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 .
Response to Arguments
Regarding the claim objections, Examiner has fully considered Applicant’s arguments and amendments. The claim amendments are sufficient to overcome the claim objections. Therefore, the claim objections are withdrawn.
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Specifically, claim 1, as amended, recites "controlling, using the at least one management demand signal, at least one control device of the at least one aquaculture farm." Controlling a control device is not reasonably characterized as a mental step. Rather, this limitation represents a physical, real-world action.,” Examiner respectfully asserts that the argued limitation is part of the additional elements for consideration under Step 2A, Prong 2. However, the present claims recite several abstract limitations for consideration under Step 2A, Prong 1. See the detailed rejection below.
Regarding Applicant’s assertion of “In particular, the present claims are directly comparable to Example 46 of the USPTO's Patent Eligibility Guidance (PEG). For example, Sample Claims 2 and 3 of Example 46 were determined to be eligible at least in part due to their recitation of physical outcomes (e.g., "taking corrective action") using information obtained via the judicial exception, which was considered a meaningful limitation that integrated the judicial exception into a practical application.,” and “Likewise, the present claims recite a particular, physical outcome ("controlling a control device")using information obtained via the alleged judicial exception (e.g., using the management demand signal).,” Examiner respectfully disagrees with Applicant’s assertions that the present claims are directed to statutory subject matter in view of Example 46. Claim 2 and 3 of Example 46 were deemed eligible because the claims are beyond merely generally linking. In particular, the October 2019 Patent Eligibility Guide states that “Limitation (d) in combination with the feed dispenser enables the control of appropriate farm equipment based on the automatic detection of grass tetany, which goes beyond merely automating the abstract idea.” In contrast, the present claims merely recite generic control of a “control device,” which is not control of “appropriate” equipment. Examiner respectfully asserts that the control of appropriate equipment integrated the judicial exception into a practical application because the control is performed in a particular way that is an “other meaningful limitation” that integrates the judicial exception into the overall livestock management scheme and accordingly practically applies the exception. Therefore, Examiner respectfully disagrees with Applicant’s assertions in view of Example 46.
Therefore, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Ouyang does not teach (i) at least one aerial parameter of use including a location of an aquaculture pond or an aquaculture farm being within a predefined distance to another aquaculture pond of a further aquaculture farm, nor (ii) a regional management parameter including a supposed or actual outbreak of a disease at the further aquaculture farm.,” Examiner respectfully asserts that Ouyang teaches the limitation of (i), which can be seen below; however, Ouyang does not explicitly teach limitation (ii). Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Kozachenok reference to cure the deficiencies of the prior art combination of the record. Accordingly, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103. See the detailed rejection below.
Therefore, the present claims are rejected under 35 USC 103.
Claim Objections
Claim 1 is objected to because of the following informalities: Examiner suggests amending the claim for the sake of antecedence by reciting “wherein the at least one regional management parameter refers to a actual outbreak of a disease at the aquaculture pond of [[the]]a further aquaculture farm.” Appropriate correction is required.
