DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the reply filed March 22, 2026.
Claims 1, 11, 18, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
Response to Arguments
As discussed in non-obvious subject matter subject matter below, the previous rejection under 35 USC 103 has been withdrawn in response to the submitted amendments.
Applicant’s arguments filed March 22, 2026 have been fully considered but they are not persuasive.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
The Applicant respectfully submits that one or more features of amended independent claim 1 is not directed to a mathematical concept under because they do not recite a mathematical formula, purely numerical calculation, or abstract data transformation in isolation. Although the claims utilize a machine learning model and an optimization model, these computational techniques are recited as part of a larger technological process for controlling emissions of physical assets, rather than as ends in themselves. Further, the machine learning and optimization models are implemented within an emissions system that receives sensor-derived operational data from real-world assets, accounts for uncertainty in asset behavior, and generates emissions optimization actions that affect how the assets operate. The mathematical processing performed by the models is therefore constrained to a specific industrial and environmental application, namely emissions control under uncertainty.
Moreover, in the claimed subject matter, the results of the optimization are not merely calculated or displayed, but are used to generate long-term and short-term emissions control outputs that influence asset operation. In particular, the long-term emissions optimization plan is required to indicate whether assets are on track to achieve net zero emissions and to comprise long-term emissions optimization actions configured to cause the assets to change their emissions trajectory.
Examiner respectfully disagrees. Regarding a mathematical formula, purely numerical calculation, or abstract data transformation in isolation, the mere presence of additional elements to not prohibit the finding of a mathematical concept.
Constraining the claims to a specific industrial and environmental application is merely limiting the claims to a field of use that does not render the claims any less abstract. See Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1259 (Fed. Cir. 2016) (merely limiting the field of use of the abstract idea to a particular … environment does not render the claims any less abstract). Regarding the particularity of the claims, merely reciting claims that are a narrow application of the identified idea is not sufficient to recite patent eligible subject matter. See Electronic Communication v. Shopperschoice.com, LLC, 958 F.3d 1178, 1183 (Fed. Cir. 2020) (“patent eligibility turns on the content of the claims, not merely on the number of words recited in the claims”); BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1287 (Fed. Cir. 2018) (“a claim is not patent eligible merely because it applies an abstract idea in a narrow way”).
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
The claim is also not directed to a method of organizing human activity, such as managing human interactions, business practices, or economic behavior. The claimed steps are machine-executed operations involving sensors, machine learning models, and automated signal transmission to assets. The claimed optimization is concerned with emissions trajectories and operational feasibility of physical assets, and the initiation and execution of emissions optimization actions are performed automatically by machines without human organization or intervention. No step of the claim recites human decision-making, human behavior, or rules for organizing people or business relationships. Instead, the method addresses a technological problem controlling and reducing emissions from physical assets in the presence of uncertainty using a technological solution implemented by computing systems interacting with physical infrastructure. The long-term and short-term emissions planning recited in the claims is therefore technical planning of asset operation, not organizational planning of human activity.
Importantly, the claims require initiating performance of emissions optimization actions on the assets and automatically transmitting commands to adjust operations of the assets, which further distinguishes the claimed subject matter from abstract organizational methods. The emissions system is designed to automatically act on assets based on calculated emissions outcomes, thereby closing the loop between sensing, computation, and operational adjustment. This end-to-end interaction with physical assets confirms that the method is rooted in technology and directed to improving the way emissions-producing assets are operated over time, rather than to organizing human behavior or carrying out a business method.
Examiner respectfully disagrees. As discussed in the rejection below, the sensors, machine learning, and transmission of data are all generic computer functions. The claims only require transmitting of commands but does not actually require the autonomous control discussed in SME examples 45 and 46. In other words, transmitting instructions for control, as claimed, is not the same as executing those controls. Instead, the identified improvements argued by Applicant are really, at best, improvements to the performance of the abstract idea itself (e.g. improvements made in the underlying business method) and not in the operations of any additional elements or technology. For example, in Trading Tech, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Trading Technologies Int’l v. IBG LLC, 921 F.3d 1084, 1093-94 (Fed. Cir. 2019).
