensor).
Regarding claim 25, the combination of Nickerson and Guan teach The method of claim 17, wherein reading ambient air temperature comprises reading a temperature model constructed by (Nickerson, Fig. 9):
Guan further teaches generating a time series representing an upper percentile of a second temperature sensor; and fitting a linear regression to the time series, where the time series of the upper percentile is plotted against a time series of the first temperature sensor ([0030] algorithm involves calculating thresholds based on descriptors of the geometric shape of time series of remotely sensed or environmental data, [0033] environmental variables include one or more such as: temperature, humidity, precipitation, and/or vapor pressure deficit, [0154] Real-time weather forecasts up to 7 days (including precipitation, air temperature, relative humidity, radiation, wind speed, and so on) can be generated and provided as model inputs (4) to simulate the forecasted ET, VPD, and soil moisture, [0053] FIGS. 16A-B show scatter plots of (a) monthly and (b) annual irrigation estimations and irrigation records from CON and SEQ across 76 site-years in Nebraska. Black dashed lines indicated the 1-to-1 relationship. The red and blue lines were the regression lines of two methods (red: CON and blue: SEQ) with the 95% confidence interval. The probability density functions in the top and right sides denoted the kernel density estimations of irrigation records and irrigation estimations )
Regarding claim 26, the combination of Nickerson and Guan teach The method of claim 25, and each of the plurality of temperature sensors is affixed above a minimum waterline and below a maximum waterline,
Guan further teaches wherein a regional temperature model is constructed by generating a time series representing the upper percentile of second temperature data, wherein the second temperature data comprise a plurality of temperature readings from a plurality of temperature sensors over the first time period, wherein the plurality of temperature sensors is deployed in a plurality of crop fields… and fitting a linear regression to the time series, where the time series of the upper percentile is plotted against a time series of the first temperature data ([0030] algorithm involves calculating thresholds based on descriptors of the geometric shape of time series of remotely sensed or environmental data, [0033] environmental variables include one or more such as: temperature, humidity, precipitation, and/or vapor pressure deficit, [0154] Real-time weather forecasts up to 7 days (including precipitation, air temperature, relative humidity, radiation, wind speed, and so on) can be generated and provided as model inputs (4) to simulate the forecasted ET, VPD, and soil moisture, [0053] FIGS. 16A-B show scatter plots of (a) monthly and (b) annual irrigation estimations and irrigation records from CON and SEQ across 76 site-years in Nebraska. Black dashed lines indicated the 1-to-1 relationship. The red and blue lines were the regression lines of two methods (red: CON and blue: SEQ) with the 95% confidence interval. The probability density functions in the top and right sides denoted the kernel density estimations of irrigation records and irrigation estimations ).
Regarding claim 27, the combination of Nickerson and Guan teach The method of claim 17, wherein at least one irrigation event is identified by a presence of a temperature deviation exceeding a minimum period of time and a minimum threshold (Nickerson, [0020] The sensor control circuit is further configured to detect an occurrence of a threshold temperature difference between the pipe temperature data and the environment temperature data. Again, the flow indicator output is communicatively coupled with the sensor control circuit and configured to further couple with the separate irrigation controller, which is configured to control irrigation valves of the irrigation system in accordance with the defined irrigation schedule. In some embodiments, the sensor control circuit is further configured to activate a flow notification from the flow indicator output based on the detected occurrence of the threshold temperature difference between the pipe temperature data and the environment temperature data, [0047] a lack of flow or no flow can be identified when the temperature of the pipe returns to within a minimal temperature difference from the environment. Similarly, a slow flow may be identified based on a failure of the temperature difference to return with a threshold after a threshold period of time).
Regarding claim 28, the combination of Nickerson and Guan teach The method of claim 17, wherein the first sensor is submerged during at least one irrigation event (Nickerson, [0027] Some previous flow sensors included paddles or other mechanical systems that are positioned in the water flow within interior to the irrigation pipes).