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 1-3, 5-10, 12-14, and 17-18 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-3, 5-10, 12-13, and 17-18 are directed to a method and claim 14 is directed to a system. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1 and 14 recite determining at least one management demand signal for the aquaculture pond, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites the limitations of “a) obtaining, at least one aerial parameter of use of at least one aquaculture pond of at least one aquaculture farm, wherein the at least one aerial parameter of use includes a location of the at least one aquaculture pond or the aquaculture farm is within at least one predefined distance to at least one further aquaculture pond of a further aquaculture farm; b) obtaining, at least one regional management parameter of at least one region regionally associated with the aquaculture farm, wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm; c) determining, at least one management demand signal for the aquaculture pond on the basis of the at least one aerial parameter of use and the regional management parameter; andand the aquaculture pond shares a feed water source with at least one further aquaculture pond.” Claim 14 recites the limitations of “obtain at least one aerial parameter of use of at least one aquaculture pond of at least one aquaculture farm, wherein the at least one aerial parameter of use includes a location of the at least one aquaculture pond or the aquaculture farm is within at least one predefined distance to at least one further aquaculture pond of a further aquaculture farm; obtain at least one regional management parameter of at least one region regionally associated with the aquaculture farm, wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm; determine at least one management demand signal for the aquaculture pond on the basis of the at least one aerial parameter of use and the regional management parameter.” These limitations, as drafted, but for the recitation of “by at least one computer,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “by at least one computer” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “by at least one computer” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2, 9-10, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 3, 5-8, and 12-13 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1 and 14 do not integrate the judicial exception into a practical application. Claim 1 is a computer-implemented method that recites the limitations, such as obtaining, as being performed “by at least one computer.” Claim 14 is a system comprising “at least one computer configured to” performing the limitations, such as obtaining, of the claim. Independent claims 1 and 14 further recite “controlling, using the at least one management demand signal, at least one control device of the at least one aquaculture farm.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2, 9-10, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 3 introduces the additional element of “further comprising d) providing, to the at least one control device the management demand signal.” Dependent claim 5 introduces the additional element of “further comprising f) informing a user, by at least one user device of the control device, about the management demand signal.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 6 introduces the additional element of “wherein the control device comprises at least one actuator device.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 7 introduces the additional element of “wherein the obtaining of the regional management parameter in step b) comprises at least one of the following: - measuring the regional management parameter by using at least one regional management signal sensor; - determining the regional management parameter from aerial sensor data; and - retrieving the regional management parameter from at least one electronic source.” Dependent claim 8 introduces the additional element of “further comprising g) obtaining, by the computer, at least one local sensor parameter from at least one local sensor device locally associated with the aquaculture farm wherein the management demand signal in step c) is determined by further taking into account the local sensor parameter.” These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 12 introduces the additional element of “A computer or computer network comprising at least one processor, wherein the processor is adapted to perform the method according to claim 1.” Dependent claim 13 introduces the additional element of “A non-transitory computer-readable storage medium having stored thereon computer-executable instructions for performing the method according to claim 1,when the computer-executable instructions are executed by at least one processor of a computer or computer network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not sufficient to prove integration into a practical application.
Step 2B: Claims 1 and 14 do not comprise anything significantly more than the judicial exception. Claim 1 is a computer-implemented method that recites the limitations, such as obtaining, as being performed “by at least one computer.” Claim 14 is a system comprising “at least one computer configured to” performing the limitations, such as obtaining, of the claim. Independent claims 1 and 14 further recite “controlling, using the at least one management demand signal, at least one control device of the at least one aquaculture farm.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, which is not anything significantly more than the judicial exception.
Dependent claims 2, 9-10, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 3 introduces the additional element of “further comprising d) providing, to the at least one control device the management demand signal.” Dependent claim 5 introduces the additional element of “further comprising f) informing a user, by at least one user device of the control device, about the management demand signal.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 6 introduces the additional element of “wherein the control device comprises at least one actuator device.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 7 introduces the additional element of “wherein the obtaining of the regional management parameter in step b) comprises at least one of the following: - measuring the regional management parameter by using at least one regional management signal sensor; - determining the regional management parameter from aerial sensor data; and - retrieving the regional management parameter from at least one electronic source.” Dependent claim 8 introduces the additional element of “further comprising g) obtaining, by the computer, at least one local sensor parameter from at least one local sensor device locally associated with the aquaculture farm wherein the management demand signal in step c) is determined by further taking into account the local sensor parameter.” These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 12 introduces the additional element of “A computer or computer network comprising at least one processor, wherein the processor is adapted to perform the method according to claim 1.” Dependent claim 13 introduces the additional element of “A non-transitory computer-readable storage medium having stored thereon computer-executable instructions for performing the method according to claim 1,when the computer-executable instructions are executed by at least one processor of a computer or computer network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-3, 5-10, 12-14, and 17-18 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5, 7-8, 10, 12-14, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang et al. (US 20210064034 A1) in view of Kozachenok et al. (US 20210329891 A1).
