DETAILED ACTION
Status of Claims
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-Final office action in response to communication received on May 05, 2026. Claims 1-20 are pending and examined herein.
Claim Rejections - 35 USC § 101
2. 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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-14 are a process; claims 15-19 are a process; and claim 20 is a non-transitory CRM. Thus, each claim 1-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of rewarding eco-friendly product purchases which is certain methods of organizing human activity (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least are a data-centric artificial intelligence (AI) module(s), knowledge graph(s), wherein the knowledge graph is trained using a hypergraph neural network to encode high-order data correlations, an eco-crypto token generated and collecting energy-saving data based on data-centric AI module and knowledge graph, an eco-crypto rewards platform and generating a unique eco-crypto token, encrypting the eco-crypto token with homomorphic encryption, providing via a platform, and monitoring to alert based on data discrepancy (per claims 1, 15, and 20); a non-transitory machine-readable medium storing instructions for generating synthetic eco-crypto tokens for carbon credits that, when executed by one or more processors, cause the one or more processors to perform steps comprising (additionally per claim 20); collecting data via using an AI data collector integrated into the data-centric AI module (per claim 2); generating dynamic smart contracts to automatically adjust the eco-crypto token (per claim 4); generating a dynamic smart contract via a dynamic smart contract engine (per claims 6 and 19); eco-token is configured for use with another cryptocurrency or another cryptocurrency platform (claim 10); knowledge graph is trained continuously using a Hypergraph Neural Network (HGNN) framework to adapt to new energy-saving data, enabling dynamic and accurate allocation of carbon credits (per claim 11) and service is designing, developing, testing, updating, or maintaining software (per claim 14). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements as tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)) including AI, Knowledge-graph trained using a hyper neural network, and encryption applied to eco-crypto token to keep track of a particular user’s rewards. The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1-2 and their associated disclosure. The processor executing the "apply it" instruction is further connected to one or more device merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) to transmit requisite energy-savings data and onboarding user on the rewards platform. Collecting energy-savings data to compute carbon credits or savings to provide rewards is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes collected and transmitted user data to ascertain how much carbon savings can be attributed to a eco-friendly product purchased by a user in order to provide a reward which is registered in blockchain ledger using unique user token to keep track of user’s accumulated rewards. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is amount of rewards accumulated by a user for purchasing eco-friendly products (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data and onboarding a customer via a network based communication environment e.g. Internet and storing rewards in a ledger per smart contract, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to one or more particular technological environments (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 explained above.
Thus, the abstract idea of rewarding eco-friendly product purchases which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of rewarding eco-friendly product purchases which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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) [similarly here user's data is received and per industry carbon savings data is transmitted over a network]; and
(ii) Collecting and analyzing information to detect misuse and notifying a user when misuse is detected (FairWarning); Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution notification about discrepancy are provided and rewards are stored in a ledger post successful validation].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
1. The Examiner provides citation to one or more of the court decisions as noting the well-understood, routine, conventional nature of the additional element(s) as follows:
(i) Higher order data encoding in machine learning
- NPL: https://medium.com/@debisreer/encoding-in-machine-learning-8e7b11857daa
-NPL: https://machinelearningmastery.com/one-hot-encoding-for-categorical-data/
-NPL:https://towardsdatascience.com/3-key-encoding-techniques-for-machine-learning-a-beginner-friendly-guide-aff8a01a7b6a/
- CN120978711A note "The invention integrates the power system domain knowledge (such as tide equation, safety constraint) and the data driving model to construct the hybrid intelligent framework. the hypergraph neural network layer is responsible for modelling the high-order relationship among multiple nodes, each hypergraph neural network layer is connected with multiple nodes for representing the complex group interaction. the space-time attention layer enhances the sensing ability of the model to the key space-time mode, and dynamically adjusts the important weight of different characteristics and time steps. Application of a two-stage optimizer training model: the first stage uses the improved particle swarm algorithm to carry out global exploration in the large-scale solution space and quickly locate the potential optimal solution area; the second stage uses the optimization method based on gradient to perform local refinement and fine tuning the model parameter. The model training adopts distributed computing frame, supporting increment learning and online updating, keeping the timeliness of the model."; and "green energy integration: the user participates in the renewable energy consumption to obtain the points and can exchange the preferential or service."
