DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to the application filed on 1/31/2025.
Claims 1-20 are pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2024-0016055, filed on 2/1/2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “easy” in claims 6 and 16 is a relative term which renders the claim indefinite. The term “easy” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 6 and 16 state “selects one or more factors that are easy to collect from the livestock house environment data, generates one or more deep learning models using the selected factors, and repeatedly trains and updates each generated deep learning model to generate one or more carbon emission measurement models.” Paragraphs [0010], [0020], [0069], [0072], and [0109] are the only paragraphs that mention selecting factors that are “easy” to collect. However, nothing in the claims or specification provide any criteria for what would or would not qualify as “easy.” What is considered easy to one person may not be easy to another. Therefore, the term is subjective and renders the claim indefinite. The examiner recommends amending the claim to remove any indication about the ease of collecting the factors.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-11 are directed to a method. Claims 12-20 are directed to a server. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
Claims 1 and 12 recite virtually identical limitations. Claim 1 will be held as representative. Each claims additional elements will be addressed individually. The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claims 1 and 12:
collecting livestock house environment data from one or more twin livestock houses; and selecting one or more factors from the livestock house environment data and generating a plurality of carbon emission measurement models.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
Claims 2 and 13:
wherein the livestock house environment data includes external environment information including at least one of factors such as an external temperature, an external humidity, a wind speed, an atmospheric pressure, or a latitude, or combination thereof, and internal environment information including at least one of factors such as a manure temperature, a manure pH, an oxygen content in manure, an internal temperature, an internal humidity, an amount of methane, an amount of carbon dioxide, an amount of ammonia, a number of livestock, or a weight of livestock, or combination thereof.
Claims 4 and 14:
wherein, in the generating of the plurality of carbon emission measurement models, generates one or more regression models using the livestock house environment data of each twin livestock house, and repeatedly verifies and modifies the generated regression model to generate one or more carbon emission measurement models.
Claims 5 and 15:
wherein, in the generating of the plurality of carbon emission measurement models, analyzes a correlation between the carbon emissions and each factor included in the livestock house environment data of each twin livestock house, selects one or more factors on the basis of the analyzed correlation, and generates the one or more regression models using the selected factors.
Claims 6 and 16:
wherein, in the generating of the plurality of carbon emission measurement models, selects one or more factors that are easy to collect from the livestock house environment data, generates one or more models using the selected factors, and repeatedly trains and updates each generated model to generate one or more carbon emission measurement models.
Claims 7 and 17:
wherein, in the generating of the plurality of carbon emission measurement models, selects a factor object from a carbon cycle object model, generates one or more models using the selected factor object, and trains and updates each generated model using the livestock house environment data to generate one or more carbon emission measurement models.
Claims 8 and 18:
further comprising, after the generating of the plurality of carbon emission measurement models: when a user requests a carbon emission measurement service, providing, a list of the plurality of carbon emission measurement models to the user; and measuring carbon emissions from a corresponding livestock house on the basis of a carbon emission measurement model selected by the user.
Claims 9 and 19:
wherein, in the measuring of the carbon emissions from the livestock house, generates an input value of the selected carbon emission measurement model in conjunction with the livestock house, and inputs the input value into the selected carbon emission measurement model to measure the carbon emissions from the livestock house.
Claims 10 and 20:
wherein, in the measuring of the carbon emissions from the livestock house, receives an input value of the selected carbon emission measurement model from the user, and inputs the input value into the selected carbon emission measurement model to measure the carbon emissions from the livestock house.
Claim 11:
provide information on operating results; detect environment information of the livestock house; and, and transmits the livestock house environment data including at least one of control information used for operating the environmental facilities, the information on the operating results, or he environment information of the livestock house to a carbon emission measurement service entity, or combination thereof.
The claims provide a manner of collecting livestock house environmental data, selecting one or more factors form the livestock environmental data, and generating carbon emission measurement models. But for the inclusion of generic computing components (i.e. processor), the claims can be performed in the human mind or with pen and paper. Thus, the claims fall under the mental process grouping of abstract idea.