Regarding claim 1, Ouyang teaches a computer-implemented aquaculture farm management method (Fig. 10A-11C), comprising
a) obtaining, by at least one computer (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0105] teaches the sensor data is communicated from the HAUCS sensing platform to at least one remote computing device for processing; see also: Figs. 2-3, [0070-0071, 0094, 0107]), at least one aerial parameter of use of at least one aquaculture pond of at least one aquaculture farm ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm; see also: [0037-0040]),
wherein the at least one aerial parameter of use includes a location of the at least one aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm; see also: [0037-0040]);
b) obtaining, by the computer, at least one regional management parameter of at least one region regionally associated with the aquaculture farm ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, wherein [0104] teaches the HAUCS framework enables novel sensing schemes that cover extended spatial regions and provide more robust readings than traditional collection processes, wherein the HAUCS sensing platform can be adaptive to the environmental conditions based on weather changes on the farm, as well as in [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, barometers, wind speed sensors, and more; see also: [0031, 0083]),
c) determining, by the computer, at least one management demand signal for the aquaculture pond on the basis of the at least one aerial parameter of use and the regional management parameter ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, as well as in [0036] teaches the backend processing system can provide predictions of pond water quality, detecting upcoming compromised water quality, such as dissolved oxygen depletion, and mitigates a pond distress either automatically or in close collaboration with the human site managers, as well as in [0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, as well as in [0046] teaches the prediction model is trained using weather data and sensor data associated with all bodies of water in the aquaculture farm in order to change behavior in the HAUCs sensing platform to mitigate emergency situations and optimize the yield of farmed fish, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operations; see also: [0050, 0087-0088]); and
d) controlling, using the at least one management demand signal, at least one control device of the at least one aquaculture farm (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0107] teaches the sensor data is processed at the remote computing device using the machine learning based analytical engine, wherein the sensor data is used to determine whether a water distress condition exists or is predicted to occur at a given amount of time, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operation; see also: Figs. 2-3, [0050, 0070-0071, 0094]),
wherein the at least one aerial parameter of use is obtained from at least one of the following: a color of the aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm);
a turbulence generated within the aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that includes turbidity sensors, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm);
wherein the at least one aerial parameter of use further includes one or more of: the aquaculture pond is filled with water ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm, and wherein [0093] teaches sensor data is obtained for one or more bodies of water including weather and environmental data; see also: [0095]);
the aquaculture pond is aerated ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm, and wherein [0093] teaches sensor data is obtained for one or more bodies of water including weather and environmental data, wherein [0108] teaches a fixed instrument within the pond includes a fixed aerator; see also: [0095]).
However, Ouyang does not explicitly teach wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm.
From the same or similar field of endeavor, Kozachenok teaches wherein the at least one regional management parameter refers to a actual outbreak of a disease at the aquaculture pond of the further aquaculture farm ([0076] teaches receiving input data indicative of underwater conditions within or proximate to the marine enclosure, including image data and environmental data, in order to output a set of intrinsic operating parameters that is determined to provide an amount of light energy administration that is sufficient to kill a first species of parasites for its intended purposes in the first use case and under current prevailing conditions, wherein the dynamic sensor operating parameter reconfiguration of the system improves laser operations so that parasite control is effective across various conditions and species without requiring physical repositioning of the sensors, which increases yield and product quality, wherein [0035] teaches laser systems may be reconfigured to respond to commands received from a computer system to improve the results of aquaculture lice treatment operations, wherein the image sensors are used to monitor conditions in marine enclosures and identify parasites attached to fish within the enclosures, wherein [0022] teaches an underwater object parameter with respect to an individual fish encompasses various individualized data including detection of one or more parasites, wherein [0010-0011] teach aquaculture stock is often subject to risk of commercial damage from parasite infestations due to the confining of fish in marine enclosures at unnaturally high densities that make it easier for diseases to spread, wherein infected fish can suffer from lesions from parasites, wherein monitoring and treatments should be carried out to ensure that parasites do not cause stress or damage to fish stock, wherein outbreaks may include sea lice outbreaks; see also: [0018, 0084, 0098-0099]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Ouyang to incorporate the teachings of Kozachenok to include wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm. One would have been motivated to do so in order to provide an efficient manner for automated and dynamic reconfiguration of laser systems to improve the results of aquaculture lice treatment operations by monitoring conditions in marine enclosures using image sensors (Kozachenok, [0035]). By incorporating the teachings of Kozachenok, one would have been able to provide dynamic sensor operations so that parasite control is effective across various conditions and specifies by identifying underwater proximate conditions within the marine enclosure (Kozachenok, [0076]).