(ii) Homomorphic Encryption
- Sarah Lee, Homomorphic Encryption: A Mathematical Revolution Uncovering the Historical Development and Significance of Homomorphic Encryption, 05/28/2025, Number Analytics,
https://www.numberanalytics.com/blog/homomorphic-encryption-history-mathematics
- Asante et al., Evolution of Homomorphic Encryption, https://www.ijcaonline.org/archives/volume183/number29/asante-2021-ijca-921679.pdf
- Homomorphic Encryption: How to Process Data Without Ever Decrypting It
https://cis2019.com/pages/homomorphic-encryption-how-to-process-data-without-ever-decrypting-it
- Allowing Computation on Encrypted Data Without Decryption: A Comprehensive Guide to Fully Homomorphic Encryption (FHE) Introduction
https://modern-cryptography.com/allowing-computation-on-encrypted-data-without-decryption-a-comprehensive-guide-to-fully-homomorphic
(iii) Executing smart contracts and electronic record-keeping
- Youb et al. Pub. No.: US 2019/0080392 note "[0009] So-called "Smart Contracts" are legal obligations tied to a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of the contracts. Smart contracts allow the performance of credible transactions without third parties. These transactions are trackable and may be irreversible. See, en.wikipedia.org/wiki/Smart contract. The phrase "smart contracts" was coined by computer scientist Nick Szabo in 1996. See, "Nick Szabo--Smart Contracts: Building Blocks for Digital Markets". www.fon.hum.uva.nl, Extropy #16; Szabo, Nick (1997 Sep. 1). "Formalizing and Securing Relationships on Public Networks". First Monday. 2 (9); Tapscott, Don; Tapscott, Alex (May 2016). The Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business, and the World. pp. 72, 83, 101, 127. ISBN 978-0670069972. A smart contract is a set of promises, specified in digital form, including protocols within which the parties perform on these promises. Recent implementations of smart contracts are based on blockchains, though this is not an intrinsic requirement. Building on this base, some recent interpretations of "smart contract" are mostly used more specifically in the sense of general purpose computation that takes place on a blockchain or distributed ledger. In this interpretation, used for example by the Ethereum Foundation or IBM, a smart contract is not necessarily related to the classical concept of a contract, but can be any kind of computer program. See, Buterin, Vitalik. "Ethereum Whitepaper", Github; Cachin, Christian. "Architecture of the Hyperledger Blockchain Fabric", ibm.com."; [0010]-[0050]; [0051] Peters, Gareth W., and Efstathios Panayi. "Understanding modern banking ledgers through blockchain technologies: Future of transaction processing and smart contracts on the internee of money." In Banking Beyond Banks and Money, pp. 239-278. Springer, Cham, 2016; [0052]-[0065].
- Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755; and 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 [similarly here unique user token is utilized to keep track of rewards earned and utilized in transactions, which are recorded in a ledger];
(iv) The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of knowledge graphs as follows:
- Zhang et al., Deep Hypergraph Structure Learning, August 26, 2022, https://arxiv.org/pdf/2208.12547
- Liu et al., Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding, May 20-25, 2024, https://aclanthology.org/2024.lrec-main.1367.pdf
- Samadrita Ghosh, Knowledge Graphs - What, Why, and How, Dec 19, 2022, Medium, https://samadritaghosh.medium.com/knowledge-graphs-what-why-and-how-84f920316ca5
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) for Non-applicability of Prior Art Based Rejection
3. As per claims 1-14 and 20, they are allowable over the prior art as a prima facie case of obviousness could not be established.
As per claims 15-19 were previously rejected under 35 U.S.C. 103(a) as being unpatentable over MacArthur et al. (Pub. No.: US 2022/0156754) referred to hereinafter as MacArthur, in view of Yantis et al. (Pub. No.: US 2022/0058633) referred to hereinafter as Yantis.
The closest prior art is as noted above with regard to claims 15-19 and the following that were discovered:
*Being noted initially:
- Patent No.: US10769700 see Abstract "A method implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable medium being operable to provide a shopping cart for a shopper, and segment items in the shopping cart based on the carbon footprint associated with each item. Additionally, the method may include displaying substitute or complementary items in the shopping cart for selection by the user to reduce total carbon values, and awarding carbon certificates."
- U NPL: Kouhizadeh, M., Sarkis, J., & Zhu, Q. (2019). At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion. Applied Sciences, 9(8), 1712. https://doi.org/10.3390/app9081712, see 4.3.2. Consumer Knowledge and Behavior note "Consumers can track product life cycle information, form knowledge, and adjust their behavior based on the available real-time information. Companies can also address proper reverse logistics strategies by using blockchain information regarding consumer demands, actions, loyalty, and post-purchase behavior. Being aware and confident of certain green product characteristics can help motivate purchase behavior for sustainable products. The transparency and traceability of blockchain technology can greatly improve this confidence; circular economy characteristics such as recycled materials can increase the confidence of an environmentally sustainable product.