Additionally, the claims are directed to using data to develop models which falls under mathematical concepts.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: processor (claims 1, 3-10, 12, 14-20); wherein, in the collecting of the livestock house environment data, the processor collects the livestock house environment data using a digital twin model (claim 3); deep learning models (claims 6, 7, 16, 17); user terminal (claims 8, 10, 18, 20); server comprising: a communication module configured to communicate with one or more twin livestock houses; and a processor connected to the communication module (claim 12); plurality of environmental facilities that form an environment of the livestock house, are operated (claim 11); a plurality of environmental sensors (claim 11); a controller that controls the operation of the plurality of environmental facilities (claim 11); service server (claim 11);
The processor, wherein, in the collecting of the livestock house environment data, the processor collects the livestock house environment data using a digital twin model, deep learning models, user terminal, server comprising: a communication module configured to communicate with one or more twin livestock houses, a processor connected to the communication module, plurality of environmental sensors, a controller that controls the operation of the plurality of environmental facilities, and service server are recited at a high level of generality and amount to “apply it” (the abstract idea) with generic computing components (spec [0053]). Nothing in the claims improves technology or a technical field (See MPEP 2106.05(f)).
The plurality of environmental facilities that form an environment of the livestock house merely provide a general link to a particular technological environment in which to perform the abstract idea. Nothing in the claims improves upon the facilities or the technical field (See MPEP 2106.05(h))
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea and provide a general link to a particular technological environment or field of use (i.e. online).
As a result, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2,12, and 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li, Bin et al (CN 116699078) hereafter “CN ‘078”
As per claim 1:
CN ‘078 teaches:
A method of measuring carbon emissions, comprising (The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. – page 2):
collecting, by a processor, livestock house environment data from one or more twin livestock houses; and (…the area to be subjected to carbon emission monitoring is pre-divided into a plurality of areas, a corresponding monitoring method is adopted for the corresponding area – page 1; According to the method for monitoring carbon emission in livestock house provided by the invention, the concentration value of the carbon emission gas in step S2 is monitored by one of the following methods: a carbon discharge gas sensor is used for directly monitoring the concentration value of the carbon discharge gas; or the anemoscope and the laser gas analyzer are used for monitoring the data and transmitting the data to the data processor to obtain the concentration value of the carbon exhaust gas; or the carbon emission gas detection unit, the wind speed sensor and the unmanned aerial vehicle are used for monitoring data and transmitting the data to the data processor to obtain the concentration value of the carbon emission gas. Page 2-3 Examiner Comment: The spec seems to differentiate between twin livestock houses and digital twins. A search of twin livestock house provides the following: “A twin livestock house typically refers to a dual-unit or double livestock shelter structure designed to house animals. It features two separate sections, stalls, or compartments sharing a common wall or roof.” As the reference provides that he livestock house is pre-divided into a plurality of areas the examiner interprets this to meet the definition of a twin livestock house.)
selecting, by the processor, one or more factors from the livestock house environment data and generating a plurality of carbon emission measurement models. (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
As per claim 12:
CN ‘078 teaches:
A carbon emission measurement service server comprising (The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. – page 2): a communication module configured to communicate with one or more twin livestock houses; and (…the area to be subjected to carbon emission monitoring is pre-divided into a plurality of areas, a corresponding monitoring method is adopted for the corresponding area – page 1; According to the method for monitoring carbon emission in livestock house provided by the invention, the concentration value of the carbon emission gas in step S2 is monitored by one of the following methods: a carbon discharge gas sensor is used for directly monitoring the concentration value of the carbon discharge gas; or the anemoscope and the laser gas analyzer are used for monitoring the data and transmitting the data to the data processor to obtain the concentration value of the carbon exhaust gas; or the carbon emission gas detection unit, the wind speed sensor and the unmanned aerial vehicle are used for monitoring data and transmitting the data to the data processor to obtain the concentration value of the carbon emission gas. Page 2-3 Examiner Comment: The spec seems to differentiate between twin livestock houses and digital twins. A search of twin livestock house provides the following: “A twin livestock house typically refers to a dual-unit or double livestock shelter structure designed to house animals. It features two separate sections, stalls, or compartments sharing a common wall or roof.” As the reference provides that he livestock house is pre-divided into a plurality of areas the examiner interprets this to meet the definition of a twin livestock house.)
