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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/22/2026 has been entered.
Status of Claims
This action is in response to the amendments filed 01/22/2026. Claims 1, 5, 8-10, 12-14, 18-21, and 23 have been amended, claims 3 and 15-16 have been cancelled, claims 24-26 have been added. Claims 1-2, 5, 7-10, 12-14, and 17-26 are currently pending.
Response to Arguments
Claims 3 and 15-16 have been cancelled, therefore the rejections of claims 3 and 15-16 no longer stand.
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new grounds of rejection. Applicant argues that the prior art does not teach using a generative artificial intelligence model to generate a smart contract. Examiner agrees but notes that the Abdelrahman reference has been brought in to more clearly teach using an artificial intelligence model to generate smart contracts. Applicant argues that the prior art does not teach detecting an action performed after a smart contract has been generated and stored. Examiner respectfully disagrees and notes that as Das teaches the capability to read a bytecode associated with a blockchain in order to perform an action associated with the smart contract stored on a distributed ledger associated with the bytecode (see at least columns 5-6 of Das), that the smart contract must have been already generated and stored before any actions can be performed by Das. Applicant argues that the logic from Das modifies human readable content rather than the LLM; however, Examiner notes that column 5 of Das teaches that the “LLM 110 can include the logic 112 or the logic 112 can include the LLM 110”. These components are not required in all embodiments to be distinct components, and so Das teaches at least one embodiment where the LLM can modify the content stored on the blockchain.
Examiner notes as claims 15-16 have been cancelled, that the Jayakumar reference is no longer relied upon. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 5, 7-10, 12, 14, and 17-26 are rejected under 35 U.S.C. 103 as being unpatentable over Das et al (US 11928438 B1, herein Das), in view of Abdelrahman et al (US 20240330927 A1, herein Abdelrahman), in further view of Rajan et al (“Quantum Blockchain using Entanglement in Time”, herein Rajan), in further view of Gidney (US 20230368050 A1, herein Gidney).
Regarding claim 1, Das teaches a method (col. 2 lines 18-19 recite “A method may comprise: hosting, by a computing instance, a large language model (LLM)”) comprising: [generating, by a processing system including at least one processor, a smart contract by] executing a generative artificial intelligence foundation model that has been trained to generate items of new content having characteristics that mimic characteristics of content included in a set of training data (col. 5 lines 13-22 recite “The LLM 110 may be a language model (e.g., a generative artificial intelligence (AI) model, a generative adversarial network (GAN) model, a generative pre-trained transformer (GPT) model) including an artificial neural network (ANN) with a set of parameters (e.g., tens of weight, hundreds of weights, thousands of weights, millions of weights, billions of weights, trillions of weights), initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm”. Col. 5 line 62 – col. 6 line 5 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract”. Col. 6 lines 43-53 recite “One approach to train the LLM 110 may involve a supervised learning technique, where a set of data includes a set of labeled bytecode examples, for each data point to include features (covariates) and an associated label, to have a learning function that is inferred and maps feature vectors (inputs) to labels (output), based on example input-output pairs. Therefore, the learning function may be inferred from labeled training data containing a set of training examples (e.g., each example is a pair including an input object (e.g., a vector) and a desired output value (e.g., a supervisory signal))” (i.e., executing a generative model trained to generate content associated with a smart contract based on a training data set)),
wherein the smart contract is tailored by the generative artificial intelligence foundation model to satisfy a requirement of a prompt that the processing system provides as an input to the generative artificial intelligence foundation model (col. 5 line 64 – col. 6 line 22 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted (i.e., the generative model receives an input prompt) to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract. The LLM 110 may determine whether the bytecode enables the smart contract by the bytecode expressing (1) an offeror identifier (e.g., a unique user network name on the P2P computer network) associated with an offeror network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (2) an offeree identifier (e.g., a unique user network name on the P2P computer network) associated with an offeree network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (3) an offer expression (e.g., a selection of an amount of a good or a service for a price value along with an expiry date if any) associated with the offeror identifier, (4) an acceptance expression (e.g., an indicator of consent for the offer expression) associated with the offeree identifier, and (5) a consideration expression (e.g., an indicator of a benefit to be respectively received) associated with the offeror identifier and the offeree identifier.” (i.e., the new content to determine whether a smart contract is enabled is generated by the generative model and is tailored to a relevant bytecode determined by the input prompt));