Blockchain can be used to incentivize products returns to the supply chain. For instance, those consumers who return products at the end of the life cycle can be rewarded by cryptocurrency tokens. This can stimulate the circularity of products and provide integrated information about the performance of the returning programs and funding management for these programs. The incentive systems also help identify those products with low returning rates which can be candidates for deletion, because they do not provide value for circular economy purposes. Traceability of information allows companies to identify products and materials that are collected by poor people and informal markets for recycling purposes and secondary markets. Deleting those products may conflict with moral values that should be considered in product deletion decision making.
In each case, we provided some examples of how the nexus can work together at multiple levels of CE. These activities and characteristics are exemplary. Many additional and emergent issues, as well as broader categories, also exist."; see 5.1. Research and Theoretical Implications note "Finally, an example of a theoretical implication at the micro level is how individual consumerism and motivation relates to CE and information on product deletion. Numerous consumer theories exist [76], and motivation theory is a core aspect of these systems. Reward systems to motivate individuals to be involved in CE practices, e.g., recycling, is a big concern. We need to sure people are fairly rewarded and incentive mechanisms in this context need study."
- V Xu et al. (2023). A knowledge graph-based survey on distributed ledger technology for IoT vertical. https://dl.acm.org/doi/pdf/10.1145/3609503, see 7.3 Energy Management note "Pop et al. [106]usedEthereum to build a blockchain prototype that collects and stores energy consumption information from IoT smart metering devices and defines rules that can automat ically execute rewards or penalties through smart contracts, as well as balance energy demand and energy supply. Experimental results show that the prototype can track demand response sig nals with high precision, while reducing the energy flexibility required for convergence. However, blockchain-based microgrid energy transactions face privacy concerns; Thus, Hassan et al. [44] used differential privacy technology to ensure that no private information of any transaction par ticipant can be easily inferred and proposed a blockchain-based microgrid energy auction system called DEAL, which maintains the overall network benefits and social welfare.
In order to solve the shortcomings of high latency and low performance in the blockchain struc-ture, some scholars use DAG to design smart energy systems. Park et al. [100] discussed the pos sibility of DAG being applied in P2P energy trading platforms from the perspective of IOTA use cases. In further work, Park and Kim [101] proposed a DAG-based smart grid. The distributed ledger is called PowerGraph. Each transaction in PowerGraph is individually confirmed through a well-designed consensus algorithm."
- WO2025/160422 A2 see [0679] In embodiments, at least one of the set of autonomous orchestration systems is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event, the at least one energy-related event including at least one of, an energy purchase and/or sale event, a service charge associated with an energy purchase and/or sale event, an energy' consumption event, an energy generation event, an energy' distribution event, an energy' storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event.
[0704] In embodiments, the intelligent agent is further configured to orchestrate delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery.sup.7 of fuel, or at least one delivery.sup.7 of stored energy. [0705] In embodiments, the intelligent agent is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event, the at least one energy-related event including at least one of. an energy purchase and/or sale event, a service charge associated with an energy purchase and/or sale event, an energy consumption event, an energy generation event, an energy’ distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event.
[0730] In embodiments, the artificial intelligence system is further configured to record, in a distributed ledger and/or blockchain, at least one energy -related event, the at least one energy- related event including at least one of, an energy' purchase and/or sale event, a sendee charge associated with an energy purchase and/or sale event, an energy' consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy' credit event, a pollution production event, or a pollution abatement event.
[0749] In embodiments, the artificial intelligence system is further configured to record, in a distributed ledger and/or blockchain, at least one energy-related event, the at least one energy- related event including at least one of, an energy' purchase and/or sale event, a service charge associated with an energy' purchase and/or sale event, an energy' consumption event, an energy' generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy' credit event, a pollution production event, or a pollution abatement event.
[0778] In embodiments, at least one of the adaptive, autonomous data handling systems is further configured to record, in a distributed ledger and/or blockchain, at least one energy -related event, the at least one energy -related event including at least one of, an energy purchase and/or sale event, a service charge associated with an energy’ purchase and/or sale event, an energy’ consumption event, an energy generation event, an energy' distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event.