a processor connected to the communication module, wherein the processor collects livestock house environment data from the one or more twin livestock houses, (…the area to be subjected to carbon emission monitoring is pre-divided into a plurality of areas, a corresponding monitoring method is adopted for the corresponding area – page 1; According to the method for monitoring carbon emission in livestock house provided by the invention, the concentration value of the carbon emission gas in step S2 is monitored by one of the following methods: a carbon discharge gas sensor is used for directly monitoring the concentration value of the carbon discharge gas; or the anemoscope and the laser gas analyzer are used for monitoring the data and transmitting the data to the data processor to obtain the concentration value of the carbon exhaust gas; or the carbon emission gas detection unit, the wind speed sensor and the unmanned aerial vehicle are used for monitoring data and transmitting the data to the data processor to obtain the concentration value of the carbon emission gas. Page 2-3)
and selects one or more factors from the collected livestock house environment data to generate a plurality of carbon emission measurement models. (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
CN ‘078 teaches the limitations of claims 1 and 12. As per claims 2 and 13:
CN ‘078 teaches:
wherein the livestock house environment data includes external environment information including at least one of factors such as an external temperature, an external humidity, a wind speed, an atmospheric pressure, or a latitude, or combination thereof, (…the wind speed sensor and the unmanned aerial vehicle are used for monitoring data and transmitting the data to the data processor to obtain the concentration value of the carbon emission gas. – page 3)and internal environment information including at least one of factors such as a manure temperature, a manure pH, an oxygen content in manure, an internal temperature, an internal humidity, an amount of methane, an amount of carbon dioxide, an amount of ammonia, a number of livestock, or a weight of livestock, or combination thereof. (According to the livestock house carbon emission monitoring system provided by the invention, the carbon emission gas sensor of the livestock house carbon emission monitoring system comprises a methane sensor, a carbon dioxide sensor, a sulphur hexafluoride sensor and a nitrous oxide sensor, and each sensor is provided with a plurality of sensors, a plurality of the same sensor are arranged on the inner top of the breeding house in the breeding house area in an array form. -Page 6)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 3 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Bin et al (CN 116699078) hereafter “CN ‘078” in view of Eun Jee Sook (KR 20210125374) hereafter “KR ‘374”
CN ‘078 teaches the limitations of claim 1. As per claim 3:
CN ‘078 does not expressly teach wherein, in the collecting of the livestock house environment data, the processor collects the livestock house environment data using a digital twin model.
KR ‘374 teaches:
wherein, in the collecting of the livestock house environment data, the processor collects the livestock house environment data using a digital twin model. (1 is a functional block diagram for explaining a smart livestock house system using a digital twin according to an embodiment of the present invention. As shown in FIG. 1 , a smart livestock house system using a digital twin according to an embodiment of the present invention includes a plurality of environmental facilities 100 , a plurality of environmental sensors 200 , a controller 300 , and an information collector 400 . and a simulator 500 . – page 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein, in the collecting of the livestock house environment data, the processor collects the livestock house environment data using a digital twin model as taught by KR ‘374 with the livestock house carbon emission monitoring of CN ‘078 in order to increase an accuracy of farm control information (page 1).
CN ‘078 teaches the limitations of claim 1. As per claim 11:
CN ‘078 teaches:
and transmits the livestock house environment data including at least one of control information used for operating the environmental facilities, the information on the operating results, or he environment information of the livestock house to a carbon emission measurement service server, or combination thereof. …the area to be subjected to carbon emission monitoring is pre-divided into a plurality of areas, a corresponding monitoring method is adopted for the corresponding area – page 1; According to the method for monitoring carbon emission in livestock house provided by the invention, the concentration value of the carbon emission gas in step S2 is monitored by one of the following methods: a carbon discharge gas sensor is used for directly monitoring the concentration value of the carbon discharge gas; or the anemoscope and the laser gas analyzer are used for monitoring the data and transmitting the data to the data processor to obtain the concentration value of the carbon exhaust gas; or the carbon emission gas detection unit, the wind speed sensor and the unmanned aerial vehicle are used for monitoring data and transmitting the data to the data processor to obtain the concentration value of the carbon emission gas. Page 2-3 . According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
CN ‘078 does not expressly teach wherein the twin livestock house includes: a plurality of environmental facilities that form an environment of the livestock house, are operated, and provide information on operating results; a plurality of environmental sensors that detect environment information of the livestock house; and a controller that controls the operation of the plurality of environmental facilities.
