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
The following non-is a final office action.
Claims 1-10 are currently pending and have been examined on their merits.
Claims 8 and 10 are newly amended see REMARKS May 01, 2025.
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 1-10 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 recite a model, claims 8-9 recite a method (i.e. a series of steps), claim 10 recites a system and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claim 1 recites: a coupling relationship between a material flow and an energy flow, an input and output of the coupling of the material flow and energy flow, and a conversion relationship between the input and output of the coupling; based on coupling relationships between a carbon flow and the material and energy flows, by supplementing conversion relationships between the carbon flow and the material and energy flows; wherein, when a parameter changes, the material flow and the energy flow of a unit where the parameter resides are decoupled by using a metallurgical mechanism underlying a production process, thereby obtaining effects on other material flows and energy flows, and determining an influence scope on an output material flow or energy flow of the unit, an intermediate material flow or energy flow product, and input and output material flows or energy flows of downstream units, as well as trend changes of variables; when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables; wherein, the material flow comprises an iron-containing material flow and a carbon- containing material flow; the energy flow comprises a fuel and an energy carrier; the conversion relationship between the input and output of the coupling of material flow and energy flow refers to inputting a material flow, energy flow or coupled material flow and energy flow into a unit and outputting it from the unit after a conversion process, and then obtaining a converted material flow, energy flow or coupled material flow and energy flow; and the conversion relationships between the carbon flow and the material and energy flows are relationships between the material flow and its carbon content, the energy flow and its carbon content, and the coupled material flow and energy flow and its carbon content.
Claim 8: A carbon emission evaluation method for integrated iron and steel sites, comprising: collecting the input and output of the material flow and the energy flow in a production process of iron and steel sites, wherein the material flow comprises an iron- containing material flow and a carbon-containing material flow, and the energy flow comprises an fuel and an energy carrier; when a parameter changes, determining, based on the collected input and output of the material flow and the energy flow in the production process of iron and steel sites, effects of the parameter change on the material flow, the energy flow and the carbon flow contained within the material flow or the energy flow, wherein the parameter comprises a single or a plurality of material flows, energy flows, and key physical quantities of the flows; quantifying, based on the effects of the parameter change on the material flow and the energy flow and the carbon flow contained within the material flow or the energy flow, an initial carbon emission and an expected carbon emission in the production process before and after the parameter change; and obtaining, based on the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, a carbon emission difference value in the production process caused by the parameter change.
Claim 10: A carbon emission evaluation system for integrated iron and steel sites, comprising: an input and output module configured to collect the input and output of the material flow and the energy flow in a production process of iron and steel sites, wherein the material flow comprises an iron-containing material flow and a carbon-containing material flow, and the energy flow comprises a fuel and an energy carrier; determine, based on the input and output of the material flow and the energy flow in the production process of iron and steel sites collected by the input and output module when a parameter changes, the effects of the parameter change on the material flow, the energy flow, and the carbon flow contained within the material flow or the energy flow, wherein the parameter comprises a single or a plurality of material flows, energy flows, and the key physical quantities of the flows; and an evaluation module configured to quantify, based on the effects of the parameter change on the material flow and the energy flow and the carbon flow contained within the material flow or energy flow obtained by the analysis module, the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, and to obtain, based on the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, the carbon emission difference value in the production process caused by the parameter change.
The claims recite a mental process. The claims recite merely a method of determining the carbon emissions of an iron and steel site and determining a change in carbon flow based on a change to a parameter. The examiner finds these limitations to merely recite concepts the courts have identified as being mental processes such as be observations, evaluations, judgements, and opinions. As the claims recite merely observing and evaluating the change to an output of carbon emissions based on changing parameters or aspects of a production process. Furthermore, the examiner finds that a user such as a site manager could mentally, or with the aid of a “pen and paper,” perform the steps of determining a change in carbon emissions outputs associated with a change in production parameters.
Therefore, the examiner finds the claims to recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A carbon emission evaluation model for integrated iron and steel sites, comprising: a material flow-energy flow coupling analysis model.
