DETAILED ACTION
Notice of 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 the Application
Claims 1-20 were pending and were rejected in the previous office action. Claims 1-5, 10-15, and 20 were amended. Claims 1-20 remain pending and are examined in this office action.
Response to Arguments
Claim Objections:
Claims 1-4, 10-14, and 20 were previous objected to for informalities. Claims 1-3, 10-14, and 20 are amended to the correct the previous issues – therefore, the objections of claims 1-3 and 10-14 withdrawn.
Claims 4 was amended, but still recites “monitoring the carbon emission of each of the processes” which should recite “monitoring the carbon emission of each of the one or more processes.” Therefore, claim 4 remains objected to in this office action.
35 USC § 112(b) Rejections:
Applicant’s arguments with respect to the previous § 112(b) rejections of claims 5, 9, 15, and 19 (pg. 9, remarks filed 3/5/2026) have been fully considered and are persuasive.
Claims 5 and 15 are amended to recite “a remaining carbon credit” and correct the previous issues. The rejection of claims 9 and 19 no longer applies as claims 1 and 11 are amended to include “a remaining useful life of each of the assets” which provides antecedent basis for claims 9 and 19. The previous § 112(b) rejections of claims 5, 9, 15, and 19 are withdrawn.
35 USC § 101 Rejection:
Applicant’s arguments with respect to the previous § 101 rejection of claims 1-20 (pgs. 9-15, remarks filed 3/5/2026) have been fully considered, but they are not persuasive.
Applicant first argues that the claims do not recite an abstract idea at step 2A prong one (pgs. 9-12, remarks). However, the examiner respectfully disagrees. The test at Step 2A Prong One is whether or not the claims include limitations that recite an abstract idea – and as described in MPEP 2106.04(a)(2)(III), “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” As identified in the previous office action, the claims recite a number of limitations which, under the broadest reasonable interpretation, could be performed in the human mind with or without the use of simple tools (e.g. pen/paper). For example, at least the steps for identifying one or more processes being run, identifying one or more assets, determining an energy demand profile, determining the carbon emissions of each of the processes based on the energy demand profile, identifying an availability of one or more green energy resources, receiving additional data (asset health index, an acquirable amount of green energy, or a weather forecast), executing an optimization model to provide and present one or more recommendations, can all be performed mentally or mentally on paper. While applicant mentions various additional elements or computer hardware elements, these elements are addressed at Step 2A Prong Two and Step 2B. Further, the alleged technical constrains are just simply different types of data that are received – the human mind is capable of receiving and understanding (e.g. reading, reviewing, observing) technical data. The recitation of generic computer components for performing these steps, as is later discussed in step 2A prong two and step 2B, does not amount to anything more than mere instructions to apply the abstract idea using generic computer components or generic computer implementation. Therefore, the examiner maintains that the claims recite an abstract idea at Step 2A Prong One.
Furthermore, while applicant characterizes the invention as being directed to “optimizing the energy supply strategy of industrial processes based on asset health, energy demand, carbon emission, renewable energy availability, and carbon credit consideration” (pg. 12, remarks) – this characterization only further supports that the claim recites an abstract idea, and is no less abstract than the examiner’s characterization of the abstract idea.
Applicant further argues that the claim recites additional limitations which integrate the abstract idea into a practical application at Step 2A Prong Two (pgs. 12-13, remarks). Specifically, applicant concludes that “the claimed method is practically implemented and used at the facility to guide how industrial processes are executed using selected energy sources, thereby reducing carbon emissions and improving carbon credit outcomes in real plant operations. This accounts for practical implementation” (pg. 13). However, the examiner respectfully disagrees. The test at Step 2A Prong Two is not whether or not the abstract idea is has any potential commercial application, but whether or not the claims recite additional elements that integrate the judicial exception into a practical application such as any of the following types of limitations described in MPEP 2106.04(d)(I)):
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The claims do not improve the functioning of a computer or improve any other technology or technical field, or otherwise apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For example, the claims do not recite a specific technical mechanism or machinery for controlling or altering the operations of specific machines involved in the industrial/manufacturing processes based on the selected one or more recommendation, but instead simply recites an abstract idea for determining and providing recommendations related to a green energy requirement and an energy source to be used for industrial processes, being applied using generic computer implementation (e.g. using claim 1 as representative, one or more processors, an asset repository, and a device with a graphical user interface). That the various identification, determination, receiving data, and providing/presentation of recommendations steps are applied within the context of industrial or manufacturing processes does not add anything that integrates the abstract idea into a practical application, beyond generally linking the performance of the abstract idea to a particular field of use (industrial/manufacturing processes). Therefore, the examiner maintains that the claims do not integrate the judicial exception (i.e. abstract idea) into a practical application.