The claimed computer components have not been improved by the automation of the manual process. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential), has been found by the courts not to be sufficient to show an improvement in a computer-functionality.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
When viewed as a whole, the claims recite a concrete, technology-based process for emissions control and optimization, in which machine learning and optimization models are tools used to achieve a physical result, namely, adjusting asset operations so that emissions targets are met. The claims therefore do not preempt a mathematical concept or a method of organizing human activity, but instead are directed to a specific technological application that integrates computation with real-world asset operation. See at least paragraphs [0029]-[0033], [0041]-[0042], [0056], [0062], [0064] of originally filed Specification.
Therefore, the subject matter of independent claim 1 is not similar to alleged abstract idea. Clearly, the above-mentioned features cannot be regarded as certain methods that can be performed in human mind under Prong 1 of Step 2A.
Examiner respectfully disagrees. The court in Smart Sys. Innovations reiterated the Court in Alice by stating that claims are necessarily performed in the physical rather than the conceptual, realm … is beside the point. See Smart Sys. Innovations, LLC v. Chi. Transit Auth., 873 F.3d 1364, 1373 (Fed. Cir. 2017). The claims only require transmitting of commands but does not actually require the autonomous control discussed in SME examples 45 and 46. In other words, transmitting instructions for control, as claimed, is not the same as executing those controls.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
Regarding Prong 2 of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong 1 of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation. For instance, the subject matter of independent claim 1 can be practically realized in for instance, plants e.g., industrial plants and buildings. Generally, the industrial plants and buildings account for approximately 60 percent of total carbon dioxide and/or other greenhouse gas emissions. In light of this, many enterprises have made sustainability commitments to their shareholders, customers, regulators, employees, and/or the public in which the enterprises have committed to achieving net zero emissions by a planned date. In order to achieve net zero emissions, enterprises have implemented emissions control, emissions plans, and emissions optimizations which implement actions, detail actions and/or detail intermediate goals to achieve net zero emissions. As such, it is necessary for enterprises to regularly track their emissions to ensure that the enterprises are adhering to their emissions control, emissions plans, and emissions optimizations and will reach net zero emissions by their planned date. Further, the proposed method is implemented using a combination of hardware and software, including sensors that collect operational data from real-world assets, machine learning models that classify and forecast variables, and optimization models that generate actionable emissions plans. Also, the emissions system is configured to initiate performance of emissions optimization actions and automatically transmitting commands to adjust operations of the assets. See at least paragraphs [0029]-[0033] of originally filed Specification. This accounts for practical implementation.
Examiner respectfully disagrees. Examiner notes that “practical implementation” or being “practically realized” is not enumerated as a consideration in the practical application analysis. See MPEP 2106.04(d)(I). Industrial plants and buildings have not been claimed and therefore do not amount to the use of a particular machine or manufacture. As discussed in the rejection the sensors and other claimed hardware are generic components that do not amount to a practical application or significantly more than the identified abstract idea. The claimed machine learning is recited at a high level and does not amount to a practical application or significantly more. See Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. ("Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.") Finally, the control signals are only transmitted to assets but there is no actual machine control in the claim. Transmitting data is a generic computer function and, without the control of a machine in the claim, a control signal is just a type of data that is transmitted.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
The Applicant asserts challenge associated with effectively managing, optimizing, and planning emissions for industrial assets and buildings, especially in the face of uncertain variables and the need to achieve net zero emissions by a target date. Traditional approaches are manual, error-prone, and lack the ability to integrate real-time data, forecast uncertainties, or provide adaptive control, making it difficult for enterprises to reliably meet sustainability goals. The solution proposed in the claim ingests operational data from sensors associated with physical assets, uses a machine learning model to separate the operational data into controllable operational parameters of asset components and uncertain variables, generates forecasting models that quantify uncertainty for the uncertain variables, and applies the operational data and uncertainty- aware predictions to an optimization model to generate a long-term emissions optimization plan and short-term emissions control. The long-term optimization plan not only determines whether the assets are on track to meet a net-zero target by a specified date. The long-term optimization plan is then used to derive a short-term emissions control layer that optimizes emissions behavior over a short time horizon in a manner that is consistent with the long-term trajectory, rather than seeking instantaneous emissions minimization. The long-term optimization actions and short-term optimization control are configured to change the emissions trajectory, while the system further initiates performance of emissions optimization actions and automatically transmits commands to adjust operations of the assets.