Claim 8: the carbon emission evaluation model for integrated iron and steel sites according to claim 1.
Claim 10: a model establishing module configured to establish the carbon emission evaluation model for integrated iron and steel sites according to claim 1 and an analysis module.
However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Merely utilizing a generic computer system to perform the claim limitations of receiving an input and generating an output such as determining a change in carbon emissions based on a change in production parameters, is not an improvement to a technology or technical field. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Therefore, the additional elements do not integrate the judicial exception into a practical application and do not amount to significantly more.
Claims 2-7 and 9 further narrowing the abstract idea of evaluating the carbon emissions of a process based on the changes to a plurality of inputs actions as disclosed by claims 1, 8, and 10.
The dependent claims do not recite any additional elements that are not discussed in the above analysis.
Therefore, claims 1-10 are rejected under U.S.C. 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beal (US 2022/0276222) in view of Kahn (US 2022/0358515).
Claim 1: Beal discloses A carbon emission evaluation model for integrated iron and steel sites, comprising: a material flow-energy flow coupling analysis model composed of a coupling relationship between a material flow and an energy flow, an input and output of the coupling of the material flow and energy flow, and a conversion relationship between the input and output of the coupling (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
and a material flow-energy flow-carbon flow coupling analysis model formed, based on coupling relationships between a carbon flow and the material and energy flows, by supplementing conversion relationships between the carbon flow and the material and energy flows on the basis of the material flow-energy flow coupling analysis model (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
wherein, when a parameter changes, the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow coupling analysis model and a metallurgical mechanism underlying a production process, thereby obtaining effects on other material flows and energy flows, and determining an influence scope on an output material flow or energy flow of the unit, an intermediate material flow or energy flow product, and input and output material flows or energy flows of downstream units, as well as trend changes of variables (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
wherein, the material flow comprises an iron-containing material flow and a carbon- containing material flow; the energy flow comprises a fuel and an energy carrier (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
the conversion relationship between the input and output of the coupling of material flow and energy flow refers to inputting a material flow, energy flow or coupled material flow and energy flow into a unit and outputting it from the unit after a conversion process, and then obtaining a converted material flow, energy flow or coupled material flow and energy flow (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
and the conversion relationships between the carbon flow and the material and energy flows are relationships between the material flow and its carbon content, the energy flow and its carbon content, and the coupled material flow and energy flow and its carbon content (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Beal discloses a system of determining the carbon emission relationship based on manufacturing processes of a material. However, Beal does not disclose the following claim limitations: when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables.
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the carbon emissions of a plurality of production practices such as producing iron or steel goods as disclosed by Beal with the system of when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Claim 2: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 1. Beal further discloses wherein, the material flow-energy flow coupling analysis model is established by steps comprising: collecting the input and output of the material flow and the energy flow in a production process of iron and steel sites; determining, based on the metallurgical mechanism and the input and output of the material flow and the energy flow in the production process, the coupling relationship between the material flow and the energy flow (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
determining, based on the metallurgical mechanism and the input and output of the material flow and the energy flow in the production process, the coupling relationships between the carbon flow and the material and energy flows (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
determining, according to the coupling relationship between the material flow and the energy flow, as well as the coupling relationships between the carbon flow and the material and energy flows, unit processes connected by the material flow, the energy flow and the carbon flow, wherein the unit processes comprise a major process, an energy system, and production equipment contained within the major process and the energy system (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
determining, according to the coupling relationship between the material flow and the energy flow, as well as the unit processes, the conversion relationship between the input and output of the coupling of material flow and energy flow in the production process; and establishing the material flow-energy flow coupling analysis model based on the input and output of the material flow and the energy flow, the coupling relationship between the material flow and the energy flow, and the conversion relationship between the input and output of the coupling of material flow and energy flow (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
and the material flow-energy flow-carbon flow coupling analysis model is established by steps comprising: determining, according to the coupling relationships between the carbon flow and the material and energy flows, as well as the unit processes, the conversion relationships between the carbon flow and the material and energy flows in the production process (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