Regarding claim 30, the combination of Nickerson and Guan teach The method of claim 17, wherein a second temperature sensor is deployed proximate to the first temperature sensor and above the maximum water level (Nickerson, Fig. 6, [0017] The sensors in some embodiments may additionally or alternatively include temperature sensors. Accordingly, in some implementations a non-invasive water flow sensor may include a pipe temperature sensor secured with the housing proximate the pipe nesting surface. An environment temperature sensor may further be secured with the housing proximate an exterior housing surface of the housing. The sensor control circuit communicatively couples with the pipe temperature sensor and the environment temperature sensor and configured to receive pipe temperature data from the pipe temperature sensor and environment temperature data from the environment temperature sensor. Based on a temperature difference between the pipe temperature data and the environment temperature data, the sensor control circuit can be configured to identify a threshold temperature difference corresponding to at least one of the low water flow condition and the excessive water flow condition) .
Regarding claim 31, the combination of Nickerson and Guan teach The method of claim 30, wherein the first sensor and the second sensor are placed on a same vertical axis within a threshold distance of an outlet of the water pump, where the first sensor is below the second sensor (Nickerson, Fig. 6).
Regarding claim 32, the combination of Nickerson and Guan teach The method of claim 31, wherein the first sensor is submerged when the water pump is active, and the second sensor is not submerged when the water pump is active (Nickerson, [0027] Some previous flow sensors included paddles or other mechanical systems that are positioned in the water flow within interior to the irrigation pipes). .
Regarding claim 33, the combination of Nickerson and Guan teach The method of claim 17, further comprising: accessing accelerometer data for one or more time points from one or more accelerometers deployed in the crop field; mapping the accelerometer data corresponding to time points within the first time period to ambient air temperature data; and identifying one or more irrigation events based additionally on the accelerometer data mapped to the ambient air temperature data (Nickerson, [0016] the sensors include an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor. In some applications the sensor control circuit is configured to identify, based on at least the acoustic data, a pattern in the acoustic data corresponding to one or more conditions. For example, the sensor control circuit may identify one of a low water flow condition and an excessive water flow condition, with the low water flow condition corresponds to a lower than normal amount of water flow, and the excessive water flow condition corresponds to a higher than normal amount of water flow).
Regarding claim 34, the combination of Nickerson and Guan teach The method of claim 17, further comprising using a set of accelerometer data to identify at least one irrigation event by: receiving the set of accelerometer data from an accelerometer deployed in the crop field; processing the set of accelerometer data to identify one or more irrigation events; and comparing the identified one or more irrigation events from the accelerometer data to the at least one irrigation event determined from the first temperature data (Nickerson, [0016] an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor. In some applications the sensor control circuit is configured to identify, based on at least the acoustic data, a pattern in the acoustic data corresponding to one or more conditions. For example, the sensor control circuit may identify one of a low water flow condition and an excessive water flow condition, with the low water flow condition corresponds to a lower than normal amount of water flow, and the excessive water flow condition corresponds to a higher than normal amount of water flow, [0021] The flow sensor system 104, in some embodiments, determines an indication that a relationship exists between a threshold or other criteria (e.g., temperature change or difference threshold, acoustic threshold, correlation with a predefined acoustic pattern, etc.) and measurement data (e.g., acoustic data, temperature data, etc.), and in the event that a threshold relationship is identified, the flow indicator output can be activated.).
Regarding claim 35, the combination of Nickerson and Guan teach The method of claim 34, wherein the set of accelerometer data and the first temperature data are associated with a same water…(Nickerson, Fig. 3-6, [0034] For example, in some embodiments, the flow sensor system includes one or more temperature sensors 314 and 315 in addition to or alternatively to one or more acoustic sensors).
Nickerson does not teach pump
Guan teaches pump ([0170] determined by the capacity of pumping well (gallon per minute, gpm))
Regarding claim 36, the combination of Nickerson and Guan teach The method of claim 35, further comprising generating a time series of irrigation events wherein each irrigation event was identified from both the accelerometer data and the first temperature data (Nickerson, Fig. 8-9, [0041] The sensor control circuit 120 may be configured to discard or filter out acoustic data during such irrigation initiation, and/or filter out audio data a threshold period of time after the detection of the irrigation initiation sequence to acquire acoustic data corresponding to the flow of fluid during irrigation, which can be evaluated relative to one or more other acoustic patterns, [0039] Predefined acoustic patterns can be used that correspond to known water flow sounds 806, 808-809, other known sounds may be determined that correspond to different aspects of water flow within the irrigation pipes, irrigation events (e.g., activation, shut-off, pressure lease, etc.), and the conditions of the irrigation pipes and system, [0056] As described above, in some embodiments, the flow sensor system 104 and/or irrigation controller 102 can operate in one or more learning modes to obtain sensor data and/or determine information corresponding to one or more states of flow within an irrigation pipe with which the sensor flow system is monitoring, which can be used for example in defining predefined thresholds, durations, and the like ).