Applicant further argues that the claim recites additional elements which amount to significantly more than the abstract idea at Step 2B (pgs. 13-15, remarks). Specifically, applicant argues that:
The Applicant asserts that generally process industries operate multiple energy-intensive industrial processes using physical assets such as equipment, hardware units, and sensors. These assets consume energy during execution of the processes and contribute to carbon emissions. The conventional energy sourcing from grids (coal, gas, thermal) results in high emissions and that industries must reduce carbon emissions while maintaining production efficiency. Further, renewable energy availability is variable and depends on environmental conditions such as weather forecasts. In addition, asset degradation affects efficiency and increases energy consumption, which in turn impacts emission levels. See at least paragraphs [0002]-[0006], [0030]- [0035] of originally filed Specification. In view of such challenges, the subject matter of amended independent claim 1 proposes a method that provides a processor-implemented solution that identifies industrial processes and their associated physical assets, determines energy demand profiles for each process, and computes carbon emissions based on actual energy requirements. It further incorporates technical data including asset health index representing remaining useful life and efficiency, forecasted weather data, and green energy availability. Using these inputs, an optimization model generates one or more recommendations specifying green energy requirements for each process. The recommendations are presented on a user interface to an operator for selection, and each recommendation is associated with carbon credit considerations and indicates an energy source to be used for execution of the processes. This integrates plant-level operational data, environmental forecasting, and emission metrics into a practical industrial energy management framework. See at least paragraphs [0061]-[0066], [0072]-[0085] of originally filed Specification. This technical solution yields practical advantages such as the claimed method provides improved industrial energy management by enabling process-specific optimization of energy sourcing based on real asset conditions and renewable availability. By incorporating asset health and remaining useful life into the optimization, the method more accurately models energy demand and compensates for efficiency degradation of equipment. By integrating weather-based renewable availability, it dynamically adapts energy sourcing decisions to fluctuating green energy supply. Additionally, by associating recommendations with carbon credit considerations and presenting selectable options to an operator, the system enables measurable reduction of carbon emissions while maintaining industrial productivity. Collectively, this results in enhanced emission control, improved energy efficiency, and optimized utilization of renewable resources in a process industry environment. See at least at paragraph [0061]-[0067], [0081]-[0091] of originally filed Specification.
Accordingly, based at least on the above, the Applicant respectfully submits that taking all the claim elements of amended independent claim 1, individually, and in combination, amended independent claim 1 as a whole amounts to significantly more than the abstract idea.
However, the examiner respectfully disagrees that the claims recite significantly more than the abstract idea. As discussed above, that the claims pertain to industrial processes merely generally links the performance of the abstract idea to a particular field of use and does not change that the underlying functions recited in the claim recite an abstract idea. Implementing the abstract idea by generic computer components, such as a processor, merely provides instructions to apply the abstract idea (i.e. “apply it”) using generic computer components. That the performance of the abstract idea improves energy sourcing decisions or provides recommendations that may reduce carbon emissions or improves energy efficiency, at best, describes an improvement to the underlying abstract idea itself, but does not describe a technological improvement (e.g. an improvement to the assets used to perform the industrial processes, improvement in the functioning of the computers). Instead, claim 1 (using claim 1 as representative), describes “identifying…one or more processes…identifying…one or more assets…determining an energy demand profile…determining the carbon emission of each of the one or more processes…identifying an availability of one or more green energy resources…receiving additional data…executing an optimization model to provide one or more recommendations…and presenting the one or more recommendations…to select at least one recommendation…wherein the selected recommendation indicates an energy source to be used for execution of each of the one or more processes.” (i.e. an abstract idea that can otherwise be performed via the human mind but for the recitation of generic computer components, i.e. a mental process) being applied using generic computer components (i.e. one or more processors, a user interface of a device – using claim 1 as an example).
However, the claims never actually control any of the assets or machinery involved in the “one or more processes” to change how the assets operate based on the provided recommendations, but merely receive data to determine and provide recommendations, and therefore at most “[T]he claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished” (See MPEP 2106.05(f)).
Therefore, the § 101 rejection of claims 1-20 is maintained. Please see the current § 101 rejection below which is updated based on the 3/5/2026 amendments.
35 USC § 102 and § 103 Rejections:
Applicant’s arguments with respect to the previous § 102 rejections of claims 1-2, 10-12, and 20 and the previous § 103 rejections of claims 3-9 and 13-19 (pgs. 15-19, remarks) have been considered but are moot, as they do not apply to the current grounds of rejection in the § 103 rejections of claims 1-20 below, in response to applicant’s amendments.
Please see the current § 103 rejections of claims 1-20 below. The examiner also notes that while applicant discusses that Tieng does not disclose the asset health index as described in amended claims 1/11/20, this features is not actually required in each of the claims, since only “at least one of” the listed types of additional data is required to be received.
Claim Objections
Claims 1, 4, 11 and 20 are objected to because of the following informalities:
Claims 1, 11, and 20 recite “wherein each of the one or more recommendations being associated with a carbon credit” – which appears it should recite “wherein each of the one or more recommendations is associated with a carbon credit.”