storing, by the processing system, the smart contract in a [quantum temporal] blockchain (col. 4 lines 4-10 recite “FIG. 1 shows a diagram of an embodiment of a computing architecture according to this disclosure. In particular, a computing architecture 100 includes a network 102, a computing terminal 104, a distributed ledger technology (DLT) node 106, and a computing instance 108. The computing instance 108 hosts a large language model (LLM) 110 and a logic 112”. Col. 4 lines 28-40 recite “The DLT (i.e., distributed ledger technology) node 106 (e.g., a desktop computer, a laptop computer, a smartphone, a tablet computer, a server, a client, a server farm, a cloud computing instance) may be a node of a peer-to-peer (P2P) computer network enabling a performance of a consensus algorithm (e.g., a proof-of-work algorithm, a proof-of-stake algorithm) such that a distributed ledger (e.g., a blockchain) is distributably replicated across the P2P computer network (e.g., servers, clients, server farms, cloud computing instances), including the DLT node 106, where each node of the P2P computer network, including the DLT node 106, copies and saves an identical copy of ledger data and updates itself independently of other respective nodes” (i.e., generated content, such as the data associated with the smart contract, is saved in a blockchain));
detecting, by the processing system, an action performed by the generative artificial intelligence foundation model against the smart contract, wherein the action is performed after the smart contract has been generated and stored in the [quantum temporal] blockchain; and logging, by the processing system, the action in the [quantum temporal] blockchain (col. 5 line 62 – col. 6 line 5 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract”. Col. 8 line 60 – col. 9 line recite “The LLM 110 may perform the first action, which may include (a) identifying the offer expression in the bytecode, the acceptance expression in the bytecode, and the consideration expression in the bytecode, (b) generating a human readable content describing the offer expression, the acceptance expression, and the consideration expression, and (c) outputting the human-readable content for consumption on the computing terminal 104. The LLM 110 may output the human-readable content to the computing terminal 104 or the logic 112 may receive the human-readable content from the LLM 110 and pass, whether as is or with modification, the human-readable content to the computing terminal 104” (i.e., detecting and logging an action, such as access to a smart contract that was already stored in the blockchain, wherein the action taken is related to information that was that was associated with the smart contract, generated by the generative model, and stored in the blockchain)).
However, while Das teaches a generative model capable of referencing a generated smart contract, Das does not explicitly teach generating, by a processing system including at least one processor, a smart contract by executing a generative artificial intelligence foundation model.
Abdelrahman teaches generating, by a processing system including at least one processor, a smart contract by executing a generative artificial intelligence foundation model (para. [0038] recites “artificial intelligence (AI) is leveraged to create, validate, and optimize smart contracts before deploying the smart contract on a distributed ledger”. At least figure 22 discloses a computing environment comprising a processor. (i.e., using a generative artificial intelligence model to generate a smart contract)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by modifying the generative artificial intelligence model from Das to include the smart contract generation capability from the generative artificial intelligence model from Abdelrahman. Das and Abdelrahman are both directed to using deploying generative models on a blockchain to keep track of model transactions more securely. As Das already teaches the ability to read smart contracts and take action using a generative model, one of ordinary skill would be motivated to add the smart contract generation capability taught by Abdelrahman to the model from Das to enable Das to generate smart contracts in addition to reading them.
However, while the combination of Das and Abdelrahman teaches storing information associated with a smart contract in a blockchain (see at least column 4 of Das), the combination of Das and Abdelrahman does not explicitly teach storing information as a qubit in a block of a quantum temporal blockchain, wherein the block is one of a plurality of blocks of the quantum temporal blockchain, and wherein all blocks of the plurality of blocks are entangled with each other such that an alteration to one block of the plurality of blocks results in a detectable alteration to a past version of the one block, and wherein the quantum temporal blockchain further stores a record identifier of the item of new content.