Regarding claim 37, the combination of Nickerson and Guan teach The method of claim 35, further comprising generating a time series of irrigation events wherein each irrigation event was identified from at least one of the accelerometer data or the first temperature data (Nickerson, [0034] This remote environment temperature data may be compared to the environment temperature proximate the flow sensor system, and/or the environment temperature proximate the flow sensor system may be mathematically cooperated with the remote environment temperature data to provide a nominal environmental temperature and/or an average environment temperature, which may take into account variations in environment temperature proximate the flow sensor system. One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.).
Regarding claim 38, the combination of Nickerson and Guan teach The method of claim 34, wherein the first temperature sensor and accelerometer are attached to the same substrate (Nickerson, Fig. 4, [0016] the sensors include an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor, [0033] The acoustic sensor 310, in some implementations, includes one more microphones, surface acoustic wave sensors, hydrophones, vibration sensors, other such sound/audio sensors, or combination of two or more of such sensors, and which may include one or more filters, other limited systems, bandpass filtered, and/or other such signal processing).
Regarding claim 39, the combination of Nickerson and Guan teach The method of claim 38, where the substrate comprises a flexible region located between a portion of the substrate that is anchored and a vibration sensor (Nickerson, Fig. 3-6, [0033] The acoustic sensor 310, in some implementations, includes one more microphones, surface acoustic wave sensors, hydrophones, vibration sensors, other such sound/audio sensors, or combination of two or more of such sensors, and which may include one or more filters, other limited systems, bandpass filtered, and/or other such signal processing, [0034] In some implementations, one or more passages (e.g., curves, u-bends, etc.), screens and/or other such structures are formed in and/or cooperated with the housing to protect the acoustic sensor, pipe temperature sensor, environment temperature sensors, and/or other such sensors, while enabling the sensors to detect the relevant conditions).
Regarding claim 40, the combination of Nickerson and Guan teach The method of claim 17, wherein at least one irrigation event during the first time period is a time series (Nickerson, [0044] For example, the temperature of the water may increase as the temperature of the soil increases during the day and decrease as the temperature of the soil decreases in the evening and night. Water that is allowed to flow through irrigation pipes typically has a temperature that is different than the temperature of the environment in which the pipe is located (e.g., buried in the soil) because the temperature of the source of water is different than the environment where temperature is measured, [0039] Predefined acoustic patterns can be used that correspond to known water flow sounds 806, 808-809, other known sounds may be determined that correspond to different aspects of water flow within the irrigation pipes, irrigation events (e.g., activation, shut-off, pressure lease, etc.), and the conditions of the irrigation pipes and system. These predefined acoustic patterns can be used in evaluating the acoustic data obtained by the one or more acoustic sensors 310 to determine one or more conditions and/or aspects of the irrigation system.).
Guan further teaches of daily irrigation activity ([0169] Daily irrigation events with different amounts from random distribution with the given ranges were the particles of particle filtering)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Guan’s teaching using daily irrigation. The combined teaching provides an expected result of controlling the water flow in an irrigation system monitoring daily irrigation activity. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy.
Regarding claim 41, the combination of Nickerson and Guan teach The method of claim 17,
Guan further teaches wherein a water volume is estimated by a duration of a period in which a pump is turned on at a known flow rate of water through the pump ([0169]-[0170] the irrigation amount could be estimated as the weighted average of all the particles with their associated weights (Eq. 6)… where It n was the irrigation particle n at time period t (mm/d); Imax was the maximum allowed irrigation amount (mm/d), usually determined by the capacity of pumping well (gallon per minute, gpm) and the field area (Sfield, acre)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Guan’s teaching estimating the amount of water. The combined teaching provides an expected result of controlling the water flow in an irrigation system estimating the amount of water. Therefore, one of ordinary skill in the art would be motivated to improve system accuracy.