Claim 4 recites “monitoring the carbon emission of each of the processes” which appears it should recite “monitoring the carbon emission of each of the one or more processes.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1:
Claims 1-10 recite “A method…” (i.e. a process); claims 11-19 recite “A system… comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs when executed by the processor, cause the processor to…” (i.e. a machine); and claim 20 recites “A non-transitory computer-readable storage medium storing program instructions…the instructions, when executed by one or more processors, perform the steps of…” (i.e. an article of manufacture). These claims fall under one of the four categories of statutory subject matter and as a result, pass Step 1 of the subject matter eligibility test. However, “Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not end the eligibility analysis, because claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection.” See MPEP 2106.04. Accordingly, the examiner continues the subject matter eligibility analysis below.
Step 2A Prong One:
Independent claim 1, 11, and 20 (using claim 1 as representative) recites limitations for optimizing a carbon emission reduction in a process industry, comprising:
identifying…one or more processes being run in the process industry;
identifying…one or more assets associated with each of the running processes, the information of one or more assets being stored in an asset repository;
determining an energy demand profile of each of the one or more processes, said profile identifying an energy requirement in running each of the one or more processes;
determining the carbon emission of each of the one or more processes based on the energy demand profile of each of the one or more processes;
identifying an availability of one or more green energy resources for acquiring the green energy by the process industry;
receiving additional data, comprising at least one of: an asset health index of the one or more assets in each of the one or more processes, wherein the asset health index represents remaining useful life of each asset of the one or more assets indicative of an efficiency level of the asset; an acquirable amount of the green energy from each of the available green energy resources, wherein each green energy resource is associated with a carbon weightage and a cost; a weather forecast for a predetermined time period for a location of the process industry;
executing an optimization model to provide one or more recommendations providing a green energy requirement for each of the one or more processes based on the received additional data, the energy demand profile and the carbon emission of the process; and
presenting the one or more recommendations….to select at least one recommendation from the one or more recommendations, wherein each of the one or more recommendations being associated with a carbon credit, wherein the selected recommendation indicates an energy source to be used for execution of each of the one or more processes
The limitations of independent claims 1, 11, and 20 above are determined to recite an abstract idea (i.e. identifying and determining an energy demand profile and associated carbon emissions of industrial processes, identifying and analyzing available green energy resources that can be used in each process, and determining and providing/presenting recommendations for selection including a green energy requirement for each process and an energy source to be used for each process) for the reasons discussed in the following continued Step 2A Prong One analysis. Note that “An abstract idea can generally be described at different levels of abstraction.” Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016).
As described in MPEP 2106.04(a)(2)(III), “[T]he "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” and “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” The limitations recited by the representative independent claims 1, 11, and 20 above, under the broadest reasonable interpretation and but for the use of generic computer components, cover concepts (e.g. observation, evaluation, judgment, and opinion) that can reasonably be performed in the human mind or by the human mind with the aid of simple tools such as pen and paper. For example, the “receiving additional data…” step amounts to an observation (e.g. a human reading, observing, or being provided with the asset health/green energy/weather data), while the “identifying one or more processes…,” “identifying one or more assets…,” “determining an energy demand profile…,” “determining the carbon emission…,” “identifying an availability…,” and “executing an optimization model to provide one or more recommendations…” (which is considered analogous to optimizing or using an optimization model by hand via pen/paper) steps would be considered evaluations, judgments, and opinions. Furthermore, “presenting the one or more recommendations…” (but for the recitation of generic computer components) could otherwise be performed by a human by writing down and presenting a set of options/recommendations on paper. Therefore, as the processes above described by the representative independent claims 1, 11, and 20 can be characterized as mental processes (i.e. observation, evaluation, judgment, and opinion), but for the recitation of generic computer components in the claims, the claims fall under the “mental processes” category of judicial exceptions (i.e. abstract ideas).
Step 2A Prong Two:
The judicial exception (i.e. abstract idea) recited in claims 1, 11 and 20 is not integrated into a practical application because the claims recite mere instructions to apply the abstract idea (i.e. identifying and determining an energy demand profile and associated carbon emissions of industrial processes, identifying and analyzing available green energy resources that can be used in each process, and determining and providing/presenting recommendations for selection including a green energy requirement for each process and an energy source to be used for each process) using generic computers/computer components (i.e. “by one or more processors,” and “a user interface of a device associated with an operator” of claim 1; “A system…comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs when executed by the processor, cause the processor to…,” and “a user interface of a device associated with an operator” of claim 11; and “A non-transitory computer-readable storage medium storing program instructions…the instructions, when executed by one or more processors, perform the steps of…,” and “a user interface of a device associated with an operator” of claim 20). See at least ¶ 0044, ¶ 0086-0093 and ¶ 0101 of applicant’s spec. filed 7/15/2024 describing various general purposes processors, hardware, and describing the user interface in a manner that indicates nothing more than a generic user interface for displaying information that could be implemented on any number of generically recited computing devices, thus demonstrating the generic nature of the computer implementation used to apply the abstract idea. See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.” Furthermore, the claims recite the use of the one or more processors (claim 11) and program instructions (claim 20) to “receive additional data,” which merely amount to the use of generic computers in their ordinary capacity (e.g. to receive data). To any extent the “asset repository” might be considered an electronic database, it would also simply describe the use of a generic database in its ordinary capacity to store data. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application, but instead also indicates that the claims recite mere instructions apply the abstract idea using a generic computer or computer components. Nothing in the claims or the specification indicates that any of the hardware components above improve the functioning of computers or any other technology. Therefore, because the claims, considered as a whole, do not recite anything that integrates the abstract idea into a practical application, the claims are directed to an abstract idea.