Regarding claim 2, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches wherein the management demand signal comprises control information on at least one of the following aquaculture pond management actions: an adjustment of aeration ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, as well as in [0036] teaches the backend processing system can provide predictions of pond water quality, detecting upcoming compromised water quality, such as dissolved oxygen depletion, and mitigates a pond distress either automatically or in close collaboration with the human site managers, as well as in [0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, as well as in [0046] teaches the prediction model is trained using weather data and sensor data associated with all bodies of water in the aquaculture farm in order to change behavior in the HAUCs sensing platform to mitigate emergency situations and optimize the yield of farmed fish, wherein [0109-0110] teach the HAUCs sensing platform can be deployed to one or more bodies of water in order to improve the water condition(s) based on a wireless signal sent from the HAUCs sensing platform with a command to modify operations, wherein [0043] teaches the UAAV can react to the body of water in distress by dispatching mobile emergency aerators to the body of water, as well as in [0049] teaches the computing device is provided to handle automatic emergency responses by the HAUCS by activating or deactivating mobile aerators; see also: [0059, 0061, 0066]).
Regarding claim 3, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches further comprising d) providing, to at least one control device the management demand signal (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0107] teaches the sensor data is processed at the remote computing device using the machine learning based analytical engine, wherein the sensor data is used to determine whether a water distress condition exists or is predicted to occur at a given amount of time, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operation; see also: Figs. 2-3, [0050, 0070-0071, 0094]).
Regarding claim 5, the combination of Ouyang and Kozachenok teaches all the limitations of claim 3 above.
Ouyang further teaches further comprising f) informing a user, by at least one user device of the control device, about the management demand signal (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0107] teaches the sensor data is processed at the remote computing device using the machine learning based analytical engine, wherein the sensor data is used to determine whether a water distress condition exists or is predicted to occur at a given amount of time, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operation, wherein [0109] teaches the human operator is caused to deploy a mobile instruction to one or more bodies of water including a mobile aerator; see also: Figs. 2-3, [0050, 0070-0071, 0094]).
Regarding claim 7, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches wherein the obtaining of the regional management parameter in step b) comprises at least one of the following:
- measuring the regional management parameter by using at least one regional management signal sensor ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, wherein [0104] teaches the HAUCS framework enables novel sensing schemes that cover extended spatial regions and provide more robust readings than traditional collection processes, wherein the HAUCS sensing platform can be adaptive to the environmental conditions based on weather changes on the farm, as well as in [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, barometers, wind speed sensors, and more; see also: [0031, 0083]);
- determining the regional management parameter from aerial sensor data ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, wherein [0104] teaches the HAUCS framework enables novel sensing schemes that cover extended spatial regions and provide more robust readings than traditional collection processes, wherein the HAUCS sensing platform can be adaptive to the environmental conditions based on weather changes on the farm, as well as in [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, barometers, wind speed sensors, and more; see also: [0031, 0083]); and
- retrieving the regional management parameter from at least one electronic source ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, wherein [0104] teaches the HAUCS framework enables novel sensing schemes that cover extended spatial regions and provide more robust readings than traditional collection processes, wherein the HAUCS sensing platform can be adaptive to the environmental conditions based on weather changes on the farm, as well as in [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, barometers, wind speed sensors, and more; see also: [0031, 0083]).
