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 amendment filed on 6/14/2026.
Claims 1, 6, 8, 11, 12, 16, and 18 have been amended.
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 § 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:
The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claim 1:
detecting internal environmental information and external environmental information form one or more twin livestock houses;
collecting the detected internal environmental and the external environmental information from the one or more twin livestock houses;
selecting one or more factors from the collected livestock house environment data;
generating a plurality of carbon emission measurement models using the selected factors,
transmitting the generated carbon emission measurement models to a user, and
measuring carbon emissions of a corresponding livestock house based on a carbon emission measurement model selected by the user among the transmitted carbon emission measurement models.
Claim 12:
collect internal environmental information and external environmental information detected from the one or more twin livestock houses,
select one or more factors from the collected livestock house environment data,
generate a plurality of carbon emission measurement models using the selected factors,
transmit the generated carbon emission measurement models to a user, and
measure carbon emissions of a corresponding livestock house based on a carbon emission measurement model selected by the user among the transmitted 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 the one or more factors that are collected 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 the 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 the 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 of a carbon emission service server in communication with the one or more twin livestock houses via a communication network (claims 1, 3-10); 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 1, 8, 10, 12, 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, 14-20); plurality of environmental facilities that form an environment of the livestock house, are operated (claim 11); a plurality of environmental sensors (claim 1, 11); a controller that controls the operation of the plurality of environmental facilities (claim 11); service server (claim 11);
The processor of a carbon emission service server in communication with the one or more twin livestock houses via a communication network, 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.
With regard to prior art:
The examiner was unable to find a reasonable combination of references to teach each and every limitation in the context of the claimed invention. Specifically, the examiner was unable to find in the context of twin livestock houses:
“generating, by the processor, a plurality of carbon emission measurement models using the selected factors, transmitting, by the processor, the generated carbon emission measurement models to a user terminal, and measuring, by the processor, carbon emissions of a corresponding livestock house based on a carbon emission measurement model selected by the user terminal among the transmitted carbon emission measurement models.”
The closest prior arts include:
Li, Bin et al (CN 116699078) - generally teaches a livestock house carbon emission monitoring method which divides a carbon emission monitoring area into multiple areas, monitors the concentration values of the carbon exhaust gas in the multiple areas, and performs fusion calculation by using the concentration values of the carbon exhaust gas in the multiple areas.
Eun Jee Sook (KR 20210125374) – generally teaches a smart livestock house system using a digital twin including a plurality of environmental facilities, a plurality of environmental sensors, a controller, an information collector, and a simulator.)
Russo et al (US 12,182,826) – generally teaches using real carbon data to iteratively train one or more models in order to increase the accuracy of projections in future iterations. 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.
O’Donnell et al (US 2022/0170388) – generally teaches a control system that confirms and compares simulation models to select measurements of temperatures, flows, and power levels at various points within a system.
Response to Arguments
The examiner has considered and finds persuasive applicant’s arguments regarding amendments to claims 6 and 16 have overcome previous rejections under 35 USC 112. As a result, such rejection has been withdrawn.
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101. With regard to whether the claims recite abstract ideas, the examiner respectfully disagrees. The claims merely use a generic computer to broadly collect environment data, select factors, generate models based on those factors, transmit the models to a user to select a model, and then measure the carbon emission using the selected model. The claims are little more than a data process that could be done by hand using pen and paper or in the mind.
With regard to mental process the examiner respectfully disagrees. The generically recited sensors are considered additional elements and are not part of the abstract idea. They are addressed with regard to step 2A prong 2. However, a human could absolutely generate carbon emission models and measure carbon emissions of a live stock house based on the selected carbon emission measurement model. The examiner did not see anything in the specification that defines “measuring” to be a particular technical process. As best the examiner can tell, the measuring is merely performing the calculation using the chosen model. If applicant finds “measuring” to include some sort of technical process then such technical features would need to be claimed. The creation and selection of models for measuring carbon emissions is neither a technical problem nor is creating the models and calculating the carbon emissions a technical solution. The same result of creating models and using the models occurs with or without computing devices.
With regard to step 2A prong 2 the examiner respectfully disagrees. The claims provide little more than generic computing devices used to implement the abstract idea. Nothing in the claims improves computer technology or a technical field. The claims in no way improve sensor technology, data collection technology, or a technical field. The sensors are merely used to gather the data in the way sensors are meant to gather data. Further, claim 12 does not even include the sensors.
Further, the examiner finds no similarities what so ever between Enfish or McRO and the present invention. Enfish improved the way computers stored and retrieved data. McRO was found eligible because it involved a set of rules that were created specifically to do something with a computer and such rules would not make sense or would been differently if not done with a computer. Applicant’s invention provide no such improvement to computers and the process outlined in the claims would be the same with or without a computer.
Similarly, the additional elements do not provide an inventive concept. The computers are and sensors are recited at a high level of generality and are merely used to implement the abstract idea. Further, the arrangement is not considered nonconventional as it merely uses sensors to provide data to a computer. The additional elements are not required to be well-understood, routine, and conventional. As a result, such rejections have been maintained.
The examiner has considered and finds persuasive applicant’s arguments regarding rejections under 35 USC 102 and 35 USC 103. As a result, such rejections have been withdrawn.
Conclusion
.THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraff can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621
/CHRISTOPHER STROUD/ Primary Examiner, Art Unit 3621
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