Step 2B:
Claims 1, 11, and 20 do not include additional elements, whether considered alone or as an ordered combination, that are sufficient to amount to significantly more than the judicial exception (i.e. abstract idea) because as mentioned above, the claims recite mere instructions to apply the abstract idea (i.e. identifying and determining an energy demand profile and associated carbon emissions of industrial processes, identifying and analyzing available green energy resources that can be used in each process, and determining and providing/presenting recommendations for selection including a green energy requirement for each process and an energy source to be used for each process) using generic computers/computer components (i.e. “by one or more processors,” and “a user interface of a device associated with an operator” of claim 1; “A system…comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs when executed by the processor, cause the processor to…,” and “a user interface of a device associated with an operator” of claim 11; and “A non-transitory computer-readable storage medium storing program instructions…the instructions, when executed by one or more processors, perform the steps of…,” and “a user interface of a device associated with an operator” of claim 20). See at least ¶ 0044, ¶ 0086-0093 and ¶ 0101 of applicant’s spec. filed 7/15/2024 describing various general purposes processors, hardware, and describing the user interface in a manner that indicates nothing more than a generic user interface for displaying information that could be implemented on any number of generically recited computing devices, thus demonstrating the generic nature of the computer implementation used to apply the abstract idea. See MPEP 2106.05(f), showing “[C]laims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp.” Furthermore, the claims recite the use of the one or more processors (claim 11) and program instructions (claim 20) to “receive additional data,” which merely amount to the use of generic computers in their ordinary capacity (e.g. to receive data). To any extent the “asset repository” might be considered an electronic database, it would also simply describe the use of a generic database in its ordinary capacity to store data. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more, but instead also indicates that the claims recite mere instructions apply the abstract idea using a generic computer or computer components. Nothing in the claims or the specification indicates that any of the hardware components above improve the functioning of computers or any other technology. Considering the additional elements above as an ordered combination does not provide anything that adds significantly more.
Dependent Claims 2-10 and 12-19:
Dependent claims 2-10 and 12-19 are directed to the same abstract idea as independent claims 1 and 11 above as they do not recite anything that integrates the abstract idea into a practical application or amounts to significantly more than the abstract idea.
Dependent claims 2-10 and 12-19 do not add any additional elements beyond those addressed above but merely further describe the abstract idea above via limitations for:
generating one or more simulations, by the optimization model, to provide the one or more recommendations… (claims 2/12 – note that generating simulations not only corresponds to “mental processes” as it can be performed by hand by the human mind with simple tools, but also additionally falls under “mathematical concepts” as it describes the performance of mathematical calculations);
determining a return on investment (ROI) corresponding to each of the one or more simulations… (claims 3/13);
monitoring the carbon emission of each of the processes…and providing a feedback to the optimization model to recalculate the green energy requirement… (claims 4/14 – note that this limitation also falls under “mathematical concepts” as it describes the performance of mathematical calculations);
determine a carbon credit…and determine a remaining carbon credit… (claims 5/15); “wherein the asset health index is determined using either principle component analysis or a neural network” (claims 6/16 – note that the BRI of the claims do not require a neural network, though it would nonetheless amount to a generic computer component recited at a high level of generality);
determining one or more newly available green energy resources (claims 7/17);
predicting the availability of the green energy resources based on real time weather data (claims 8/18);
determining the remaining useful life of the asset by using a Bayesian regression algorithm (claims 9/19 – note that this also falls under “mathematical concepts” as it describes performing mathematical calculation); and “wherein the each of the one or more processes and the assets acquires the energy for its execution, the energy may be one of the green energy, a power grid supply or a combination of both” (claim 10)
For the reasons describes above, none of the dependent claims recite additional limitations which would integrate the abstract idea into a practical application, or add significantly more than the abstract idea.
Therefore, claims 1-20 are ineligible under § 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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 10-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez).
Note: US 20250192550 A1 claims priority to Taiwan Application Serial Number 112147851, filed Dec. 8, 2023, and therefore qualifies as prior art under 35 USC 102(a)(2). A copy of TWI877944B and an English translation (Google patents) of the publication of this application TWI877944B was attached with the previous 12/10/2025 office action and supports the subject matter relied upon herein.