Regarding claim 8, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches the method further comprising g) obtaining, by the computer, at least one local sensor parameter from at least one local sensor device locally associated with the aquaculture farm wherein the management demand signal in step c) is determined by further taking into account the local sensor parameter ([0108] teaches the fixed instrument can be commanded to enable operations or disable operations, wherein the fixed instrument may be a pre-deployed sensor including a dissolved oxygen sensor, a barometer, a temperature sensor, a turbidity sensor, a pH sensor, a fungus detector, a biological demand sensor, and more, wherein Fig. 10D and [0111] teaches the additional sensor data is received by the remote computing device and is processed by the machine learning analytical engine in order to determine whether the water distress condition still exists or is still predicted to occur in a given amount of time, wherein [0112] teaches the mission plan can be modified by controlling operations of the fixed instrument to maintain water conditions, wherein the remote computing device can utilize this information to enable or disable operations; see also: [0109-0110]).
Regarding claim 10, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches wherein step b) further comprises determining, by the computer, a temporal development of the regional management parameter on the basis of a time series of regional management parameters obtained by the computer ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, as well as in [0036] teaches the backend processing system can provide predictions of pond water quality, detecting upcoming compromised water quality, such as dissolved oxygen depletion, and mitigates a pond distress either automatically or in close collaboration with the human site managers, as well as in [0046] teaches the prediction model is trained using weather data and sensor data associated with all bodies of water in the aquaculture farm in order to change behavior in the HAUCs sensing platform to mitigate emergency situations and optimize the yield of farmed fish, wherein [0038] teaches the HAUCS framework provides continuous monitoring, maintenance, and forecasting of next-generation fish farms by allowing farmers to stay several steps ahead of any potential catastrophic event, such as dissolved oxygen depletion, wherein the HAUCS platform can provide more accurate reporting of pond conditions by monitoring spatial and temporal pond variations, wherein [0076] teaches software applications installed on the computing device are generally operative to facilitate the training of the prediction model for a machine learning based analytical algorithm, wherein [0077] teaches the software applications may utilize a machine learning based data analytical engine, wherein the engine may employ a recurrent neural network that preserves long-range dependencies in time series prediction, wherein [0078] teaches the neural network can learn the D.O. variation trend of the bodies of water and predict missing values in order to reduce false negatives; see also: [0005, 0050, 0087-0088]),
wherein the determining of the management demand signal on the basis of the aerial parameter of use and the regional management parameter takes into account the temporal development of the regional management parameter ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, as well as in [0036] teaches the backend processing system can provide predictions of pond water quality, detecting upcoming compromised water quality, such as dissolved oxygen depletion, and mitigates a pond distress either automatically or in close collaboration with the human site managers, as well as in [0046] teaches the prediction model is trained using weather data and sensor data associated with all bodies of water in the aquaculture farm in order to change behavior in the HAUCs sensing platform to mitigate emergency situations and optimize the yield of farmed fish, wherein [0038] teaches the HAUCS framework provides continuous monitoring, maintenance, and forecasting of next-generation fish farms by allowing farmers to stay several steps ahead of any potential catastrophic event, such as dissolved oxygen depletion, wherein the HAUCS platform can provide more accurate reporting of pond conditions by monitoring spatial and temporal pond variations, wherein [0076] teaches software applications installed on the computing device are generally operative to facilitate the training of the prediction model for a machine learning based analytical algorithm, wherein [0077] teaches the software applications may utilize a machine learning based data analytical engine, wherein the engine may employ a recurrent neural network that preserves long-range dependencies in time series prediction, wherein [0078] teaches the neural network can learn the D.O. variation trend of the bodies of water and predict missing values in order to reduce false negatives; see also: [0005, 0050, 0087-0088]).
Regarding claim 11, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches A computer program comprising computer-executable instructions for performing the method according to claim 1 when the program is executed on a computer or computer network (Fig. 3 and [0075] teach software code in storing one or more sets of instructions configured to implement the methodologies described therein; see also: [0073-0074]).
Regarding claim 12, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
Ouyang further teaches A computer or computer network comprising at least one processor (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0105] teaches the sensor data is communicated from the HAUCS sensing platform to at least one remote computing device for processing, wherein Fig. 3 and [0074-0075] teach the computing device comprises a central processing unit that can execution instructions; see also: [0046, 0071-0073]), wherein the processor is adapted to perform the method according to claim 1.