and establishing the material flow-energy flow-carbon flow coupling analysis model based on the coupling relationships between the carbon flow and the material and energy flows by supplementing the conversion relationships between the carbon flow and the material and energy flows on the basis of the material flow-energy flow coupling analysis model (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
Claim 3: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 2. Beal further discloses wherein the input and output of the material flow and the energy flow in the production process comprises: basic information and key physical quantities of the material flow and the energy flow in the production process; wherein, the basic information comprises: types, flow trajectories, conversion rules, and constraint conditions of the material flow and the energy flow; and the key physical quantities comprise: consumption quantity, production quantity, recovery quantity, emission quantity, sinter return quantity, gas calorific value, steam temperature, steam pressure, carbon content, and iron content (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Claim 4: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 2. Beal further discloses wherein the coupling relationship between the material flow and the energy flow exists in solid fuels, and the coupling relationships between the carbon flow and the material and energy flows exist in solid fuels (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Claim 5: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 2. Beal further discloses wherein, the major process comprises coking, sintering, pelletizing, ironmaking, steelmaking, hot rolling, and cold rolling processes in an integrated blast furnace-basic oxygen furnace procedure; the energy system comprises coal gas, oxygen, steam, electric power, and water systems; the production equipment contained within the major process comprises a coke oven, a coke dry quenching device, a sintering machine, a circular cooler, roasting equipment, cooling equipment, a blast furnace, a hot blast stove, a basic oxygen furnace, a ladle, a refining furnace, a concaster, a reheating furnace, a roughing mill set, a finishing mill set, an annealing furnace, and a cold-rolling mill set; and the production equipment contained within the energy system comprises a coal gas producer, a combustion chamber, a waste heat boiler, a coal-fired boiler, a gas-fired boiler, a power generator set, an oxygen plant, a compressor, a blower, a pressurizer, and a water treatment device (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Claim 6: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 1. However, Beal does not disclose wherein said decoupling the material flow and the energy flow of a unit where the parameter resides by using the material flow-energy flow coupling analysis model and a metallurgical mechanism underlying a production process comprises: when a parameter changes, obtaining, according to the unit into which the parameter flows and by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, a variation pattern of the material flow or energy flow converted from and flowing out of the unit into which the parameter flows; and determining for true or not the material flow or energy flow is an intermediate product, if true, then analyzing, by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, variation patterns of other material flows or energy flows converted from and flowing out of the unit into which the parameter flows.
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches wherein said decoupling the material flow and the energy flow of a unit where the parameter resides by using the material flow-energy flow coupling analysis model and a metallurgical mechanism underlying a production process comprises: when a parameter changes, obtaining, according to the unit into which the parameter flows and by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, a variation pattern of the material flow or energy flow converted from and flowing out of the unit into which the parameter flows; and determining for true or not the material flow or energy flow is an intermediate product, if true, then analyzing, by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, variation patterns of other material flows or energy flows converted from and flowing out of the unit into which the parameter flows (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of wherein said decoupling the material flow and the energy flow of a unit where the parameter resides by using the material flow-energy flow coupling analysis model and a metallurgical mechanism underlying a production process comprises: when a parameter changes, obtaining, according to the unit into which the parameter flows and by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, a variation pattern of the material flow or energy flow converted from and flowing out of the unit into which the parameter flows; and determining for true or not the material flow or energy flow is an intermediate product, if true, then analyzing, by using the material flow-energy flow coupling analysis model and the metallurgical mechanism underlying the production process, variation patterns of other material flows or energy flows converted from and flowing out of the unit into which the parameter flows as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Claim 7: Modified Beal discloses the carbon emission evaluation model for integrated iron and steel sites as per claim 1. However, Beal does not disclose wherein said decoupling the carbon flows from the material flow and the energy flow of a unit where the parameter resides by using the material flow-energy flow-carbon flow coupling analysis model and a metallurgical mechanism underlying a production process comprises: when the parameter changes, obtaining, according to the unit into which the parameter flows and by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, a variation pattern of the carbon flow contained within the material flow or energy flow converted from and flowing out of the unit into which the parameter flows; and determining for true or not the material flow or energy flow is an intermediate product, if true, then analyzing, by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, variation patterns of the carbon flows contained within other material flows or energy flows converted from and flowing out of the unit into which the parameter flows.