[0796] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network, such as for handling one or more remote sensing applications, such as for taking inputs from sensors distributed throughout various transactional environments. In embodiments, the MLP neural network may be used for classification of transactional environments and resource environments, such as lending markets, spot markets, forward markets, energy markets, renewable energy credit (REC) markets, networking markets, advertising markets, spectrum markets, ticketing markets, rewards markets, compute markets, and others mentioned throughout this disclosure, as well as physical resources and environments that produce them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, and the like, including classification of geological structures (including underground features and above ground features), classification of materials (including fluids, minerals, metals, and the like), and other problems. This may include fuzzy classification.
[1044] In some graph neural networks, an architecture of the neural network may be selected and/or adapted based on a topology of one or more input graph data sets and/or output graph data sets. For example, a bipartite input graph data set may include two more subgraphs, and a graph neural network may include two or more distinct subsets of neurons that are respectively configured to receive and process data associated with the nodes and/or edges included in one of the subgraphs. As another example, a multigraph input graph data set may include a plurality of edges connecting two or more nodes. For example, a graph representing a social network may include various types of edges that represent various types of relationships (e.g., familial relationships, friendships, and/or professional relationships), and two or more nodes may be connected by a plurality of edges (e.g., a first edge indicating a friendship among the two or more nodes and a second edge indicating a professional relationship among the two or more nodes). An architecture of the graph neural network may correspond to the multi graph nature of the input graph data set. For example, a graph neural network may include two or more distinct subsets of neurons that are respectively configured to receive and process data associated with a subset of edges of the input graph data set that are of a particular edge type (e.g., a first subset of neurons that is configured to receive and process nodes connected by edges that represent friendships, and a second subset of neurons that is configured to receive and process nodes connected by edges representing professional relationships). As yet another example, an input hypergraph data set may include one or more hyperedges that interconnect three or more nodes. An architecture of a graph neural network that is configured to receive and process the input hypergraph data set may include one or more neurons with synapses that interconnect to two or more other neurons in correspondence with one or more hyperedges of the input hypergraph data set.
[1086] Examples of additional applications of various graph neural networks to various graph data sets include, without limitation: graph mining applications (e.g., graph matching and/or clustering); physics (e.g., physical systems modeling and/or evolution over time); chemistry (e.g., molecular fingerprints and/or chemical reaction predictions); biology (e.g., protein interface predictions, side effects predictions, and/or disease classification); knowledge graphs (e.g., knowledge graph completion and/or knowledge graph alignment); generation (e.g.. output graph data set generation that corresponds to an expression, an image, a video, a music sample, or a scene graph); combinatorial optimization; traffic networks (e.g., traffic state prediction); recommendation systems (e.g., user-item interaction predictions and/or social recommendations); economic networks (e.g., stock markets); software and information technology (e.g., software defined networks, AMR graph-to-text tasks, and program verification); text processing (e.g., text classification, sequence labeling, machine translation, relation extraction, event extraction, fact verification, question answering, and/or relational reasoning); and image processing (e.g., social relationship understanding, image classification, visual question answering, object detection, interaction detection, region classification, and/or semantic segmentation). Further examples of applications for processing various graph data sets by various graph neural networks are presented elsewhere in this disclosure and/or will be known to or appreciated by persons of ordinary skill in the art.
[1164] In embodiments, an optimization of energy’ for compute and compute for energy management module is enhanced by resource optimization Al. The module may be designed to optimize the allocation and utilization of both energy and computational resources through the application of advanced Al algorithms. In the context of advanced computation, the module may employ Al to dynamically allocate processing power and memory resources across various energy applications, ensuring optimal performance and cost-efficiency. For energy efficiency optimization, the Al may analyze energy’ efficiency ratios and metrics in real-time, adjusting investment strategies and capital distributions to balance returns against risk exposure. Risk shifting may be achieved through Al-driven models that predict energy market volatility and credit risk, enabling proactive rebalancing of portfolios to mitigate potential losses. Technical features enabling these implementations may include deep learning networks for complex energy' modeling, real-time analytics engines for monitoring resource utilization, and evolutionary algorithms that adapt energy strategies to changing market conditions. Additionally, the module may incorporate distributed ledger technology for transparent tracking of resource allocation decisions and smart contracts for the automated execution of optimization strategies. By integrating these technical features, the resource optimization modules provide a sophisticated framework for maximizing the efficiency and effectiveness of energy and computational resource management within the energy sector.