Claim 1: Tieng teaches:
A method for optimizing a carbon emission reduction in a process industry (Tieng: ¶ 0006-0007 intelligent energy management system and methods for reducing carbon emissions in manufacturing), comprising:
identifying, by one or more processors (Tieng: ¶ 0046 “The intelligent energy management system 100 includes a memory 200 and a processor 300”, ¶ 0048 “The memory 200 may include random access memory (RAM) or other types of dynamic storage devices that can store information and instructions for the processor 300 to perform…”), one or more processes being run in the process industry (Tieng: ¶ 0049 showing “The intelligent energy management step S04 includes performing a plurality of energy prediction steps S042, a production scheduling step S044, a facility control step S046 and a microgrid integration step S04”; and also see ¶ 0086 showing data obtaining operation S22 involves obtaining a plurality of sets of process data 502, and ¶ 0018, ¶ 0046 showing obtaining production line information; wherein as per ¶ 0110 the present disclosure pertains to manufacturing systems and processes to fabricate products);
identifying, by the one or more processors, one or more assets associated with each of the running processes (Tieng: ¶ 0018 showing “The plurality of data sources include production line information, factory information, microgrid information, environmental information, a carbon emission, a low-carbon product process, enterprise organization information and carbon neutrality information. The production line information comes from production equipment of a manufacturing device. The factory information comes from factory equipment of the manufacturing device”),
the information of one or more assets being stored in an asset repository (Tieng: Fig. 1 and ¶ 0046 “The memory 200 stores a plurality of data sources. The plurality of data sources include production line information 202 (PLI), factory information 204 (FI), microgrid information 206 (MI)”; note that the claim does not describe any particulars of what “assets” include);
determining an energy demand profile of each of the one or more processes, said profile identifying an energy requirement in running each of the one or more processes (Tieng: ¶ 0018 and ¶ 0049 showing calculating the production-equipment predicted energy consumption and the factory-equipment predicted energy consumption information);
determining the carbon emission of each of the one or more processes based on the energy demand profile of each of the processes (Tieng: ¶ 0049 showing determining carbon emissions of each factory and production equipment, and ¶ 0056 showing “carbon emission cost includes a sum of direct and indirect carbon emissions of the machine…”; and ¶ 0066 “The carbon emission cost of the factory equipment 404 includes a sum of direct and indirect carbon emissions of the machine multiplied by a carbon price…” which is calculated at least in part on the predicted power consumption of the factory equipment);
identifying an availability of one or more green energy resources for acquiring the green energy by the process industry (Tieng: ¶ 0007, ¶ 0009, ¶ 0020, ¶ 0049-0050, ¶ 0067-0070, ¶ 0078, ¶ 0090 showing identifying/determining a predicted amount of renewable energy power generation from a renewable energy generator);
receiving additional data (Tieng: ¶ 0046, ¶ 0049 showing received environmental information including indoor and outdoor environmental factor information), comprising at least one of:
an asset health index of the one or more assets in each of the one or more processes, wherein the asset health index represents remaining useful life of each asset of the one or more assets indicative of an efficiency level of the asset;
an acquirable amount of the green energy from each of the available green energy resources, wherein each green energy resource is associated with a carbon weightage and a cost;
a weather forecast for a predetermined time period for a location of the process industry (Tieng: ¶ 0051, ¶ 0061 future environmental information, i.e. weather forecast information specific to the equipment, i.e. a location of the process; ¶ 0093 “high temperatures in summer and low temperatures in winter can be memorized, and the changing trend of temperature and possible abnormalities at the next time point can be predicted by learning the changing types of the temperatures, such as snowstorms or high temperature warnings”; also ¶ 0094 “future information (such as temperature and humidity predicted by the meteorological bureau…)” along with “ environmental information (e.g., an outdoor temperature (° C.)”);
executing an optimization model to provide at least one recommendation providing a green energy requirement for each of the processes based on the received additional data, the energy demand profile and the carbon emission of the process (Tieng: ¶ 0068 “In the microgrid integration step S048, the microgrid integration algorithm includes performing an optimization algorithm to solve the optimal power distribution ratio information… the optimization algorithm can be a genetic algorithm to meet an electricity demand of whole-factory estimated total energy consumption and regulate an electricity supply ratio of a power company, an energy storage system, the renewable energy generator 4062 and a non-renewable energy generator, thereby solving the power distribution with minimum cost”; note that ; also see ¶ 0067 showing generating the renewable-energy-generator predicted power generation information which is used as a reference for power distribution optimization, and showing that the past environmental information (Tk), the future environmental information (Tk+1) is used in generation of the renewable-energy-generator predicted power generation information; and also generally see ¶ 0018-0020 showing determining optimal production scheduling information and optimal power distribution ratio configured to control the product/factory equipment and a renewable energy generator)