Rajan teaches storing information as a qubit in a block of a quantum temporal blockchain, wherein the block is one of a plurality of blocks of the quantum temporal blockchain, and wherein all blocks of the plurality of blocks are entangled with each other such that an alteration to one block of the plurality of blocks results in a detectable alteration to a past version of the one block (the abstract recites “We propose a conceptual design for a quantum blockchain. Our method involves encoding the blockchain into a temporal GHZ (Greenberger-Horne-Zeilinger) state of photons that do not simultaneously coexist (i.e., a qubit). It is shown that the entanglement in time, as opposed to an entanglement in space, provides the crucial quantum advantage”. Section I para. 3 recites “we propose a conceptual design for a quantum blockchain using entanglement in time”. Section I para. 4 recites “Our novel methodology encodes a blockchain into these temporally entangled states, which can then be integrated into a quantum network for further useful operations”. Section III para. 1 recites “our aim is to replace the data structure component of the classical blockchain with a quantum system. In quantum information theory, quantum systems are described as information carriers, with an encoding and decoding process. In particular, multipartite GHZ (Greenberger-Horne-Zeilinger) states are ones in which all subsystems contribute to the shared entangled property”. Section V para. 4 recites “The temporal GHZ blockchain (EQ 9) adds a far greater benefit in that the attacker cannot even attempt to access the previous photons, since they no longer exist. They can at best try to tamper with the last remaining photon, which would invalidate the full state” (i.e., storing information in qubits of a quantum temporal blockchain wherein the blocks are entangled with one another)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by applying the start contract management methods from Das (as modified by Abdelrahman) using the quantum blockchain from Rajan. Das and Rajan both teach use of a blockchain to store important data. One of ordinary skill in the art would be motivated to modify the blockchain from Das with the quantum blockchain from Rajan to improve the security of the blockchain, as Rajan teaches in at least paragraph 3 of section V: “The temporal GHZ blockchain adds a far greater benefit in that the attacker cannot even attempt to access the previous photons, since they no longer exist. They can at best try to tamper with the last remaining photon, which would invalidate the full state. Hence, in this application of quantum information, we see that the entanglement in time provides a far greater security benefit than an entanglement in space”.
However, while the combination of Das, Abdelrahman, and Rajan teaches storing a large language model in a blockchain (see at least column 4 of Das), the combination of Das, Abdelrahman, and Rajan does not explicitly teach wherein the block of the [quantum temporal] blockchain further stores a record identifier of the item of new content.
Gidney teaches wherein the block of the [quantum temporal] blockchain further stores a record identifier of the smart contract comprising a unique identifier that is used to locate an entry for the smart contract in the quantum temporal blockchain (para. [0047] recites “a stream on the blockchain may maintain a record of UUIDs or hash IDs corresponding to each party. The stream may store a counter that indicates how many different parties (e.g., indicated by encryption keys associated with the records of the stream) are currently within the stream. In some embodiments, this may be done using a smart contract type code, so that when an additional party contributes data to the stream, the count is incremented”. Para. [0035] recites “FIG. 2 illustrates a blockchain that can be used to monitor and track data submitted by various parties for model training”. Para. [0037] recites “The blockchain 200 comprises a plurality of blocks, each corresponding to a record 202. Each record 202 corresponds to one or more actions performed on the data stored at the distributed file system 210. For example, a record 202 may correspond to a set of data added to the distributed file system 210 by a party, a request to delete data on the distributed file system 210 by a party, the training of a model 225 based on the training data 215 stored on the distributed file system 210, and/or the like”. Para. [0046] recites “When an item is stored on the distributed file system 210, its UUID and descriptive information (such as the source system and company identification) are encoded onto the blockchain” (i.e., a record identifier of newly added content, such as content associated with a smart contract, can be stored on a blockchain)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by storing the content record identifiers from the blockchain taught by Gidney in the quantum temporal blockchain from Das (as modified by Abdelrahman and Rajan). Gidney and Das are both directed to methods of utilizing large language models stored on a distributed ledger, or blockchain. One of ordinary skill in the art would be motivated to store record identifiers such as those taught by Gidney to properly implement the large language model from Das.