*Previously noted:
- WO2021186331 Schaffner see pg. 6 lines 24-33 note " the plurality of end users 106 are 25 individuals whose activities include a purchase of items having a recyclable component (for example, a purchase of a beverage in a recyclable container). The plurality of end users 106 can return the recyclable component for a variety of rewards that are distributed to them automatically when a return activity is validated using the system 100. The purchase, use and return of recyclable items is tracked by the system and is available to the user via a mobile 30 application accessible on the end users' portable electronic device. As is explained in further detail below, the system 100 also allows the end users 106 to share information, validate collected recyclable amounts, receive rewards in the form of cryptocurrency and exchange or donate their rewards."
Pg. 10 lines 13-15 note "According to some embodiments, a unique digital wallet is created for each user which is used with all of the partner applications created with the reward system resources 102."
Pg. 12 lines 25-34 note "The reward system application can also include a token management module, for example, a wallet where a user stores tokens earned as a reward for sustainability activities. According to some embodiments, a unique digital wallet is created for each user which is used with all of the partner applications created with the reward system resources 102. The tokens can include tokens for use with the selected blockchain employed by the system 100. 30 In one embodiment, the tokens include ERC-20 tokens employed with an Ethereum blockchain. The tokens are assigned to an end user via a smart contract that assigns a value to the user's sustainability actions once validated. The value can be pre-determined to allow the plurality of end users to understand how any specific sustainability activity is valued, in advance."
- WO2024112127 see note "The business server receives the carbon reduction amount information, stores it in a blockchain, and provides a reward according to the carbon reduction amount to the consumer terminal"
- US2024/0070708 see [0099] "The eco-friendly product seller 420, the eco-friendly product purchaser, and the airdrop beneficiary 170 can transfer and sell the rewarded Green Coin to the DeFi service provider 160 to exchange it for fiat currency."; [0100] "The airdrop beneficiary 170 purchases an eco-friendly product from the eco-friendly product seller 420, receives a Green Coin reward (airdrop) from the Green Coin blockchain system 140, and remits the rewarded Green Coin to the DeFi service provider 160 to exchange it for fiat currency. The airdrop beneficiary 170 pays a Green Coin transfer fee to the Green Coin blockchain system 140 when a transaction of transferring the rewarded Green Coin to the DeFi service provider 160 occurs (step 523d)."
- US20220156754 see [0045] "A purchaser may be required to create a user profile or account with the system 100 before submitting a request to redeem a purchased point. The registration of the user profile may be made by mail, phone, and/or Internet; in one embodiment, however, subsequent interactions are made exclusively over the Internet. Creation of the user profile may require that the purchaser 116 enter identifying information such as name, address, and/or telephone number, and this information may be stored in the computer database 108. The information may be used to validate the purchaser 116 in order to prevent or reduce fraudulent transactions. The purchaser 116 may choose, during registration, to receive news and information about products in which the purchaser 116 has an interest. In one embodiment, the purchaser 116 is given a point award for using a preferred registration mechanism, such as registering via the Internet."
- Pub. No. US2019/0180311 Chan et al. see Abstract " blockchain-based loyalty point distribution system may be configured to provide decentralized loyalty IDs. The decentralized loyalty IDs may allow distribution of loyalty rewards in response to transaction-based events and non-transaction-based events, including behavioral interactions. The system may include a loyalty partner system that receives a rewards event from a customer. The loyalty partner system may determine a loyalty ID associated with the customer, and write the rewards event to the blockchain. A loyalty provider system may receive notification of the rewards event write and distribute the loyalty reward to the customer. The loyalty provider may instruct the loyalty partner to remit payment for the loyalty reward, and may update customer and loyalty partner balances on the blockchain"
- WO 2018071673 see page 7 lines 26-34 note “The server periodically (e.g., daily) executes "jobs" that obtain from one or more relevant data sources the following for each item in each user's wish list(s): a current web price, a shipping charge associated with the purchase of the product on the website, one or more rewards available for use in purchasing the product on the website (including a cashback reward), and a best coupon available for use in purchasing the product on the website (step 255). Based on the most recent web price, best applicable coupon, shipping charge, and reward amount(s) obtained by the jobs executing on the server, the server periodically calculates a new fully-stacked price for each wish list item and compares it to previously calculated fully-stacked price for the item (step 260).”
- WO2016112099 Madden et al see "According to various embodiments, the reward engine 130 operates to generate and provide electronic wallets to the plurality of end users 106, generate smart contracts and provide rewards to the plurality of end users 106, for example, Eco Rewards in the form of cryptocurrency tokens. For example, the reward engine 130 generates an electronic wallet for each of the plurality of users 106, respectively, when the respective user registers in the system 100. The reward engine 130 also operates to write data, for example, smart contracts into the blockchain."