With respect to the remaining limitations, while Tieng teaches generating, using an intelligent energy management system, an optimized power distribution/generation plan for powering a production/manufacturing site/factory (Tieng: ¶ 0006-0007, ¶ 0018-0020, ¶ 0067-0068 as above), but does not explicitly teach presenting a recommendation on a user interface of an operator device to select at least one recommendation. However, Ibanez teaches:
presenting the one or more recommendations on a user interface of a device associated with an operator to select at least one recommendation from the one or more recommendations (Ibanez: ¶ 0033 “The recommendation engine 160 can provide the recommended plan to the requestor client 150 via the remote recommendation server 130. Accordingly, the requestor client 150 can display the recommended plan on the GUI to display to the requestor”; see ¶ 0023-0034 and ¶ 0036-0040 generally showing generation and display of the plan to reduce a carbon footprint of a site, which may be a manufacturing plant or industrial building as per ¶ 0010-0013),
wherein each of the one or more recommendations being associated with a carbon credit (Ibanez: ¶ 0012, ¶ 0031 showing the plans may include purchasing power offset credits representing emission reduction of carbon, i.e. carbon credits),
wherein the selected recommendation indicates an energy source to be used for execution of each of the one or more processes (Ibanez: ¶ 0033-0034 showing a plan is approved, i.e. selected, wherein the selected/approved plan may involve energy sourcing decisions to curtail a carbon footprint during peak usage such as switching power provided to the site to using solar panels and battery power during a peak time)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the presentation of recommended plans for reducing a carbon footprint of an industrial/manufacturing site of Ibanez in the intelligent energy management system of Tieng with a reasonable expectation of success of arriving at the claimed invention, with the motivation that “carbon emissions can be reduced by replacing and/or altering operations of the energy consuming equipment, as well as reducing and/or altering the sources of energy” (Ibanez: ¶ 0010), and/or “to reduce the carbon footprint based on data associated with the given site” (Ibanez: ¶ 0012).
Note: While claim 1 is amended to further detail the asset health index, this element is not required by the claims. The claim only requires “at least one of” the different types of additional data – and Tieng teaches receiving weather data and a weather forecast relevant to the production equipment, i.e. for the location of the process being carried out.
Claims 2: Tieng/Ibanez teach claim 1. Tieng, as modified above, further teaches:
further comprising: generating one or more simulations, by the optimization model, to provide the one or more recommendations to acquire the green energy from the one or more green energy resources based on at least one of: the additional data, the energy demand profile, and the carbon emission of each of the one or more processes (Tieng: ¶ 0067-0068 as above, showing using predicted carbon emissions, energy consumption, and past/predicted environmental information into optimization model to solve for and output an optimal power distribution ratio, to meet an electricity demand of whole-factory estimated total energy consumption and regulate an electricity supply ratio of a power company, an energy storage system, the renewable energy generator 4062 and a non-renewable energy generator, which reads on at least a single simulation, i.e. one or more simulations)
Claim 10: Tieng/Ibanez teach claim 1. Tieng, as modified above, further teaches:
wherein the each of the one or more processes and the assets acquires the energy for its execution, the energy may be one of the green energy, a power grid supply or a combination of both (Tieng: ¶ 0068 showing “the optimization algorithm can be a genetic algorithm to meet an electricity demand of whole-factory estimated total energy consumption and regulate an electricity supply ratio of a power company, an energy storage system, the renewable energy generator 4062 and a non-renewable energy generator, thereby solving the power distribution with minimum cost”)
Claim 11: See the rejection of claim 1 above disclosing analogous limitations. Tieng further discloses: A system for optimizing a carbon emission reduction in a process industry, comprising: one or more processors; a memory; and one or more programs stored in the memory, the one or more programs when executed by the processor, cause the processor to… (Tieng: ¶ 0046 “The intelligent energy management system 100 includes a memory 200 and a processor 300”, ¶ 0048 “The memory 200 may include random access memory (RAM) or other types of dynamic storage devices that can store information and instructions for the processor 300 to perform…”; and ¶ 0006-0007 intelligent energy management system and methods for reducing carbon emissions in manufacturing).
Claim 12: See the rejection of claim 2 above.
Claim 20: See the rejection of claim 1 above disclosing analogous limitations. Tieng further discloses A non-transitory computer-readable storage medium storing program instructions for optimizing a carbon emission reduction in a process industry, the instructions, when executed by one or more processors, perform the steps of… (Tieng: ¶ 0109 “The aforementioned embodiments can be provided as a computer program product, which may include a machine-readable medium on which instructions are stored for programming a computer (or other electronic devices) to perform a process based on the embodiments of the present disclosure”; and ¶ 0006-0007 intelligent energy management system and methods for reducing carbon emissions in manufacturing).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20240385576 A1 to Tinaz et al. (Tinaz).