KR ‘374 teaches:
wherein the twin livestock house includes: a plurality of environmental facilities that form an environment of the livestock house, are operated, and provide information on operating results; a plurality of environmental sensors that detect environment information of the livestock house; and a controller that controls the operation of the plurality of environmental facilities, (The present invention relates to a smart livestock barn system and method using a digital twin, and more particularly, to an automation technology for livestock barn design and testing.In these latest livestock houses, sensors and controllers are attached to most equipment, such as feeders and drinking machines, temperature/humidifiers, and video devices, and IoT technology is applied to activate automatic control and monitoring functions. However, the existing livestock house technology of the automatic operation system in which the monitoring and automatic control technology is grafted has a problem that it is difficult to be applied and used in various environments because it is customized for one livestock house.1 is a functional block diagram for explaining a smart livestock house system using a digital twin according to an embodiment of the present invention. As shown in FIG. 1 , a smart livestock house system using a digital twin according to an embodiment of the present invention includes a plurality of environmental facilities 100 , a plurality of environmental sensors 200 , a controller 300 , and an information collector 400 . and a simulator 500 .A plurality of environmental facilities 100 are installed in the livestock house, are driven according to control information of the controller 300 , and provide driving result information to the information collector 400 . The environmental equipment 100 according to an embodiment of the present invention may be a ventilation fan, a cooling pad, a drinking machine, a feed amount meter, a weight scale, and a feed empty amount meter. The information collector 400 collects and stores control information for driving the environmental facility 100 , driving result information, and environmental information of the livestock house based on time. – page 2-3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the twin livestock house includes: a plurality of environmental facilities that form an environment of the livestock house, are operated, and provide information on operating results; a plurality of environmental sensors that detect environment information of the livestock house; and a controller that controls the operation of the plurality of environmental facilities as taught by KR ‘374 with the livestock house carbon emission monitoring of CN ‘078 in order to increase an accuracy of farm control information (page 1).
Claim(s) 4-6 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Bin et al (CN 116699078) hereafter “CN ‘078” in view of Russo et al (US 12,182,826)
CN ‘078 teaches the limitations of claims 1 and 12. As per claims 4 and 14:
CN ‘078 teaches:
wherein, in the generating of the plurality of carbon emission measurement models, the processor generates one or more regression models using the livestock house environment data of each twin livestock house, {…} According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. According to the livestock house carbon emission monitoring method provided by the invention, in step S4, all carbon emission rate data are fitted through direct interpolation method, constructing a one-element regression equation, as shown in formula (1), formula (2) and formula (3): using formula (1), formula (2) and formula (3), calculating and obtaining the carbon emission total amount of the carbon emission monitoring area in a certain period of time)
CN ‘078 does not expressly teach and repeatedly verifies and modifies the generated regression model to generate one or more carbon emission measurement models.
Russo teaches:
and repeatedly verifies and modifies the generated regression model to generate one or more carbon emission measurement models. ([C2L38-41] In one or more embodiments, real carbon data may be used to iteratively train one or more models in order to increase the accuracy of projections in future iterations. [C18L53-C19L1] For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes and/or other processes to calculate an output datum. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include and repeatedly verifies and modifies the generated regression model to generate one or more carbon emission measurement models as taught by Russo with the livestock house carbon emission monitoring of CN ‘078 in order to iteratively train one or more models in order to increase the accuracy of projections in future iterations.