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches wherein said decoupling the carbon flows from the material flow and the energy flow of a unit where the parameter resides by using the material flow-energy flow-carbon flow coupling analysis model and a metallurgical mechanism underlying a production process comprises: when the parameter changes, obtaining, according to the unit into which the parameter flows and by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, a variation pattern of the carbon flow contained within the material flow or energy flow converted from and flowing out of the unit into which the parameter flows; and determining for true or not the material flow or energy flow is an intermediate product, if true, then analyzing, by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, variation patterns of the carbon flows contained within other material flows or energy flows converted from and flowing out of the unit into which the parameter flows (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the carbon emissions of a plurality of production practices such as producing iron or steel goods as disclosed by Beal with the system of when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Claim 8: Beal discloses A carbon emission evaluation method for integrated iron and steel sites, comprising: collecting the input and output of the material flow and the energy flow in a production process of iron and steel sites, wherein the material flow comprises an iron- containing material flow and a carbon-containing material flow, and the energy flow comprises an fuel and an energy carrier (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
quantifying, based on the effects of the parameter change on the material flow and the energy flow and the carbon flow contained within the material flow or the energy flow, an initial carbon emission and an expected carbon emission in the production process before and after the parameter change; and obtaining, based on the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, a carbon emission difference value in the production process caused by the parameter change (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Beal discloses a system of determining the carbon emission relationship based on manufacturing processes of a material. However, Beal does not disclose the following claim limitations: when a parameter changes, determining, based on the collected input and output of the material flow and the energy flow in the production process of iron and steel sites, effects of the parameter change on the material flow, the energy flow and the carbon flow contained within the material flow or the energy flow by using the carbon emission evaluation model for integrated iron and steel sites according to claim 1, wherein the parameter comprises a single or a plurality of material flows, energy flows, and key physical quantities of the flows
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches when a parameter changes, determining, based on the collected input and output of the material flow and the energy flow in the production process of iron and steel sites, effects of the parameter change on the material flow, the energy flow and the carbon flow contained within the material flow or the energy flow by using the carbon emission evaluation model for integrated iron and steel sites according to claim 1, wherein the parameter comprises a single or a plurality of material flows, energy flows, and key physical quantities of the flows (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the carbon emissions of a plurality of production practices such as producing iron or steel goods as disclosed by Beal with the system of when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Claim 9: Modified Beal discloses the carbon emission evaluation method for integrated iron and steel sites according as per claim 8. However, Beal does not disclose wherein when multiple parameters change, effects of a first parameter change on the material flow, the energy flow, and the carbon flow contained within the material flow or the energy flow is determined by using the material flow-energy flow- carbon flow coupling analysis model, and effects of a second parameter change on the material flow, the energy flow, and the carbon flow contained within the material flow or the energy flow is determined by using the material flow-energy flow-carbon flow coupling analysis model.