Regarding claim 13, the combination of Ouyang and Kozachenok teaches all the limitations of claim 11 above.
Ouyang further teaches A non-transitory computer-readable storage medium having stored thereon computer-executable instructions for performing the method according to claim 1, when the computer-executable instructions are executed by at least one processor of a computer (Fig. 3 and [0075] teach the computing device including a computer-readable storage medium on which is stored a set of instructions; see also: [0073-0074]).
Regarding claim 14, Ouyang teaches an aquaculture farm management system for managing at least one aquaculture farm having at least one aquaculture pond, the aquaculture farm management system comprising (Figs. 1-3):
at least one computer configured to (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0105] teaches the sensor data is communicated from the HAUCS sensing platform to at least one remote computing device for processing, as well as in [0105-0108] teach the remote computing device receives the sensor data and processes it using the machine learned based analytical engine, wherein [0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist; see also: [0070-0071, 0094, 0107]):
obtain at least one aerial parameter of use of at least one aquaculture pond of at least one aquaculture farm (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0105] teaches the sensor data is communicated from the HAUCS sensing platform to at least one remote computing device for processing, as well as in [0105-0108] teach the remote computing device receives the sensor data and processes it using the machine learned based analytical engine, wherein [0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm; see also: Figs. 2-3, [0070-0071, 0094, 0107]),
wherein the at least one aerial parameter of use includes a location of the at least one aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, turbidity, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm; see also: [0037-0040]);
obtain at least one regional management parameter of at least one region regionally associated with the aquaculture farm (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0105] teaches the sensor data is communicated from the HAUCS sensing platform to at least one remote computing device for processing, as well as in [0105-0108] teach the remote computing device receives the sensor data and processes it using the machine learned based analytical engine, wherein [0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, wherein [0104] teaches the HAUCS framework enables novel sensing schemes that cover extended spatial regions and provide more robust readings than traditional collection processes, wherein the HAUCS sensing platform can be adaptive to the environmental conditions based on weather changes on the farm, as well as in [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, barometers, wind speed sensors, and more; see also: [0031, 0083]),
determine at least one management demand signal for the aquaculture pond on the basis of the at least one aerial parameter of use and the regional management parameter ([0082] teaches predicting the status of the body of water using the sensor data from the HAUCS sensing platforms and the other environmental data, wherein the environmental data includes temperature data, wind speed data, and wind direction data, which can be obtained from weather reports and weather stations, as well as in [0036] teaches the backend processing system can provide predictions of pond water quality, detecting upcoming compromised water quality, such as dissolved oxygen depletion, and mitigates a pond distress either automatically or in close collaboration with the human site managers, as well as in [0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, as well as in [0046] teaches the prediction model is trained using weather data and sensor data associated with all bodies of water in the aquaculture farm in order to change behavior in the HAUCs sensing platform to mitigate emergency situations and optimize the yield of farmed fish, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operations; see also: [0050, 0087-0088]); and
control, using the at least one management demand signal, at least one control device of the at least one aquaculture farm (Fig. 1 and [0059-0060] teach a computing device that exchanges data with the HAUCS sensing platform, wherein [0107] teaches the sensor data is processed at the remote computing device using the machine learning based analytical engine, wherein the sensor data is used to determine whether a water distress condition exists or is predicted to occur at a given amount of time, wherein [0108-0110] teach a wireless signal is sent from the remote computing device to the fixed instrument with a command to enable one or more operations thereof, disable one or more operations thereof, turn on system power, turn off system power, or more, wherein the fixed instrument may be a fixed aerator or a pre-deployed sensor in order to improve the water condition(s) based on a wireless signal sent from the remote computing device with a command to modify operation; see also: Figs. 2-3, [0050, 0070-0071, 0094]).
However, Ouyang does not explicitly teach wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm.