"In general, the reward system resources 102 provide a dedicated platform and cryptocurrency reward system available to registered users and partners. The reward system resources 102 allow for the creation of generic events and challenges. The reward system resources 102 also allow for the creation of specific events and challenges. In each case, the events and challenges are available to incentivize sustainability activities by the plurality of users 106. These events and challenges deliver rewards via a generic smart contract. In addition, the reward system resources 102 also allow for the creation of multiple different partner applications within the platform where the partner application can reward user activity completed in specific events and challenges with a smart contract specific to the partner application. According to some embodiments, a unique digital wallet is created for each user which is used with all of the partner applications created with the reward system resources 102."
- US2023/0206266 see [0089] "In operations 940 and 1040, the host may issue rewards to composters commensurate, e.g., proportional to, the amount of their traded compost. In one embodiment, the rewards given to the composter is equal to the value of their traded compost. In another embodiment, the rewards may be correlated with, but not equal to, their traded compost. For example, a third party may pay the exchange host for the carbon credits and separately issue rewards to the composter, thus paying not only for carbon credits but also for additional user data, such as, target demographics information. In some embodiments, the host may embed the exchanged rewards inside digital media and distribute the digital media to the composter."
U Zhang et al., Deep Hypergraph Structure Learning, August 26, 2022, https://arxiv.org/pdf/2208.12547
V Liu et al., Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding, May 20-25, 2024, https://aclanthology.org/2024.lrec-main.1367.pdf
W Samadrita Ghosh, Knowledge Graphs - What, Why, and How, Dec 19, 2022, Medium, https://samadritaghosh.medium.com/knowledge-graphs-what-why-and-how-84f920316ca5
Response to Applicant’s Arguments or Remarks
4. As per claim interpretation section, it has been withdrawn in view of filed claim amendments.
As per 103, it is withdrawn in view of filed claim amendments for substantially similar reason(s) as noted in the Non-Final rejection of record 09/24/2025.
As per 101, the Examiner respectfully disagrees. The Applicant argues against prong one as set forth in “A. Reframing the Step 2A Prong One "Directed To" Inquiry” and “B. Step 2A Prong One: The Claims Are Not Directed to Methods of Organizing Human Activity”. The Applicant is reminded that the claims are to be given their broadest reasonable interpretation in light of the as-filed specification and the analysis is carried out at USPTO (the office) using 2019 PEG framework. Based on the 2019 PEG step 2A analysis is broken down into prong one and prong two which differs from the District, Federal, and Supreme Court analysis as they do not breakdown the analysis into prong one and prong two, as far as the Examiner is aware. Accordingly, the Applicant’s argument in view of SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) appears to be misplaced. Indeed as part of step 2A the claim as a whole is considered under prong two. That being said, at the office, under prong one, indeed the analysis is based on abstract recitation not additional elements, wherein the additional elements are first considered under prong two. Accordingly, based on the abstract recitation, the Examiner ascertained that the claims recite an abstract idea, which squarely falls in the certain methods of organizing human activity. Thus, the Applicant’s argument “A.” in view of case law and additional elements, at least note items (a) – (g) as noted by the Applicant, appear to be misplaced as the analysis is based on abstract recitation.
Furthermore, as per argument “B.”, the Examiner finds no correlation between the unique facts of the instant application and McRO, and the Examiner has not invoked mental processes grouping. Indeed as a PHOSITA would understand under BRI in light of the as-filed specification, squarely, the claims based on abstract recitation recite certain methods of organizing human activity, note “abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of rewarding eco-friendly product purchases which is certain methods of organizing human activity (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.” The Examiner has also specified which sub-grouping under certain methods of organizing human activity is applicable by applying bold and underlining to the sub-grouping text, i.e. “commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations”. Also, the Examiner notes that the claims clearly recite evaluating or analyzing collected energy-savings data associated with an energy-efficient transaction or energy-efficient service. Further relationship and correlations between the energy savings, the carbon credits, and product or service attributes are evaluated to provide an on-boarded customer with carbon credits for energy savings once a transaction or service has gone through successful validation which entails adjusting detected discrepancy or error. Thus, the Applicant’s argument “B.” in view of case law and additional elements which disregards the above inventive concept based on abstract recitation in the claims as a whole is unpersuasive.