Claim 3: Tieng/Ibanez teach claim 2. With respect to the following limitation, Tieng teaches an optimization model to simulate an optimized power allocation (between a power company, an energy storage system, the renewable energy generator 4062 and a non-renewable energy generator) to meet a power demand for a factory according to additional data, the energy demand profile, and the carbon emission of equipment used in a factory process (Tieng: see ¶ 0067-0068 as above), but Tieng/Ibanez do not explicitly teach an ROI associated with one or more simulations. However, Tinaz teaches:
determining a return on investment (ROI) corresponding to each of the one or more simulations (Tinaz: ¶ 0139 “The simulations can then be run to generate a report of results of a simulation (e.g., a return on investment report)…”) based on the additional data (Tinaz: ¶ 0136 weather data), the energy demand profile, and the carbon emission of each of the one or more processes (Tinaz: ¶ 0056 “The data sources 112 can include sustainability data, for example relating to energy consumption, water consumption, or other resource consumption; carbon emissions, pollutant emissions, etc.; or green energy generation (e.g., solar generation, wind power generation, geothermal power generation)… Sustainability data can include data from resource providers…relating to the availability of renewable energy resources”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of a return on investment for each of the simulations of Tinaz in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation “to improve operations based on simulation results” (Tinaz: ¶ 0139).
Claim 13: See the rejection of claim 3 above.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20230186217 A1 to Kulkarni et al. (Kulkarni).
Claim 4: Tieng/Ibanez teach claim 1. With respect to the limitations:
further comprising: monitoring the carbon emission of each of the processes and associated assets; and
providing a feedback to the optimization model to recalculate the green energy requirement of each of the one or more processes
Tieng teaches inputting production line information and calculated carbon emissions for factory/production equipment for a production line into an optimization model to provide an optimal power distribution ratio between different energy sources including renewable energy sources (Tieng: ¶ 0067-0068) as per the rejection of claim 1 above, but Tieng/Ibanez do not explicitly teach then monitoring and updating the optimization model based on monitored carbon emissions.
However, Kulkarni teaches an optimization model that provides recommendations for reduction of carbon emissions (Kulkarni: ¶ 0015, ¶ 0020-0021, ¶ 0029, ¶ 0037), which may include to utilize green energy (Kulkarni: ¶ 0029-0031 showing suggestions to generate energy capacity from renewables, and/or find optimal energy mix between renewable and non-renewable), and updating the model according to the monitored carbon emissions on an ongoing basis to recalculate the recommendations (Kulkarni: ¶ 0035 “ jointly optimizing emissions and profits across the supply-chain spatio-temporally. Such an embodiment can include recommending one or more interventions (based, for example, on counterfactual queries) that enable the joint optimization, and continuous updating of the intervention(s) depending on the resulting emissions and how they compare against the recommended budget”; and ¶ 0038, ¶ 0042, ¶ 0044-0046 using updated carbon emissions data to re-optimize and determine new recommendations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included updating the optimization model and recalculating the recommendations using monitored carbon emissions of Kulkarni in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation to address the issue that “enterprises commonly face challenges in determining strategies that will enable attainment of stated carbon emission reduction goals in conjunction with other enterprise objectives and/or constraints” (Kulkarni: ¶ 0001) and “to jointly optimize emissions reductions and enterprise profits, and can include recommendations across one or more time-scales and one or more locations in an attempt to ensure that carbon budgets are met” (Kulkarni: ¶ 0037).
Claim 14: See the rejection of claim 4 above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20080255899 A1 to McConnell et al. (McConnell).
Claim 5: Tieng/Ibanez teach claim 2. With respect to the following limitations, while Tieng teaches execution of an optimization model, i.e. at least one simulation, as per claim 2 above, and Ibanez teaches carbon credits associated with a recommended plan (Ibanez: ¶ 0033-0034 as per claim 1), Tieng/Ibanez do not explicitly teach the following limitations. However, McConnell teaches:
further comprising: determining a carbon credit associated with the process industry (McConnell: ¶ 0144 “various systems described herein allow for tracking and verifying carbon credits awarded or otherwise earned for reducing of emissions over a predetermined period of time. Certain GHG emission reduction programs currently available to entities with GHG emitting assets and/or locations allow for accumulation of carbon credits for reducing GHG emissions by a predetermined amount for predetermined periods of time such as five years or twenty years or any such period…”); and
determining a remaining carbon credit corresponding to the one or more simulations generated by the optimization model (McConnell: ¶ 0170 “analysis software utilizes the amount to calculate an estimate of the impact of such GHG emissions reduction on the total financial costs of the site, including the impact on the energy consumption costs and the total cost of ownership of the assets at the site and the carbon credits at the site. In one embodiment, the software utilizes the predictive analysis and planning capabilities described elsewhere herein to determine the impact on the total costs of ownership as a result of any asset upgrade or modification required to achieve the emissions reduction, or any other associated cost increase, while also estimating any profits associated with an increase in the number of carbon credits accumulated as a result of the emissions reduction…”; also see ¶ 0008-0010, ¶ 0028, ¶ 0084, ¶ 0163 showing the carbon credits may be associated with an asset or an accumulated number of carbon credits associated with each asset)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of carbon credits associated with a generated predictive analysis of McConnell in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation “ there is a need in the art for a system or method for expeditiously and efficiently tracking and reporting the GHG emissions and the resulting carbon credits” (McConnell: ¶ 0006).