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the carbon emissions of a plurality of production practices such as producing iron or steel goods as disclosed by Beal with the system of when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Claim 10: Beal discloses A carbon emission evaluation system for integrated iron and steel sites, comprising: an input and output module configured to collect the input and output of the material flow and the energy flow in a production process of iron and steel sites, wherein the material flow comprises an iron-containing material flow and a carbon-containing material flow, and the energy flow comprises a fuel and an energy carrier (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
a model establishing module configured to establish the carbon emission evaluation model for integrated iron and steel sites according to claim 1 (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products);
and an evaluation module configured to quantify, based on the effects of the parameter change on the material flow and the energy flow and the carbon flow contained within the material flow or energy flow obtained by the analysis module, the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, and to obtain, based on the initial carbon emission and the expected carbon emission in the production process before and after the parameter change, the carbon emission difference value in the production process caused by the parameter change (Paragraph [0005-0008]; [0013-0015]; [0021]; [0023-0027]; system and methods describe machine learning algorithms that apply modifications to models based on a type of data obtained, including producer specific management practice data, performance data, energy production data, among other such data to optimize models configured to quantify an amount of emissions emitted/generated by an emissions producing system. The models can include for example, product centric models. A product can include for example a material product (e.g. iron, gold, etc.), or other products. The model can include incorporating emissions calculations form one or more product specific models. The model can include generating a model that include appropriate equations components and adjusting data variables associated with the equation components based on performance data or other such data. On-site management practice data/protocols may be used with one or more other data and/or equations to determine energy, material or other product’s emissions or expected emissions over an assessment cycle. Similar data may be obtained from on-site management and operations. Once the data is obtained, a unique model that utilizes appropriate data can be generated. Input parameters can by dynamically selected based on available data. Thereafter the models can be used to determine emissions data for each emissions producing systems. For example, the models can be used to determine emissions for each energy carrier group (e.g. a gallon of gasoline), each material (e.g. iron ore) or other products).
Beal discloses a system of determining the carbon emission relationship based on manufacturing processes of a material. However, Beal does not disclose the following claim limitations: an analysis module configured to determine, based on the input and output of the material flow and the energy flow in the production process of iron and steel sites collected by the input and output module when a parameter changes, the effects of the parameter change on the material flow, the energy flow, and the carbon flow contained within the material flow or the energy flow by using the carbon emission evaluation model for integrated iron and steel sites established by the model establishment module, wherein the parameter comprises a single or a plurality of material flows, energy flows, and the key physical quantities of the flows.
In the same field of endeavor of measuring carbon emissions of a manufacturing steps Kahn teaches an analysis module configured to determine, based on the input and output of the material flow and the energy flow in the production process of iron and steel sites collected by the input and output module when a parameter changes, the effects of the parameter change on the material flow, the energy flow, and the carbon flow contained within the material flow or the energy flow by using the carbon emission evaluation model for integrated iron and steel sites established by the model establishment module, wherein the parameter comprises a single or a plurality of material flows, energy flows, and the key physical quantities of the flows (Paragraph [0004-0005]; [0021-0024]; [0030-0031]; Fig. 2B, various aspects of the technology described are generally directed to systems, methods, and computer storage media providing carbon emissions data analytics recommendations using a carbon emissions data analytics engine in a management system. The recommendation can be associated with simulate carbon emissions optimization results data based on carbon emissions data analytics model. The simulated carbon emissions optimization results data refers to outputs of a carbon emissions data analytics model. The model can process different data types of carbon emissions model input data and generate data visualizations that include the simulated carbon emissions optimization results data. The results data can be generated using factors of different activities, where a factor supports estimating pollutant associated with each activity. Activity data are automatically mapped to the carbon emissions factors).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of evaluating the carbon emissions of a plurality of production practices such as producing iron or steel goods as disclosed by Beal with the system of when a parameter changes, carbon flows from the material flow and the energy flow of a unit where the parameter resides are decoupled by using the material flow-energy flow-carbon flow coupling analysis model and the metallurgical mechanism underlying the production process, thereby obtaining effects on the carbon flows coupled with the material flow and the energy flow, and determining an influence scope on an output carbon flow of the unit, an intermediate material flow or energy flow product containing carbon flow, and input and output carbon flows of a downstream unit, as well as trend changes of variables as taught by Kahn. With the motivation of helping to determine practices to reduce carbon emissions by analyzing and displaying carbon emission estimation and recommendation data (Kahn [0003]).
Therefore, claim 1-10 are rejected under U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Silverstein (US 2021/0224819) Carbon foot print tracker.
Berggren (US 2018/0178292) Novel methods of metal processing.
Zimmerman (US 2008/0177605) Method and apparatus for generating standardized environmental benefit credits.
Trout (US 2006/0020502) Method and system for greenhouse gas emissions performance assessment and allocation.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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