The advantage of this approach is that this is a closed loop uncertainty-aware emissions control and planning for real assets, because it does not rely on static emissions estimates or manual planning. By explicitly distinguishing controllable operational parameters from uncertain variables, and by generating predictions associated with uncertainty before optimization, the system can produce plans and controls that are robust to variability and can continuously guide asset operations toward a long-term net-zero objective. Further, because the method includes initiating performance of optimization actions and automatically transmitting commands that adjust asset operations, the system reduces the latency and error associated with manual intervention and enables emissions outcomes to be influenced through actual operational adjustments, rather than merely reporting or recommending actions. The technical result is improved operational feasibility and reliability of meeting emissions targets over time, because decisions are generated from real sensor data, incorporate quantified uncertainty, and are tied to actionable operational changes on the assets. See at least paragraphs [0002], [0029]-[0033], [0004]-[0006], [OOSS]-[0066], [0066]-[0075], [0088]-[0094], [00127]-[00166], [0088]- [0094] of originally filed Specification.
Examiner respectfully disagrees. The identified improvements argued by Applicant are really, at best, improvements to the performance of the abstract idea itself (e.g. improvements made in the underlying business method) and not in the operations of any additional elements or technology. See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245, 1251 (Fed. Cir. 2021) (“[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological process; rather, it is merely an enhancement to the abstract mathematical calculation … itself.”); Parker v. Flook, 437 U.S. 584, 591-92 (1978) ("the novelty of the mathematical algorithm is not a determining factor at all")
As discussed in the rejection the sensors and other claimed hardware are generic components that do not amount to a practical application or significantly more than the identified abstract idea. The claimed machine learning is recited at a high level and does not amount to a practical application or significantly more. See Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. ("Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.") Finally, the control signals are only transmitted to assets but there is no actual machine control in the claim. Transmitting data is a generic computer function and, without the control of a machine in the claim, a control signal is just a type of data that is transmitted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Alice/Mayo Framework Step 1:
Claims 1-10 recite a series of steps and therefore recite a process.
Claims 11-19 recite a combination of devices and therefore recite a machine.
Claims 20 recite a tangible article given properties through artificial means and therefore recite a manufacture.
Alice/Mayo Framework Step 2A – Prong 1:
Claims 1, 11, and 20, as a whole, are directed to the abstract idea of identifying variables that influence emission and accounting for the identified variables in order to reduce emissions, which is a mathematical concept and a method of organizing human activity. The claims recite a mathematical concept because the identified idea is a mathematical calculation by reciting forecasting uncertain variables, optimizing operational data, and generating operational controls. See MPEP 2106.04(a)(2)(I)(C). The claims recite a method of organizing human activity because the identified idea is a fundamental economic principles or practices (including hedging, insurance, mitigating risk) by reciting optimizing assets to operate with fewer emissions. See MPEP 2106.04(a)(2)(II)(A). The mathematical concept and method of organizing human activity of “identifying variables that influence emission and accounting for the identified variables in order to reduce emissions,” is recited by claiming the following limitations: receiving operation data, classifying operational data, generating forecasting models to predict uncertain variables, generating an optimization model, generating short term control, initiating optimization actions. The mere nominal recitation of a processor, a non-transitory memory, operational sensors, transmitting signals to perform actions, and a non-transitory computer-readable storage medium does not take the claim of the mathematical concept or method of organizing human activity grouping. Thus, the claim recites an abstract idea.
With regards to Claims 4 and 14, the claims further recite the above-identified judicial exception (the abstract idea) by reciting the following limitations: receiving updated operational data, generating an adjusted long-term emissions plan, generating an adjusted short-term control, and initiating performance of adjusted emissions optimization actions.
Alice/Mayo Framework Step 2A – Prong 2:
Claims 1, 11, and 20 recite the additional elements: a processor, a non-transitory memory, operational sensors, transmitting signals to perform actions, and a non-transitory computer-readable storage medium. These processor, non-transitory memory, transmitting signals to perform actions, and non-transitory computer-readable storage medium limitations are no more than mere instructions to apply the exception using a generic computer component. The receiving operational sensor data step is recited at a high level of generality (i.e., as a general means of gathering operational data), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The transmitting signals to perform actions step is recited at a high level of generality (i.e., as a general means of outputting the results of the abstract idea), and amounts to an insignificant application, which is a form of insignificant extra-solution activity. Taken individually these 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.