Regarding claim 42, the combination of Nickerson and Guan teach The method of claim 41, Guan further teaches wherein a field level water balance model is applied to the time series of daily irrigation activity ([0169] Daily irrigation events with different amounts from random distribution with the given ranges were the particles of particle filtering (Eq. 2). The first particle with 0 mm was always set to represent no irrigation for the targeted day, [0215] the STAIR algorithm provides the generation of daily high resolution images that can be used to update model prediction immediately. The real-time optical model is built in a way that utilizes the daily continuous STAIR data to generate daily crop cover prediction without retraining the model (FIG. 25).).
Regarding claim 43, the combination of Nickerson and Guan teach The method of claim 17, further comprising: initiating an irrigation event based on the identified irrigation events (Nickerson, [0039] Predefined acoustic patterns can be used that correspond to known water flow sounds 806, 808-809, other known sounds may be determined that correspond to different aspects of water flow within the irrigation pipes, irrigation events (e.g., activation, shut-off, pressure lease, etc.), and the conditions of the irrigation pipes and system. These predefined acoustic patterns can be used in evaluating the acoustic data obtained by the one or more acoustic sensors 310 to determine one or more conditions and/or aspects of the irrigation system, [0026] The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions.).
Regarding claim 44, the combination of Nickerson and Guan teach The method of claim 43, wherein the irrigation event is initiated in response to determining that the identified irrigation events are not sufficient to irrigate the crop field (Nickerson, [0026] The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0055] can allow the irrigation controller 102 to close that upstream valve in the event of an error condition).
Regarding claim 45, the combination of Nickerson and Guan teach The method of claim 17, further comprising: modifying a planned irrigation event based on the identified irrigation events (Nickerson, [0026] The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0055] can allow the irrigation controller 102 to close that upstream valve in the event of an error condition, [0024] The irrigation controller may take one or more actions, such as temporarily halting execution of one or more watering schedules until a subsequent resume data signal is sent. ).
Regarding claim 46, the combination of Nickerson and Guan teach The method of claim 45, wherein modifying the planned irrigation event comprises at least one of: increasing a length of the planned irrigation event, reducing the length of the planned irrigation event, increasing a flow of the planned irrigation event, decreasing the flow of the planned irrigation event, modifying a start time of the planned irrigation event, modifying an end time of the planned irrigation event, and canceling the planned irrigation event (Nickerson, [0026] The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0055] can allow the irrigation controller 102 to close that upstream valve in the event of an error condition, [0024] The irrigation controller may take one or more actions, such as temporarily halting execution of one or more watering schedules until a subsequent resume data signal is sent. ). .
Regarding claim 50, the combination of Nickerson and Guan teach The method of claim 45, wherein the planned irrigation event is modified in response to determining that the planned irrigation event is likely to cause one or more ecosystem attributes of the crop field to exceed a predefined threshold (Nickerson, [0026] The irrigation controller can be configured to receive this information and/or data, evaluation and/or process the data and/or information, and make one or more determinations regarding adjustments relative to one or more zones, interruption of irrigation, interruption of one or more zones, communication with the user, communication with a central irrigation control system, and/or other such actions, [0055] can allow the irrigation controller 102 to close that upstream valve in the event of an error condition, [0024] The irrigation controller may take one or more actions, such as temporarily halting execution of one or more watering schedules until a subsequent resume data signal is sent. ).
Regarding claim 47, the combination of Nickerson and Guan teach The method of claim 46,
Guan teaches wherein the predefined threshold is a threshold quantity of greenhouse gas emissions ([0013] Both greenhouse gas (GHG) emissions (carbon footprint) and water quantity/quality (water footprint) are explicitly simulated in the MAPS modeling framework, making it an ideal platform to assess the sustainability and guide the BMP design from field to watershed/basin to continental scales, [0043] FIG. 6 shows changes of soil organic carbon (SOC) and nitrous dioxide emission (N2O) under different management practices over the Spoon River watershed) .