From the same or similar field of endeavor, Kozachenok teaches wherein the at least one regional management parameter refers to a actual outbreak of a disease at the aquaculture pond of the further aquaculture farm ([0076] teaches receiving input data indicative of underwater conditions within or proximate to the marine enclosure, including image data and environmental data, in order to output a set of intrinsic operating parameters that is determined to provide an amount of light energy administration that is sufficient to kill a first species of parasites for its intended purposes in the first use case and under current prevailing conditions, wherein the dynamic sensor operating parameter reconfiguration of the system improves laser operations so that parasite control is effective across various conditions and species without requiring physical repositioning of the sensors, which increases yield and product quality, wherein [0035] teaches laser systems may be reconfigured to respond to commands received from a computer system to improve the results of aquaculture lice treatment operations, wherein the image sensors are used to monitor conditions in marine enclosures and identify parasites attached to fish within the enclosures, wherein [0022] teaches an underwater object parameter with respect to an individual fish encompasses various individualized data including detection of one or more parasites, wherein [0010-0011] teach aquaculture stock is often subject to risk of commercial damage from parasite infestations due to the confining of fish in marine enclosures at unnaturally high densities that make it easier for diseases to spread, wherein infected fish can suffer from lesions from parasites, wherein monitoring and treatments should be carried out to ensure that parasites do not cause stress or damage to fish stock, wherein outbreaks may include sea lice outbreaks; see also: [0018, 0084, 0098-0099]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Ouyang to incorporate the teachings of Kozachenok to include wherein the at least one regional management parameter refers to a supposed or actual outbreak of a disease at the aquaculture pond of the further aquaculture farm. One would have been motivated to do so in order to provide an efficient manner for automated and dynamic reconfiguration of laser systems to improve the results of aquaculture lice treatment operations by monitoring conditions in marine enclosures using image sensors (Kozachenok, [0035]). By incorporating the teachings of Kozachenok, one would have been able to provide dynamic sensor operations so that parasite control is effective across various conditions and specifies by identifying underwater proximate conditions within the marine enclosure (Kozachenok, [0076]).
Regarding claim 17, the combination of Ouyang and Kozachenok teaches all the limitations of claim 11 above.
Ouyang further teaches wherein the aerial parameter of use is obtained from a color of the aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that include dissolved oxygen sensors, barometers, temperature sensors, humidity sensors, pH sensors, fungus detectors, parasite detectors, biological oxygen demand sensors, cameras, vibration sensors, colored dissolved organic matter sensors, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm).
Regarding claim 18, the combination of Ouyang and Kozachenok teaches all the limitations of claim 11 above.
Ouyang further teaches wherein the aerial parameter of use is obtained from a turbulence of the aquaculture pond ([0005] teaches utilizing a hybrid aerial underwater robotic system (HAUCS) robotics system sensing platform in order to travel to a first location in proximity to a body of water by air in order to collect first sensor data in order to determine whether a water distress condition exists or is predicted to exist, wherein [0045] teaches the UAAV has a plurality of sensors coupled to it that includes turbidity sensors, and more, wherein [0034] teaches in aquaculture fish farms, management of water quality, or dissolved oxygen (D.O), is critically important for successful operation, wherein [0036] teaches the HAUCS sensing platform can cover a subset of ponds within an aquaculture farm).
Claim(s) 6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang et al. (US 20210064034 A1) in view of Kozachenok et al. (US 20210329891 A1) in view of Zhuo et al. (US 20180181876 A1).
Regarding claim 6, the combination of Ouyang and Kozachenok teaches all the limitations of claim 3 above.
However, Ouyang fails to explicitly teach wherein the control device comprises at least one actuator device.