Considering the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception does not amount to a practical application of the abstract idea. The claim as a whole does not improve the functioning of a computer or improve other technology or improve a technical field. The claim as a whole is not implemented with a particular machine. The claim as a whole does not effect a transformation of a particular article to a different state. The claim as a whole is not applied in any 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. The claim as a whole merely describes how to generally “apply” the concept of emissions planning in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing energy audit process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim is directed to the abstract idea.
Alice/Mayo Framework Step 2B:
Claims 1, 11, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite a generic computer performing generic computer function by reciting a processor, a non-transitory memory, and a non-transitory computer-readable storage medium. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1341 (describing a “processor” as a generic computer component); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347–48 (discussing the same with respect to “data” and “memory”). The claims recite the following computer functions recognized by the courts as generic computer functions by reciting receiving and transmitting information (See MPEP 2106.05(d)(II) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec; TLI Communications LLC; OIP Techs.; buySAFE, Inc.), processing information (See MPEP 2106.05(d)(II) performing repetitive calculations, Flook; Bancorp Services). The specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a processor (Specification [0048]), a non-transitory memory (Specification [0051]), operational sensors (Specification [0042]), and a non-transitory computer-readable storage medium (Specification [0051]). See MPEP 2106.05(d)(I)(2). The claims add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a processor, a non-transitory memory, transmitting signals to perform actions, and a non-transitory computer-readable storage medium. See MPEP 2106.05(f). The claims recite insignificant extrasolution activity (i.e. mere data gathering, selecting a particular data source or type of data to be manipulated, or an insignificant application) by reciting receiving data from operational sensors and transmitting signals to perform actions. See MPEP 2106.05(g). The claims limit the field of use by reciting emissions from assets. See MPEP 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements alone, and in combination, that are sufficient to amount to significantly more than the recited judicial exception.
With regards to Claims 10, the additional elements do not amount to significantly more than the judicial exception. Claims 10 recites instructions to implement the abstract idea on a computer by providing a user interface, and responding to a user interface using the computer's ordinary ability to display and process data inputs. (See MPEP 2106.05(f) accessing information through a mobile interface Intellectual Ventures v. Erie Indem. Co.; Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc.) Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exception.
Remaining Claims:
With regards to Claims 2-3, 5-9, 12-13, and 15-19, these claims merely add a degree of particularity to the limitations discussed above rather than adding additional elements capable of transforming the nature of the claimed subject matter. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims as a whole do not amount to significantly more than the abstract idea itself.
Non-Obvious Subject Matter
The following is a statement of reasons for the indication of non-obvious subject matter:
The closest prior art already made of record is Avadhani et al. (U.S. P.G. Pub. 2023/0115876 A1), Kumar et al. (U.S. P.G. Pub. 2022/0327538 A1), and Morris et al. (U.S. P.G. Pub. 2024/0005223 A1).
In addition to the closest prior art of record Madigan (U.S. P.G. Pub. 2024/0139788 A1), Ramsoy et al. (U.S. P.G. Pub. 2017/0308802 A1), and McMahon et al. (U.S. P.G. Pub. 2016/0048766 A1) are made of record.
Madigan discloses that emissions below zero are tracked as credits to offset other unrelated emissions but does not propose increasing emissions to achieve net zero emissions (Madigan [0097]).
Ramsoy discloses classifying input operation data to produce outcome variable s relating to equipment failures, maintenance needs, etc., and an uncertainty factor for the outcome variable (Ramsoy [0063]).
McMahon discloses using a classifier to produce outcome variables and an uncertainty factor for the variables (McMahon [0007]).
It would not have been obvious to one of ordinary skill in the art to have combined the closest prior art of record to disclose, teach, or suggest the claimed combination of limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Madigan (U.S. P.G. Pub. 2024/0139788 A1), Ramsoy et al. (U.S. P.G. Pub. 2017/0308802 A1), and McMahon et al. (U.S. P.G. Pub. 2016/0048766 A1)
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/SCOTT M TUNGATE/Primary Examiner, Art Unit 3628
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