Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This action is in response to the application filed on 27 November 2023.
Claims 1-26 are presently pending for examination.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 11/27/2023 has being considered by the examiner.
Claim Rejections - 35 USC § 103
3. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over Padmanabhan (Padman), U. S. Patent Publication No. 2019/0236598 in view of Kocsis et al., U. S. Patent Publication No. 2022/0030031.
Regarding claim 1, Padman discloses a method for integrating a plurality of neural networks, neural smart contracts, and neural consensus algorithms onto a distributed ledger (see Padman, Abstract and ¶ [0003]; a neural network for distributed ledger that receives consensus from nodes that execute smart contract transactions is disclosed), the method comprising:
a. storing and verifying transactions in a decentralized and immutable manner on the distributed ledger (see Padman, ¶ [0044], [0067] and [0117]; verified transactions are stored in a decentralized distributed ledger); b. performing data analysis tasks and sharing information with the plurality of neural networks by the distributed ledger, which neural networks are operatively connected to the distributed ledger (see Padman, ¶[0046], [0064] and [0227]; data analysis is performed to shared information); and d. facilitating agreement on the validity of transactions, and network state, among the participants in the distributed ledger, using neural consensus algorithms implemented within the distributed ledger (see Padman, ¶ [0140]; transactions are validated using consensus protocols).
Although Padman discloses the invention substantially as claimed, it does not explicitly disclose c. enabling the neural smart contracts, implemented within the distributed ledger, to automatically execute and enforce predefined functions and decision-making processes.
Kocsis teaches c. enabling the neural smart contracts, implemented within the distributed ledger, to automatically execute and enforce predefined functions and decision-making processes (see Kocsis, ¶ [0018] and [0063]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the teachings of Kocsis with that of Padman in order to obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the teachings of Kocsis with that of Padman in order to efficiently have the decision-making process made in consensus thus removing reliance on a single authority.
Regarding claim 2, Padman-Kocsis teaches further comprising providing external data inputs to the plurality of neural networks by integrated oracles, to enable real-time information integration and to enhance the accuracy and responsiveness of the method (see Padman, ¶ [0435]).
Regarding claim 3, Padman-Kocsis teaches wherein the plurality of neural networks comprises a first neural network for data preprocessing, a second neural network for pattern recognition, and a third neural network for decision-making (see Padman, ¶ [0407], [0409] and Kocsis, ¶ [0036]). Same motivation utilized in claim 1 applies equally as well to claim 3.
Regarding claim 4, Padman-Kocsis teaches wherein the distributed ledger facilitates secure and transparent storage of neural network models, training data, analysis results, and neural smart contracts, allowing for decentralized access and update mechanisms (see Padman, ¶ [0071] and [0326]).
Regarding claim 5, Padman-Kocsis teaches wherein the integration of the plurality of neural networks, neural smart contracts, and neural consensus algorithms onto the distributed ledger enables collaborative learning, information sharing, automated governance processes, and real-time integration of external data among the participants, enhancing the overall accuracy, efficiency, and decision-making speed (see Padman, ¶ [0129] and Kocsis, ¶ [0050] and [0082]). Same motivation utilized in claim 1 applies equally as well to claim 5.
Regarding claim 6, Padman-Kocsis teaches wherein the transactions are voting that is analyzed for fraud detection and prevention and real-time election monitoring (see Padman, ¶ [0134] and [0136]).
Regarding claim 7, Padman-Kocsis teaches where consensus mechanisms determine the value and representation of tokens associated with various assets which are digital representations that can be traded on a blockchain or distributed ledger (see Padman, ¶ [0094]).
Regarding claim 8, Padman-Kocsis teaches wherein the transactions are financial transactions that are analyzed for risk assessment and credit scoring, fraud detection and prevention, and personalized investment recommendations (see Padman, ¶ [0227] and [0460]).
Regarding claim 9, Padman-Kocsis teaches wherein the transactions are carbon credits that are analyzed for carbon footprint calculations, carbon credit trading optimization, and emission reduction planning (see Padman, ¶ [0084] and [0193]).
Regarding claim 10, Padman-Kocsis teaches wherein the transactions are accounting transactions that are analyzed for regulatory compliance monitoring, forensic data analysis, and risk management and auditing (see Padman, ¶ [0084] and [0148]).
Regarding claim 11, Padman-Kocsis teaches wherein the transactions are cargo handling requests that are analyzed for cargo details, ensuring compliance with regulations and confirming the availability of resources (see Padman, ¶ [0084] and [0196]).
Regarding claim 12, Padman discloses an integrated distributed ledger that is integrated with a plurality of neural networks, neural smart contracts, and neural consensus algorithms (see Padman, Abstract and ¶ [0003]; a neural network for distributed ledger that receives consensus from nodes that execute smart contract transactions is disclosed), the integrated distributed ledger comprising: a. a storage module and a verification module for storing and verifying transactions in a decentralized and immutable manner on the integrated distributed ledger (see Padman, ¶ [0044], [0067] and [0117]; verified transactions are stored in a decentralized distributed ledger); b. a connection module for connecting the integrated distributed ledger to the plurality of neural networks for performing data analysis tasks and sharing information with the plurality of neural networks (see Padman, ¶ [0046], [0064] and [0227]; data analysis is performed to shared information);
and d. a neural consensus algorithm module for facilitating agreement on the validity of transactions, and network state, among the participants in the integrated distributed ledger (see Padman, ¶ [0140]; transactions are validated using consensus protocols).