From the same or similar field of endeavor, Zhuo teaches wherein the control device comprises at least one actuator device (Fig. 1A and [0020] teach a plurality of actuators, wherein [0022] teaches the actuators can allow each device to perform some kind of action to affect its environment, wherein the devices can include one or more respective actuators that accept an input and performs its respective action in response thereto, wherein the actuators can include controllers to activate additional functionality, such as selectively toggle the power or operation of an alarm, camera, heating, cooling, or other functions, wherein [0041] teaches the devices include aquatic sensors and actuators capable of being used in a variety of aquatic resources and detecting a variety of aquatic conditions; see also: [0023, 0045, 0060, 0073, 0083]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ouyang and Kozachenok to incorporate the teachings of Zhuo to include wherein the control device comprises at least one actuator device. One would have been motivated to do so in order to improve the aquatic health of the aquatic resources based on anomalies in the water, geographic location, climate and other factors that correspond to the various levels of aquatic health (Zhuo, [0049]). By incorporating the teachings of Zhuo, one would have been able to effectively detect and manage one or more conditions within the aquatic resources by deploying sensors (Zhuo, [0042]).
Regarding claim 9, the combination of Ouyang and Kozachenok teaches all the limitations of claim 1 above.
However, Ouyang fails to explicitly teach wherein step b) further comprises comparing the regional management parameter with at least one reference parameter, wherein in step c) the management demand signal is determined depending on the comparison of the regional management parameter with the at least one reference parameter.
From the same or similar field of endeavor, Zhuo teaches wherein step b) further comprises comparing the regional management parameter with at least one reference parameter ([0047] teaches determining one or more levels of aquatic health for one or more aquatic resources based on outputs, such as calculated optimal conditions, received from the aquatic condition optimization system,, wherein the reported outputs can be logged and reporting events can be determined based on the receipt of outputs determined to be one or more undesirable aquatic conditions, wherein the aquatic resources management logic may include functionality for applying a threshold to the outputs to determine a state of health or a level of threat for one or more aquatic resources based on a comparison of an optimal condition and a current condition for one or more conditions in one or more aquatic resources, wherein the aquatic resource management logic may additionally trigger corrective actions based on the determine level of health for the aquatic resources, wherein [0104] teaches the level of health for one or more aquatic resources may be determined or predicted based on a comparison of the current conditions to the optimal conditions, wherein [0041] teaches determining one or more optimized conditions for an aquatic region including one or more bodies of water or aquatic resources, as well as in [0053] teaches collecting aquatic conditions to be selected as a basis in predicting optimal conditions for a particular aquatic region; see also: [0050, 0119, 0121]),
wherein in step c) the management demand signal is determined depending on the comparison of the regional management parameter with the at least one reference parameter ([0047] teaches determining one or more levels of aquatic health for one or more aquatic resources based on outputs, such as calculated optimal conditions, received from the aquatic condition optimization system,, wherein the reported outputs can be logged and reporting events can be determined based on the receipt of outputs determined to be one or more undesirable aquatic conditions, wherein the aquatic resources management logic may include functionality for applying a threshold to the outputs to determine a state of health or a level of threat for one or more aquatic resources based on a comparison of an optimal condition and a current condition for one or more conditions in one or more aquatic resources, wherein the aquatic resource management logic may additionally trigger corrective actions based on the determine level of health for the aquatic resources, wherein [0104] teaches the level of health for one or more aquatic resources may be determined or predicted based on a comparison of the current conditions to the optimal conditions, wherein [0041] teaches determining one or more optimized conditions for an aquatic region including one or more bodies of water or aquatic resources, as well as in [0053] teaches collecting aquatic conditions to be selected as a basis in predicting optimal conditions for a particular aquatic region; see also: [0050, 0119, 0121]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ouyang and Kozachenok to incorporate the teachings of Zhuo to include wherein step b) further comprises comparing the regional management parameter with at least one reference parameter, wherein in step c) the management demand signal is determined depending on the comparison of the regional management parameter with the at least one reference parameter. One would have been motivated to do so in order to improve the aquatic health of the aquatic resources based on anomalies in the water, geographic location, climate and other factors that correspond to the various levels of aquatic health (Zhuo, [0049]). By incorporating the teachings of Zhuo, one would have been able to effectively detect and manage one or more conditions within the aquatic resources by deploying sensors (Zhuo, [0042]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rishi et al. (US 20200170227 A1) discloses improving a feeding or farming strategy based on factors that previously led to disease outbreaks
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625