DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-3 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure Statement(s) filed 08/14/2025 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, and fails step 2 of the analysis because the focus of the claims is not on the devices themselves or a practical application but rather directed towards an abstract idea, the analysis is provided below.
Step 1 (Statutory Categories) - The claims pass step 1 of the subject matter eligibility test (see MPEP 2106(III)) as the claims are directed towards a system.
Step 2A – Prong One (Do the claims recite an abstract idea?) - The idea is recited in the claims, in part, by:
collect energy consumption data and CO2 emission data in real time,
store and preprocess the received data,
analyze the preprocessed data and generate a renewable energy investment proposal, and
generate an investment proposal report and provide said report to a user.
The steps recited above under Step 2A Prong One of the analysis under the broadest reasonable interpretation covers commercial or legal interactions (including marketing or sales activities or behaviors; business relations) but for the recitation of generic computer components. That is other than reciting a processor, an IoT terminal, a cloud server, and generative AI nothing in the claim elements are directed towards anything other than commercial or legal interactions for analyzing energy consumption and CO2 emissions and generating and providing a renewable energy investment proposal report based on the analysis. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A – Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) - This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a processor, an IoT terminal, a cloud server, and generative AI. The processor, IoT terminal, cloud server, and generative AI are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components and limits the judicial exception to the particular environment of computers. Mere instructions to apply the judicial exception using generic computer components and limiting the judicial exception to a particular environment are not indicative of a practical application (see MPEP 20106.05(f) and MPEP 20106.05(h)). As MPEP 2106.05(f) 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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone);. Further, with respect to the use of generative AI, this is akin to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), where Courts found the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment and that the requirements that the machine learning model be “iteratively trained” or dynamically did not represent a technological improvement as iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Accordingly, 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. The claims are directed towards an abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) - The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, with respect to integration of the abstract idea into a practical application, using the additional elements of processor, IoT terminal, cloud server, and generative AI to perform the steps recited in Step 2A Prong One of the analysis amounts to no more than mere instructions to apply the exception using generic computer components and limits the judicial exception to the particular environment. Mere instructions to apply an exception using generic computer components and limiting the judicial exception to a particular environment does not provide an inventive concept. The additional elements have been considered separately, and as an ordered combination, and do not add significantly more (also known as an “inventive concept”) to the judicial exception. Further, MPEP 2106.05(d)(ii) provides that receiving and transmitting data over a network (see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."), are well-understood routine and conventional, similar to the instant application claims which recites and collecting and storing on a cloud server and analyzing the data (i.e. performing repetitive calculations). See also Electric Power Group, in which the Courts found the claims ineligible, particularly claims to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);, similar to the instant application which is directed towards analyzing energy consumption and CO2 emissions and generating and providing a renewable energy investment proposal report. The claims are not patent eligible.
The dependent claims have been given the full analysis including analyzing the additional limitations both individually and in combination as a whole. For instance, claims 2 and 3 further define the abstract idea are all steps that fall within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas, generally linking the use of the judicial exemption to a particular technical computing environment. The Dependent claims when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 for the same reasoning as above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cella, et al. (US Patent Application Publication 20260118837), “Cella”.
As per claim 1, Cella discloses:
A system comprising a processor, [0576], [01176]
wherein the processor is configured to collect energy consumption data and CO2 emission data in real time by an IoT terminal, [0517] The set of intelligent data layers 130, the set of intelligence enablement systems 112 more generally, and/or the configurable data and intelligence modules and services 118 may access data from various sources throughout the platform 102 and, in embodiments, may operate from the set of shared data resources, which may be contained in a centralized database and/or in a set of distributed databases, or which may consist of a set of distributed or decentralized data sources, such as IoT or edge devices that produce energy-relevant event logs or streams. The set of intelligent data layers 130 may be configured for a wide range of energy-relevant tasks, such as prediction/forecasting of energy consumption, generation, storage or distribution parameters (e.g., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery, configuration and/or execution of energy transactions (including microtransactions and/or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and/or storage (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (e.g., messaging to prompt or promote favorable user behavior); and many others.
store and preprocess the received data on a cloud server, [0010], [0517] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to perform one or more of, extracting energy-related data, detecting and/or correcting errors in energy-related data, transforming, converting, normalizing, and/or cleansing energy-related data, parsing energy-related data, detecting patterns, content, and/or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and/or storing energy-related data, routing and/or transporting energy-related data, or maintaining security of energy-related data…. The set of intelligent data layers 130 may be configured for a wide range of energy-relevant tasks, such as prediction/forecasting of energy consumption, generation, storage or distribution parameters (e.g., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery, configuration and/or execution of energy transactions (including microtransactions and/or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and/or storage (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (e.g., messaging to prompt or promote favorable user behavior); and many others.