Although Padman discloses the invention substantially as claimed, it does not explicitly disclose c. a neural smart contracts module for enabling the neural smart contracts to automatically execute and enforce predefined functions and decision-making processes.
Kocsis teaches c. a neural smart contracts module for enabling the neural smart contracts to automatically execute and enforce predefined functions and decision-making processes (see Kocsis, ¶ [0018] and [0063]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the teachings of Kocsis with that of Padman in order to efficiently have the decision-making process made in consensus thus removing reliance on a single authority.
Regarding claim 13, Padman-Kocsis teaches further comprising an oracle module for providing external data inputs to the plurality of neural networks, to enable real-time information integration and to enhance accuracy and responsiveness (see Padman, ¶ [0435]).
Regarding claim 14, Padman-Kocsis teaches wherein the plurality of neural networks comprises a first neural network for data preprocessing, a second neural network for pattern recognition, and a third neural network for decision-making (see Padman, ¶ [0407], [0409] and Kocsis, ¶ [0036]). Same motivation utilized in claim 12 applies equally as well to claim 14.
Regarding claim 15, Padman-Kocsis teaches wherein the integrated distributed ledger using the storage module further facilitates secure and transparent storage of neural network models, training data, analysis results, neural smart contracts, and oracle data, allowing for decentralized access and update mechanisms (see Padman, ¶ [0071] and [0326]).
Regarding claim 16, Padman-Kocsis teaches wherein the integration of the plurality of neural networks, neural smart contracts, oracles, and neural consensus algorithms onto the distributed ledger enables collaborative learning, information sharing, automated governance processes, and real-time integration of external data among the participants, enhancing the overall accuracy, efficiency, and decision-making speed (see Padman, ¶ [0129] and Kocsis, ¶ [0050] and [0082]). Same motivation utilized in claim 12 applies equally as well to claim 16.
Regarding claim 17, Padman discloses a method for integrating a plurality of neural networks, neural smart contracts, oracles, and neural consensus algorithms onto a distributed ledger, enabling the distributed ledger to be a decentralized distributed ledger framework that comprises a network of AI nodes that grows and evolves over time, promoting decentralization and collaboration among participants (see Padman, Abstract and ¶ [0003]; a neural network for distributed ledger that receives consensus from nodes that execute smart contract transactions is disclosed), the method comprising: a. receiving data from multiple sources using the distributed ledger, including oracles, and preprocessing the data using a first neural network (see Padman, ¶ [0044] and [0125]; transaction data is received);
b. transmitting the preprocessed data to a second neural network for pattern recognition and generating insights (see Padman, ¶ [0128]; prepared data packets are transmitted); c. storing the generated insights and analysis results, including oracle data, on the distributed ledger (see Padman, ¶ [0067], [0117] and [0227]; analyzed data are stored);
e. updating the distributed ledger with the decision outcomes and incorporating real-time oracle data (see Padman, ¶ [0060], [0345] and [0390] updates including decision are recorded); f. repeating steps a) to e) iteratively to refine the neural network’s performance and to incorporate the latest oracle information (see Padman, ¶ [0067], [0125], [0128] and [0345]; steps a-e are repeated); and h. applying neural consensus algorithms, including both novel and common algorithms, to reach agreement on the validity of transactions, network state, and oracle data within the distributed ledger (see Padman, ¶ [0140] and [0475]).
Although Padman discloses the invention substantially as claimed, it does not explicitly disclose d. accessing the stored insights and oracle data by a third neural network for decision-making and g. executing predefined functions and decision-making processes using neural smart contracts within the distributed ledger.
Kocsis teaches d. accessing the stored insights and oracle data by a third neural network for decision-making and g. executing predefined functions and decision-making processes using neural smart contracts within the distributed ledger (see Kocsis, ¶ [0016], [0018] and [0063]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the teachings of Kocsis with that of Padman in order to efficiently have the decision-making process made in consensus thus removing reliance on a single authority.
Regarding claim 18, Padman-Kocsis teaches wherein the distributed ledger ensures the integrity, security, and transparency of the data, insights, decision outcomes, neural smart contracts, oracle data, and consensus-related information exchanged (see Padman, ¶ [0065] and [0074]).
Regarding claim 19, Padman-Kocsis teaches wherein the transactions are voting that is analyzed for fraud detection and prevention and real-time election monitoring (see Padman, ¶ [0134] and [0136]).
Regarding claim 20, Padman-Kocsis teaches wherein the transactions are financial transactions that are analyzed for risk assessment and credit scoring, fraud detection and prevention, and personalized investment recommendations (see Padman, ¶ [0227] and [0460]).
Regarding claim 21, Padman-Kocsis teaches wherein the transactions involve the creation of digital representations to enable asset monetization and trading on distributed ledger technologies and blockchains (see Padman, ¶ [0068]).