Regarding claim 2, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the generative artificial intelligence foundation model is based in a deep learning technique (Das col. 5 lines 13-22 recite “The LLM 110 may be a language model (e.g., a generative artificial intelligence (AI) model, a generative adversarial network (GAN) model, a generative pre-trained transformer (GPT) model) including an artificial neural network (ANN) with a set of parameters (e.g., tens of weight, hundreds of weights, thousands of weights, millions of weights, billions of weights, trillions of weights), initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm” (i.e., a deep learning model)).
Regarding claim 5, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the action comprises an action that the smart contract specifies is to be taken in response to an occurrence of a predefined condition (Das col. 5 line 62 – col. 6 line 22 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract. The LLM 110 may determine whether the bytecode enables the smart contract by the bytecode expressing (1) an offeror identifier (e.g., a unique user network name on the P2P computer network) associated with an offeror network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (2) an offeree identifier (e.g., a unique user network name on the P2P computer network) associated with an offeree network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (3) an offer expression (e.g., a selection of an amount of a good or a service for a price value along with an expiry date if any) associated with the offeror identifier, (4) an acceptance expression (e.g., an indicator of consent for the offer expression) associated with the offeree identifier, and (5) a consideration expression (e.g., an indicator of a benefit to be respectively received) associated with the offeror identifier and the offeree identifier” (i.e., an action specified by the smart contract to be taken in response to a predefined condition)).
Regarding claim 7, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the quantum temporal blockchain further stores the set of training data used to train the generative artificial intelligence foundation model (Das col. 6 lines 43-64 recite “One approach to train the LLM 110 may involve a supervised learning technique, where a set of data includes a set of labeled bytecode examples, for each data point to include features (covariates) and an associated label, to have a learning function that is inferred and maps feature vectors (inputs) to labels (output), based on example input-output pairs. Therefore, the learning function may be inferred from labeled training data containing a set of training examples (e.g., each example is a pair including an input object (e.g., a vector) and a desired output value (e.g., a supervisory signal)). As such, the LLM 110 may be supervisedly taught with a set of bytecode examples compiled from a source code example for a smart contract example, where each bytecode example is respectively labeled with (1) the offeror identifier associated with the offeror network address on the distributed ledger, (2) the offeree identifier associated with the offeree network address on the distributed ledger, (3) the offer expression associated with the offeror identifier, (4) the acceptance expression associated with the offeree identifier, and (5) the consideration expression associated with the offeror identifier and the offeree identifier” (i.e., data compiled from the blockchain can serve as training examples to train the model)).
Regarding claim 8, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the smart contract and the action are stored in the quantum temporal blockchain as at least one Greenberger-Horne-Zeilinger qubit (the abstract of Rajan recites “We propose a conceptual design for a quantum blockchain. Our method involves encoding the blockchain into a temporal GHZ (Greenberger-Horne-Zeilinger) state of photons that do not simultaneously coexist”. Rajan section III para. 1 recites “our aim is to replace the data structure component of the classical blockchain with a quantum system. In quantum information theory, quantum systems are described as information carriers, with an encoding and decoding process. In particular, multipartite GHZ (Greenberger-Horne-Zeilinger) states are ones in which all subsystems contribute to the shared entangled property”. Gidney para. [0057] recites “the model can be automatically deployed by each of parties when the event is sensed. One method to enable this is to use a form of smart contract, where code is used to monitor the blockchain ledger for those events and changes and when seen, automatically executes for the stream and model” (i.e., storing content, such as data associated with a smart contract, in the quantum temporal blockchain using at least one GHZ qubit)).