analyze the preprocessed data and generate a renewable energy investment proposal by a generative Al, and [0589][0593], [01180] The set of configured stakeholder energy edge solutions 108 may also include a set of enterprise optimization solutions 154, such as to provide an enterprise with greater visibility into the role that energy plays in enterprise operations (such as to enable targeted, strategic investment in energy-relevant assets)… The set of enterprise optimization solutions 154 may include a set of enterprise energy digital twin solutions 520, where the platform 102 may be used to collect, monitor, store, process and represent in a digital twin a wide range of data representing states, conditions, operating parameters, events, workflows and other attributes of energy-relevant entities, such as assets of the enterprise involved in operations, assets of external entities that are relevant to the energy utilization or transactions of the enterprise (e.g., energy grid entities, pipelines, charging locations, and the like), energy market entities (e.g., counterparties, smart contracts, blockchains, prices and the like). A user of the set of enterprise energy digital twin solutions 520 may, for example, view a set of factories that are consuming energy and be presented with a view that indicates the relative efficiency of each factory, of individual machines within the factory, or of components of the machines, such as to identify inefficient assets or components that should be replaced because the cost of replacement would be rapidly recouped by reduced energy usage. The digital twin, in such example, may provide a visual indicator of inefficient assets, such as a red flag, may provide an ordered list of the assets most benefiting from replacement, may provide a recommendation that can be accepted by the user (e.g., triggering an order for replacement), or the like….In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them. However, the output layer in the auto-encoder may have the same number of units as the input layer, where the purpose of the MLP neural network is to reconstruct its own inputs (rather than just emitting a target value). Therefore, the auto encoders may operate as an unsupervised learning model. An auto encoder may be used, for example, for unsupervised learning of efficient codings, such as for dimensionality reduction, for learning generative models of data, and the like. In embodiments, an auto-encoding neural network may be used to self-learn an efficient network coding for transmission of analog sensor data from a machine over one or more networks or of digital data from one or more data sources. In embodiments, an auto-encoding neural network may be used to self-learn an efficient storage approach for storage of streams of data.
generate an investment proposal report and provide said report to a user. [0589], [0593] The set of configured stakeholder energy edge solutions 108 may also include a set of enterprise optimization solutions 154, such as to provide an enterprise with greater visibility into the role that energy plays in enterprise operations (such as to enable targeted, strategic investment in energy-relevant assets)… The set of enterprise optimization solutions 154 may include a set of enterprise energy digital twin solutions 520, where the platform 102 may be used to collect, monitor, store, process and represent in a digital twin a wide range of data representing states, conditions, operating parameters, events, workflows and other attributes of energy-relevant entities, such as assets of the enterprise involved in operations, assets of external entities that are relevant to the energy utilization or transactions of the enterprise (e.g., energy grid entities, pipelines, charging locations, and the like), energy market entities (e.g., counterparties, smart contracts, blockchains, prices and the like). A user of the set of enterprise energy digital twin solutions 520 may, for example, view a set of factories that are consuming energy and be presented with a view that indicates the relative efficiency of each factory, of individual machines within the factory, or of components of the machines, such as to identify inefficient assets or components that should be replaced because the cost of replacement would be rapidly recouped by reduced energy usage. The digital twin, in such example, may provide a visual indicator of inefficient assets, such as a red flag, may provide an ordered list of the assets most benefiting from replacement, may provide a recommendation that can be accepted by the user (e.g., triggering an order for replacement), or the like.
As per claim 2, Cella discloses:
wherein the processor is further configured to perform integrity verification of the energy consumption data and CO2 emission data, and to correct abnormal values. [0010], [0517] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to perform one or more of, extracting energy-related data, detecting and/or correcting errors in energy-related data, transforming, converting, normalizing, and/or cleansing energy-related data, parsing energy-related data, detecting patterns, content, and/or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and/or storing energy-related data, routing and/or transporting energy-related data, or maintaining security of energy-related data… The set of intelligent data layers 130 may be configured for a wide range of energy-relevant tasks, such as prediction/forecasting of energy consumption, generation, storage or distribution parameters (e.g., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery, configuration and/or execution of energy transactions (including microtransactions and/or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and/or storage (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (e.g., messaging to prompt or promote favorable user behavior); and many others.
As per claim 3, Cella discloses:
wherein the processor is further configured to monitor the post-installation performance of a renewable energy system and provide feedback. [0593], [0660], [0841], see also [0522-524] The set of enterprise optimization solutions 154 may include a set of enterprise energy digital twin solutions 520, where the platform 102 may be used to collect, monitor, store, process and represent in a digital twin a wide range of data representing states, conditions, operating parameters, events, workflows and other attributes of energy-relevant entities, such as assets of the enterprise involved in operations, assets of external entities that are relevant to the energy utilization or transactions of the enterprise (e.g., energy grid entities, pipelines, charging locations, and the like), energy market entities (e.g., counterparties, smart contracts, blockchains, prices and the like). A user of the set of enterprise energy digital twin solutions 520 may, for example, view a set of factories that are consuming energy and be presented with a view that indicates the relative efficiency of each factory, of individual machines within the factory, or of components of the machines, such as to identify inefficient assets or components that should be replaced because the cost of replacement would be rapidly recouped by reduced energy usage. The digital twin, in such example, may provide a visual indicator of inefficient assets, such as a red flag, may provide an ordered list of the assets most benefiting from replacement, may provide a recommendation that can be accepted by the user (e.g., triggering an order for replacement), or the like. Digital twins may be role-based, adaptive based on context or market conditions, personalized, augmented by artificial intelligence, and the like, in the many ways described herein and in the documents incorporated by reference herein… The data collection system may dynamically update the collection of historical, current, and/or forecast energy demand parameters (e.g., reconfiguring sensors to collect different forms of data for a particular type of energy demand). Alternatively or additionally, the data collection system may dynamically update the storage of historical, current, and/or forecast energy demand parameters (e.g., reprocessing and modifying stored data to increase, decrease, annotate, summarize, and/or transform the stored data for a particular type of energy demand). Alternatively or additionally, the data collection system may dynamically update the presentation of historical, current, and/or forecast energy demand parameters (e.g., changing the type, amount, and/or structure of data reported for a particular type of energy demand).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY S CUNNINGHAM II whose telephone number is (313)446-6564. The examiner can normally be reached Mon-Fri 8:30am-4pm.
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GREGORY S. CUNNINGHAM II
Primary Examiner
Art Unit 3694
/GREGORY S CUNNINGHAM II/Primary Examiner, Art Unit 3694
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