Next, the Applicant argues Step 2A Prong Two: Practical Application and finds it analogous to Example 46, Example 47, Example 39, DDR, and Enfish, however the unique facts of the instant application are not analogous to the Applicant’s assertion and that it noted in the properly updated rejection in view of filed claim amendments, note “claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least are a data-centric artificial intelligence (AI) module(s), knowledge graph(s), wherein the knowledge graph is trained using a hypergraph neural network to encode high-order data correlations, an eco-crypto token generated and collecting energy-saving data based on data-centric AI module and knowledge graph, an eco-crypto rewards platform and generating a unique eco-crypto token, encrypting the eco-crypto token with homomorphic encryption, providing via a platform, and monitoring to alert based on data discrepancy (per claims 1, 15, and 20); a non-transitory machine-readable medium storing instructions for generating synthetic eco-crypto tokens for carbon credits that, when executed by one or more processors, cause the one or more processors to perform steps comprising (additionally per claim 20); collecting data via using an AI data collector integrated into the data-centric AI module (per claim 2); generating dynamic smart contracts to automatically adjust the eco-crypto token (per claim 4); generating a dynamic smart contract via a dynamic smart contract engine (per claims 6 and 19); eco-token is configured for use with another cryptocurrency or another cryptocurrency platform (claim 10); knowledge graph is trained continuously using a Hypergraph Neural Network (HGNN) framework to adapt to new energy-saving data, enabling dynamic and accurate allocation of carbon credits (per claim 11) and service is designing, developing, testing, updating, or maintaining software (per claim 14). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements as tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)) including AI, Knowledge-graph trained using a hyper neural network, and encryption applied to eco-crypto token to keep track of a particular user’s rewards. The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1-2 and their associated disclosure. The processor executing the "apply it" instruction is further connected to one or more device merely sending/receiving data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) to transmit requisite energy-savings data and onboarding user on the rewards platform. Collecting energy-savings data to compute carbon credits or savings to provide rewards is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes collected and transmitted user data to ascertain how much carbon savings can be attributed to a eco-friendly product purchased by a user in order to provide a reward which is registered in blockchain ledger using unique user token to keep track of user’s accumulated rewards. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is amount of rewards accumulated by a user for purchasing eco-friendly products (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor and storing rewards generated via eco-friendly purchase transaction in a rewards ledger per smart contract, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 explained above.
Thus, the abstract idea of rewarding eco-friendly product purchases which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
Furthermore, the courts have already noted that there is a clear distinction between using technology and improving it, for instance note as follows:
(A) Rady v. Boston Consulting Group “Using known specialized but known hardware and a computer to perform an abstract idea is still merely applying an abstract idea.”, and “The use of known blockchain processes alone, absent a showing of specific technical improvements, is insufficient to demonstrate technological improvement or significantly more.”;
(B) Recentive Analytics v. Fox Corp see Page 12, lines 1-4: The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.
Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.”; and
(C) “Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract” (SAP v. Investpic: Page 10, lines 18-24).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”
Next, the Applicant argues step 2B, the Applicant is reminded that the evaluation is that of one or more additional element(s) considered both singularly and in-combination. Further, as it pertains to evidence to prove WURC that is limited to additional elements is limited to insignificant extra solution activity per prong two analysis and the additional element that merely execute the abstract idea as “apply it” instructions and generally link the abstract idea to one or more technical environment(s) that is analysis is simply carried over to step 2B, i.e. evidence for "apply it" instructions and/or generally linking to technical environment(s) as determined in prong two is not required as noted per 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More - I. THE SEARCH FOR AN INVENTIVE CONCEPT -
B. Examples Of How Courts Conduct The Search For An Inventive Concept - II. ELIGIBILITY STEP 2B: WHETHER THE ADDITIONAL ELEMENTS CONTRIBUTE AN "INVENTIVE CONCEPT" – further note "Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should:
• Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant.” Nevertheless, the Examiner has provided evidence in view of filed claim amendments and compact prosecution only, note “The abstract idea of rewarding eco-friendly product purchases which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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) [similarly here user's data is received and per industry carbon savings data is transmitted over a network]; and
(ii) Collecting and analyzing information to detect misuse and notifying a user when misuse is detected (FairWarning); Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution notification about discrepancy are provided and rewards are stored in a ledger post successful validation].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
1. The Examiner provides citation to one or more of the court decisions as noting the well-understood, routine, conventional nature of the additional element(s) as follows:
(i) Higher order data encoding in machine learning
- NPL: https://medium.com/@debisreer/encoding-in-machine-learning-8e7b11857daa
-NPL: https://machinelearningmastery.com/one-hot-encoding-for-categorical-data/
-NPL:https://towardsdatascience.com/3-key-encoding-techniques-for-machine-learning-a-beginner-friendly-guide-aff8a01a7b6a/
- CN120978711A note "The invention integrates the power system domain knowledge (such as tide equation, safety constraint) and the data driving model to construct the hybrid intelligent framework. the hypergraph neural network layer is responsible for modelling the high-order relationship among multiple nodes, each hypergraph neural network layer is connected with multiple nodes for representing the complex group interaction. the space-time attention layer enhances the sensing ability of the model to the key space-time mode, and dynamically adjusts the important weight of different characteristics and time steps. Application of a two-stage optimizer training model: the first stage uses the improved particle swarm algorithm to carry out global exploration in the large-scale solution space and quickly locate the potential optimal solution area; the second stage uses the optimization method based on gradient to perform local refinement and fine tuning the model parameter. The model training adopts distributed computing frame, supporting increment learning and online updating, keeping the timeliness of the model."; and "green energy integration: the user participates in the renewable energy consumption to obtain the points and can exchange the preferential or service."