Regarding claim 48, the combination of Nickerson and Guan teach The method of claim 17, further comprising measuring one or more ecosystem attributes at least in part by applying one or more ecosystem attribute models to one or more identified irrigation events (Nickerson, [0016] the sensors include an acoustic sensor coupled to the housing proximate the pipe nesting surface and configured to receive acoustic data. The sensor control circuit is communicatively coupled with the acoustic sensor and configured to receive acoustic data from the acoustic sensor. In some applications the sensor control circuit is configured to identify, based on at least the acoustic data, a pattern in the acoustic data corresponding to one or more conditions. For example, the sensor control circuit may identify one of a low water flow condition and an excessive water flow condition, with the low water flow condition corresponds to a lower than normal amount of water flow, and the excessive water flow condition corresponds to a higher than normal amount of water flow, [0028] The detected data and/or measurements may include one or more of temperature data, temperature differences, acoustic or sound data, and/or other relevant information, [0056] the sensor control circuit 120 is configured to operate in the learn state and determine one or more predefined acoustic patterns while in the learn state as a function of detected acoustic data, temperature data and/or other sensor data while in the learn state. Additionally or alternatively, flow state information, temperature patterns, and/or predefined acoustic patterns can be communicated to the flow sensor system 104 and/or the irrigation controller 102.).
Regarding claim 49, the combination of Nickerson and Guan teach The method of claim 17, wherein the ambient air temperature is determined by accessing ambient air temperature data from a set of sensors located at one or more locations within a threshold proximity to the crop field and combining the accessed ambient air temperature data (Nickerson, [0063] environment temperature data can additionally be received from one or more environment temperature sensors 315 secured proximate one of one or more exterior housing surfaces 308 of the housing. The temperature data can be evaluated to detect an occurrence of a threshold temperature difference 922 between the pipe temperature data and the environment temperature data, and a flow notification can be activated from the flow indicator output 110 based on the detected threshold temperature difference occurring between the pipe temperature data 902 and the environment temperature data 904, [0034] Some embodiments may further include one or more additional remote environment temperature sensors that are configured to detect the temperature of an environment remote from the flow sensor system 104. This remote environment temperature data may be compared to the environment temperature proximate the flow sensor system, and/or the environment temperature proximate the flow sensor system may be mathematically cooperated with the remote environment temperature data to provide a nominal environmental temperature and/or an average environment temperature, which may take into account variations in environment temperature proximate the flow sensor system. One or more tables may be stored in the flow sensor system for use in evaluating the environmental temperature data, the remote temperature data, and/or some combination thereof. In some instances, for example, the table defines relevant temperature variations over time (e.g., yearly) relative to general locations (e.g., cites, counties, zip codes, regions, etc.), [0026] In some implementations, the irrigation controller 102 may be unaware that irrigation has been interrupted. A notification may be issued by the flow sensor system 104 (e.g., an visual indicator, an audio output that can be heard by someone within a threshold distance, a wireless communication to remote device (e.g., a user's smart phone, laptop, a remote server, etc.), communication to the irrigation controller, or the like).) .
Claim(s) 29 is rejected under 35 U.S.C. 103 as being unpatentable over Nickerson et al. (US20190297797, herein Nickerson), in view of Guan et al. (US20220061236, herein Guan), in further view of Humphrey et al. (US20210063024A1, herein Humphrey).
Regarding claim 29, the combination of Nickerson and Guan teach The method of claim 17,
The combination of Nickerson and Guan teach wherein the first temperature sensor is deployed within a threshold distance of an outlet of a water pump in the field.
Humphrey teaches wherein the first temperature sensor is deployed within a threshold distance of an outlet of a water pump in the field (Fig. 1, The system includes an output temperature sensor disposed downstream of the heat exchanger outlet).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nickerson’s teaching controlling the water flow in an irrigation system with Humphrey’s teaching of a temperature sensor downstream from an outlet. The combined teaching provides an expected result of controlling the water flow in an irrigation system with a temperature sensor downstream from an outlet. Therefore, one of ordinary skill in the art would be motivated safely monitoring the system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Palmer (US20170316345) discloses machine learning aggregation maximizing the irrigation efficiency of the water pump based on predicted temperature.
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/YVONNE T FOLLANSBEE/
Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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