CN ‘078 in view of Russo teaches the limitations of claims 4 and 14. As per claims 5 and 15:
Russo further teaches:
wherein, in the generating of the plurality of carbon emission measurement models, the processor analyzes a correlation between the carbon emissions and each factor included in the livestock house environment data of each twin livestock house, selects one or more factors on the basis of the analyzed correlation, and generates the one or more regression models using the selected factors. ([C22L52-C23L40] With continued reference to FIG. 1, processor 108 may use a machine learning module, such as a projection machine learning module for the purposes of this disclosure, to implement one or more algorithms or generate one or more machine-learning models, such as a projection machine learning model 172, to generate one or more projected carbon emission 160 and/or projected carbon blocks 168. Training data may include inputs and corresponding predetermined outputs so that a machine-learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows machine-learning model to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning models, as described in further detail below.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein, in the generating of the plurality of carbon emission measurement models, the processor analyzes a correlation between the carbon emissions and each factor included in the livestock house environment data of each twin livestock house, selects one or more factors on the basis of the analyzed correlation, and generates the one or more regression models using the selected factors as taught by Russo with the livestock house carbon emission monitoring of CN ‘078 in order to iteratively train one or more models in order to increase the accuracy of projections in future iterations.
CN ‘078 teaches the limitations of claims 1 and 12. As per claims 6 and 16:
CN ‘078 teaches:
wherein, in the generating of the plurality of carbon emission measurement models, the processor selects one or more factors that are easy to collect from the livestock house environment data (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
CN ‘078 does not expressly teach generates one or more deep learning models using the selected factors, and repeatedly trains and updates each generated deep learning model to generate one or more carbon emission measurement models.
Russo teaches:
generates one or more deep learning models using the selected factors, and repeatedly trains and updates each generated deep learning model to generate one or more carbon emission measurement models. ([C22L43-59] With continued reference to FIG. 1, in one or more embodiments, carbon projection module 164 may generate projected carbon emissions 160 as a function of a machine learning model. Additionally or alternative, carbon projection module 164 may determine one or more projected carbon blocks 168 as a function of a machine learning model, wherein the aggregation of projected carbon blocks 168 may be used to generate projected carbon emissions 160. ) With continued reference to FIG. 1, processor 108 may use a machine learning module, such as a projection machine learning module for the purposes of this disclosure, to implement one or more algorithms or generate one or more machine-learning models, such as a projection machine learning model 172, to generate one or more projected carbon emission 160 and/or projected carbon blocks 168. [C43L41-54] As a further non-limiting example, a machine-learning model 324 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 304 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generates one or more deep learning models using the selected factors, and repeatedly trains and updates each generated deep learning model to generate one or more carbon emission measurement models as taught by Russo with the livestock house carbon emission monitoring of CN ‘078 in order to iteratively train one or more models in order to increase the accuracy of projections in future iterations.
CN ‘078 teaches the limitations of claims1 and 12. As per claims 7 and 17:
CN ‘078 teaches:
wherein, in the generating of the plurality of carbon emission measurement models, the processor selects a factor object from a carbon cycle object model (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
CN ‘078 does not expressly teach generates one or more deep learning models using the selected factorobject, and trains and updates each generated deep learning model using the livestock house environment data to generate one or more carbon emission measurement models.
Russo teaches:
generates one or more deep learning models using the selected factor object, and trains and updates each generated deep learning model using the livestock house environment data to generate one or more carbon emission measurement models. ([C2L38-41] In one or more embodiments, real carbon data may be used to iteratively train one or more models in order to increase the accuracy of projections in future iterations. [C22L43-59] With continued reference to FIG. 1, in one or more embodiments, carbon projection module 164 may generate projected carbon emissions 160 as a function of a machine learning model. Additionally or alternative, carbon projection module 164 may determine one or more projected carbon blocks 168 as a function of a machine learning model, wherein the aggregation of projected carbon blocks 168 may be used to generate projected carbon emissions 160. ) With continued reference to FIG. 1, processor 108 may use a machine learning module, such as a projection machine learning module for the purposes of this disclosure, to implement one or more algorithms or generate one or more machine-learning models, such as a projection machine learning model 172, to generate one or more projected carbon emission 160 and/or projected carbon blocks 168. [C43L41-54] As a further non-limiting example, a machine-learning model 324 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 304 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generates one or more deep learning models using the selected factor object, and trains and updates each generated deep learning model using the livestock house environment data to generate one or more carbon emission measurement models as taught by Russo with the livestock house carbon emission monitoring of CN ‘078 in order to iteratively train one or more models in order to increase the accuracy of projections in future iterations.