(ii) Homomorphic Encryption
- Sarah Lee, Homomorphic Encryption: A Mathematical Revolution Uncovering the Historical Development and Significance of Homomorphic Encryption, 05/28/2025, Number Analytics,
https://www.numberanalytics.com/blog/homomorphic-encryption-history-mathematics
- Asante et al., Evolution of Homomorphic Encryption, https://www.ijcaonline.org/archives/volume183/number29/asante-2021-ijca-921679.pdf
- Homomorphic Encryption: How to Process Data Without Ever Decrypting It
https://cis2019.com/pages/homomorphic-encryption-how-to-process-data-without-ever-decrypting-it
- Allowing Computation on Encrypted Data Without Decryption: A Comprehensive Guide to Fully Homomorphic Encryption (FHE) Introduction
https://modern-cryptography.com/allowing-computation-on-encrypted-data-without-decryption-a-comprehensive-guide-to-fully-homomorphic
(iii) Executing smart contracts and electronic record-keeping
- Youb et al. Pub. No.: US 2019/0080392 note "[0009] So-called "Smart Contracts" are legal obligations tied to a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of the contracts. Smart contracts allow the performance of credible transactions without third parties. These transactions are trackable and may be irreversible. See, en.wikipedia.org/wiki/Smart contract. The phrase "smart contracts" was coined by computer scientist Nick Szabo in 1996. See, "Nick Szabo--Smart Contracts: Building Blocks for Digital Markets". www.fon.hum.uva.nl, Extropy #16; Szabo, Nick (1997 Sep. 1). "Formalizing and Securing Relationships on Public Networks". First Monday. 2 (9); Tapscott, Don; Tapscott, Alex (May 2016). The Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business, and the World. pp. 72, 83, 101, 127. ISBN 978-0670069972. A smart contract is a set of promises, specified in digital form, including protocols within which the parties perform on these promises. Recent implementations of smart contracts are based on blockchains, though this is not an intrinsic requirement. Building on this base, some recent interpretations of "smart contract" are mostly used more specifically in the sense of general purpose computation that takes place on a blockchain or distributed ledger. In this interpretation, used for example by the Ethereum Foundation or IBM, a smart contract is not necessarily related to the classical concept of a contract, but can be any kind of computer program. See, Buterin, Vitalik. "Ethereum Whitepaper", Github; Cachin, Christian. "Architecture of the Hyperledger Blockchain Fabric", ibm.com."; [0010]-[0050]; [0051] Peters, Gareth W., and Efstathios Panayi. "Understanding modern banking ledgers through blockchain technologies: Future of transaction processing and smart contracts on the internee of money." In Banking Beyond Banks and Money, pp. 239-278. Springer, Cham, 2016; [0052]-[0065].
- Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755; and 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 [similarly here unique user token is utilized to keep track of rewards earned and utilized in transactions, which are recorded in a ledger];
(iv) The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of knowledge graphs as follows:
- Zhang et al., Deep Hypergraph Structure Learning, August 26, 2022, https://arxiv.org/pdf/2208.12547
- Liu et al., Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding, May 20-25, 2024, https://aclanthology.org/2024.lrec-main.1367.pdf
- Samadrita Ghosh, Knowledge Graphs - What, Why, and How, Dec 19, 2022, Medium, https://samadritaghosh.medium.com/knowledge-graphs-what-why-and-how-84f920316ca5
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.”
Therefore, the Examiner respectfully maintains the rejection as the amended claims are directed to an abstract idea without significantly more.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571)270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
Prosecution Insights