Regarding claim 22, Padman-Kocsis teaches wherein the transactions are carbon credits that are analyzed for carbon footprint calculations, carbon credit trading optimization, and emission reduction planning (see Padman, ¶ [0084] and [0193]).
Regarding claim 23, Padman-Kocsis teaches wherein the transactions are accounting transactions that are analyzed for regulatory compliance monitoring, forensic data analysis, and risk management and auditing (see Padman, ¶ [0084] and [0148]).
Regarding claim 24, Padman-Kocsis teaches wherein the transactions are cargo handling requests that are analyzed for cargo details, ensuring compliance with regulations and confirming the availability of resources (see Padman, ¶ [0084] and [0196]).
Regarding claim 25, Padman discloses a method of integrating an AI neural network onto a distributed ledger using a data collection module, an AI neural network module, a neural consensus module, and an integration and neural smart contracts DLT module (see Padman, Abstract and ¶ [0003]; a neural network for distributed ledger that receives consensus from nodes that execute smart contract transactions is disclosed), in order to analyze and process data, the method comprising:
b. processing the preprocessed data from the data collection module with the AI neural network module (see Padman, ¶ [0125] and [0128]; prepared data packets are processed); c. training the AI neural network using the processed data with the AI neural network module (see Padman, ¶ [0378] and [0399]; neural network is trained);
d. selecting a consensus mechanism to apply to the processed data using the neural consensus module and to define and validate the transaction (see Padman, ¶ [0140] and [0475]; consensus protocol is selected to apply on the collected data); and
e. integrating the AI neural network onto the distributed ledger using an integration and neural smart contracts DLT module in order to analyze the results of the processed data and the transaction using neural smart contracts (see Padman, ¶ [0046] and [0377]; neural network for distributed ledger is disclosed).
Although Padman discloses the invention substantially as claimed, it does not explicitly disclose a. collecting and preprocessing the data using the data collection module.
Kocsis teaches a. collecting and preprocessing the data using the data collection module (see Kocsis, ¶ [0020] and [0058]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate the teachings of Kocsis with that of Padman in order to efficiently analyze transactions accuracy and maintain data integrity for improved governance and consensus mechanisms.
Regarding claim 26, Padman discloses an AI neural network integrated with a distributed ledger to make an integrated network/ledger to process data, the integrated network/ledger comprising:
b. an AI neural network module to process the preprocessed data and train itself using the processed data (see Padman, ¶ [0125] and [0128]; prepared data packets are processed); c. a neural consensus module to select and apply a neural consensus mechanism to the processed data and to define and validate a transaction (see Padman, ¶ [0140] and [0475]; consensus protocol is selected to apply on the collected data); and d. an integration and neural smart contracts DLT module for integrating the AI neural network onto the distributed ledger so that the distributed ledger can analyze the processed data and the transaction using neural smart contracts (see Padman, ¶ [0046] and [0377]; neural network for distributed ledger is disclosed).
Although Padman discloses the invention substantially as claimed, it does not explicitly disclose a. a data collection module to collect and preprocess the data.
Kocsis teaches a. a data collection module to collect and preprocess the data.
Prior Art of Record
4. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Please refer to form PTO-892 (Notice of Reference Cited) for a list of relevant prior art.
a) US 20220092056 A1 is directed to a method for providing prediction-as-a-service through intelligent blockchain smart contracts according to one embodiment includes mining transaction data stored on a blockchain ledger in a blockchain network, executing at least one smart contract stored on the blockchain ledger to apply a predictive artificial intelligence model in the blockchain network based on the mined transaction data and trigger one or more actions in relation to an enterprise, and automatically instructing the enterprise to execute the one or more actions.
b) US 20200065763 A1 is directed to a smart contract so that the terms of the smart contract can be implemented across a diverse array of consensus networks or blockchain platforms. In one example, this disclosure describes a method that includes receiving, by a first computing device, information describing a smart contract, wherein the first computing device is included within a first plurality of computing devices, each on a first consensus network that maintains a first distributed ledger; performing, by the first computing device and in response to receiving the information describing the smart contract, operations to update the first distributed ledger; and interpreting, by at least one of the first plurality of computing devices, the information describing the smart contract to determine and perform at least one of a plurality of first smart contract operations on the first consensus network.
c) US 20190332702 A1 is directed to a system that has first node for enrolling with a blockchain network to participate in management of the blockchain network. The first node obtains a ledger block from a local copy of a distributed ledger stored at first physical computing node. The first node reads a change request proposed by second node of the blockchain network from the ledger block. The first node generates a vote on whether to approve the change request based on local policy rules. The first node transmits the vote to the blockchain network, where some of a set of computing nodes each provide a respective vote on whether to approve the change request. The first node determines a consensus decision that the change request is approved based on the vote and the respective votes. The first node generates a blockchain transaction comprising a payload that indicates a state of the first computing node after the consensus decision is generated.
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED IBRAHIM whose telephone number is (571)270-1132. The examiner can normally be reached on Monday through Friday from 9:30AM to 6:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on 571-272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mohamed Ibrahim/
Primary Examiner, Art Unit 2444