Claim(s) 8, 9, 10 18,19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li, Bin et al (CN 116699078) hereafter “CN ‘078” in view of O’Donnell et al (US 2022/0170388)
CN ‘078 teaches the limitations of claims 1 and 12. As per claims 8 and 18:
CN ‘078 does not expressly teach further comprising, after the generating of the plurality of carbon emission measurement models: when a user terminal requests a carbon emission measurement service, providing, by the processor, a list of the plurality of carbon emission measurement models to the user terminal; and measuring, by the processor, carbon emissions from a corresponding livestock house on the basis of a carbon emission measurement model selected by the user terminal.
O’donnell teaches:
further comprising, after the generating of the plurality of carbon emission measurement models: when a user terminal requests a carbon emission measurement service, providing, by the processor, a list of the plurality of carbon emission measurement models to the user terminal; and measuring, by the processor, carbon emissions from a corresponding livestock house on the basis of a carbon emission measurement model selected by the user terminal. ([0590] In one implementation, the control system confirms and compares simulation models to select measurements of temperatures, flows, and power levels at various points within the system. The control system may consider the models in control calculations governing power to the heating elements. For example, wall temperatures may be a limiting factor in the current input power allowable for a given heater, with limits calculated based on peak refractory temperature and peak wire temperature. A constant-wattage (constant-Q heat flux) charging may not be feasible without the heater temperature significantly exceeding the refractory temperature.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include further comprising, after the generating of the plurality of carbon emission measurement models: when a user terminal requests a carbon emission measurement service, providing, by the processor, a list of the plurality of carbon emission measurement models to the user terminal; and measuring, by the processor, carbon emissions from a corresponding livestock house on the basis of a carbon emission measurement model selected by the user terminal as taught by O’Donnell with the livestock house carbon emission monitoring of CN ‘078 in order to lower-cost energy storage systems and technologies that utilize VRE to provide industrial process energy, which may expand VRE and reduce fossil fuel combustion ([0005]).
CN ‘078 in view of O’Donnell teaches the limitations of claims 8 and 18. As per claims 9 and 19:
CN ‘078 teaches:
wherein, in the measuring of the carbon emissions from the livestock house, the processor generates an input value of the selected carbon emission measurement model in conjunction with the livestock house, and inputs the input value into the selected carbon emission measurement model to measure the carbon emissions from the livestock house. (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
CN ‘078 in view of O’Donnell teaches the limitations of claims 8 and 18. As per claims 10 and 20:
CN ‘078 teaches:
wherein, in the measuring of the carbon emissions from the livestock house, the processor receives an input value of the selected carbon emission measurement model from the user terminal, and inputs the input value into the selected carbon emission measurement model to measure the carbon emissions from the livestock house. (According to the method for monitoring carbon emission in livestock house provided by the invention, in step S3, the method for obtaining the carbon emission rate by using the carbon emission gas emission rate is as follows: Carbon emission rate = carbon dioxide emission rate * carbon dioxide global warming potential value + methane emission rate * methane global warming potential value + nitrous oxide emission rate * nitrous oxide global warming potential value + sulphur hexafluoride emission rate * nitrous oxide global warming potential value. Page 3; The invention provides a livestock house carbon emission monitoring method, monitoring system, electronic device and storage medium, by deploying multi-point gas monitoring sensor, it can be automatically replaced according to the field requirement, and by means of open-circuit laser gas analyzer, the three-dimensional ultrasonic anemoscope and so on construct the livestock and poultry farm area carbon emission monitoring model, using the space compensation algorithm to optimize the model, estimating the total carbon content of the farm, realizing the carbon emission three-dimensional monitoring and total carbon calculation of the farm. Page 2; According to the method for monitoring carbon emission in a livestock house provided by the present invention, in step S1003, the source intensity of the carbon emission gas in the channel region is calculated, and is performed by using an inversion type gas diffusion model, page 5; S2023, using the calculation fluid dynamics method, using the standard the turbulent flow model and the component conveying model perform simulation analysis on the wind speed, temperature and humidity in the measured methane sensor matrix and the methane concentration value measured by the methane sensor to obtain the methane concentration simulation value in the measured methane sensor matrix; - page 10)
Conclusion
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/ Primary Examiner, Art Unit 3621