Claim 15: See the rejection of claim 5 above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20220058590 A1 to Phan et al. (Phan).
Claim 6: Tieng/Ibanez teach claim 1. With respect to the following limitation, Tieng/Ibanez do not explicitly teach, however, Phan teaches:
wherein the asset health index is determined using either principle component analysis or a neural network (Phan: Figure 4 and ¶ 0044-0045 showing a process for determining a risk index for equipment including a step of determining an asset health index based on data collected about equipment “E” which is analyzed using machine learning, such as an artificial neural network)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of an asset health index for industrial equipment of Phan in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation to “provide a preventive maintenance for equipment to help to gain better availability, utilization, and performance. In case of large geo-distributed industrial systems, such as utility grids for electricity, water, telecommunications, and other such services that rely on geographically distributed equipment, it is crucial to determine when to perform a preventive maintenance and a replacement for each equipment in the system. Embodiments of the present invention improve the existing methods for performing such preventive maintenance, which is performed in a time-based manner, or an ad-hoc manner based on human judgment” (Phan: ¶ 0013).
Claim 16: See the rejection of claim 6 above.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20240403895 A1 to Menon et al. (Menon).
Claim 7: Tieng/Ibanez teach claim 1. With respect to the following limitation, while Tieng teaches identifying the one or more green energy resources for acquiring the green energy by the process industry (Tieng: ¶ 0049 the renewable-energy-generator predicted power generation information, ¶ 0067-0068 and identifying optimal energy allocation ratio between renewable and non-renewable), Tieng/Ibanez do not explicitly teach determining newly available green energy resources. However, Menon teaches:
wherein identifying the one or more green energy resources for acquiring the green energy by the process industry comprises: determining one or more newly available green energy resources (Menon: ¶ 0058 showing “, the engineering workflow systems 78 may include a new energy system that tracks new energy sources that may be used to meet the energy requests of various portions of the enterprise. The new energy sources may include renewable energy sources to improve the sustainability parameters for the enterprise operations. As such, the new energy system may determine whether alternative energy sources can be used to replace energy sources that may be less sustainable”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the determination of newly available renewable energy sources of Menon in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation “to improve the sustainability parameters for the enterprise operations. As such, the new energy system may determine whether alternative energy sources can be used to replace energy sources that may be less sustainable” (Menon: ¶ 0058).
Claim 17: See the rejection of claim 7 above.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20150186904 A1 to Guha et al. (Guha).
Claim 8: Tieng/Ibanez teach claim 1. With respect to the following limitation, Tieng teaches calculating an expected amount of renewable energy generation based on environmental data, but Tieng/Ibanez do not explicitly teach predicting an availability of green energy resources based on real time weather data. However, Guha teaches:
further comprising: predicting the availability of the green energy resources based on real time weather data (Guha: ¶ 0038, ¶ 0042, ¶ 0017, ¶ 0028 determining renewable energy availability based on weather data, ¶ 0023 showing “the weather station can provide information relating to temperature, humidity, wind speed, wind direction and other weather-related factors that can affect sunlight and/or wind source conditions. The information from the weather station would be real-time information”; also see claim 4 of Guha showing “using the weather data to predict the availability of the power from the renewable energy source during the given timeframe”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included determining the availability renewable energy sources based on weather data of Guha in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation “to maximize the energy use from this type of renewable energy source by directly tying the supply with the demand using forecasting techniques to predict the available renewable energy sources” (Guha: ¶ 0017).
Claim 18: See the rejection of claim 8 above.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20250192550 A1 to Tieng et al. (Tieng) in view of US 20240311733 A1 to Ibanez et al. (Ibanez), and further in view of US 20240084783 A1 to Moser et al. (Moser).
Claim 9: Tieng/Ibanez teach claim 1. With respect to the following limitation, Tieng/Ibanez do not explicitly teach, however, Moser teaches:
further comprising: determining the remaining useful life of the asset by using a Bayesian regression algorithm (Moser: ¶ 0028, ¶ 0035 showing using machine learning model to estimate the remining useful life of equipment; ¶ 0111-0113 showing using a regularization technique on the data set to train a machine learning model, including a Bayesian regression technique)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included determining the a remaining useful life using Bayesian regression of Moser in the intelligent energy management system of Tieng/Ibanez with a reasonable expectation of success of arriving at the claimed invention, with the motivation that “a remaining useful life […] can then in particular be determined, in particular predicted with an increased accuracy” (Moser: ¶ 0026), and also that “in order to obtain a high-quality machine learning model, it has been recognized that a so-called overfitting should be avoided during the training process” (Moser: ¶ 0111).
Claim 19: See the rejection of claim 9 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HUNTER MOLNAR/Examiner, Art Unit 3628
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