Regarding claim 9, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the quantum temporal blockchain further stores at least one of: a location of the smart contract, or geofence data associated with the smart contract (Das col. 10 line 60 – col. 11 line 7 recite “Since the LLM 110 may be prompted from the computing terminal 104 at a location (e.g., a physical location, a network location), whether via the computing terminal 104 or through the logic 112, the computing instance 108, which may include the LLM 110 or the logic 112, may be programmed to determine the location when the bytecode is determined to enable the smart contract on the P2P computer network, including the DLT node 106, such that the third action or the fourth action involves determining whether the location is covered (geographically) by the jurisdictional identifier and outputting a first message based the location being determined to be covered by the jurisdictional identifier and a second message based the location being determined to not be covered by the jurisdictional identifier” (i.e., the blockchain can store jurisdictional, or geofence data associated with generated smart contracts)) or a hashed representation of the smart contract (Rajan section I para. 3 recites “we propose a conceptual design for a quantum blockchain using entanglement in time”. Rajan section I para. 4 recites “Our novel methodology encodes a blockchain into these temporally entangled states, which can then be integrated into a quantum network for further useful operations”. Rajan section II para. 2 recites “Records about the past, which occurred at around the same time, are received and collected into a data block. These blocks are time-stamped to ensure that the data existed at the specified time. Furthermore, the blocks are linked in chronological order through cryptographic hash functions”. Gidney para. [0047] recites “a stream on the blockchain may maintain a record of UUIDs or hash IDs corresponding to each party. The stream may store a counter that indicates how many different parties (e.g., indicated by encryption keys associated with the records of the stream) are currently within the stream. In some embodiments, this may be done using a smart contract type code, so that when an additional party contributes data to the stream, the count is incremented” (i.e., the quantum temporal blockchain stores at least hashed representations of the stored content that can be associated with a smart contract)).
Regarding claim 10, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the action comprises at least one of: an access of the smart contract by a user (Das col. 5 line 62 – col. 6 line 5 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract”. Col. 8 line 60 – col. 9 line recite “The LLM 110 may perform the first action, which may include (a) identifying the offer expression in the bytecode, the acceptance expression in the bytecode, and the consideration expression in the bytecode, (b) generating a human readable content describing the offer expression, the acceptance expression, and the consideration expression, and (c) outputting the human-readable content for consumption on the computing terminal 104. The LLM 110 may output the human-readable content to the computing terminal 104 or the logic 112 may receive the human-readable content from the LLM 110 and pass, whether as is or with modification, the human-readable content to the computing terminal 104” (i.e., reading a bytecode, or accessing content that can be associated with a smart contract, that was generated by the generative model)) or a modification of the smart contract by a user (Das col. 5 line 62 – col. 6 line 5 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract”. Col. 8 line 60 – col. 9 line recite “The LLM 110 may perform the first action, which may include (a) identifying the offer expression in the bytecode, the acceptance expression in the bytecode, and the consideration expression in the bytecode, (b) generating a human readable content describing the offer expression, the acceptance expression, and the consideration expression, and (c) outputting the human-readable content for consumption on the computing terminal 104. The LLM 110 may output the human-readable content to the computing terminal 104 or the logic 112 may receive the human-readable content from the LLM 110 and pass, whether as is or with modification, the human-readable content to the computing terminal 104” (i.e., taking an action to modify, or enable a smart contract, generated by the model)).
Regarding claim 12, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the action comprises a portion of a negotiation process for creating an agreement among a plurality of parties to the smart contract (Das col. 5 line 62 – col. 6 line 5 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract”. Col. 8 line 60 – col. 9 line recite “The LLM 110 may perform the first action, which may include (a) identifying the offer expression in the bytecode, the acceptance expression in the bytecode, and the consideration expression in the bytecode, (b) generating a human readable content describing the offer expression, the acceptance expression, and the consideration expression, and (c) outputting the human-readable content for consumption on the computing terminal 104. The LLM 110 may output the human-readable content to the computing terminal 104 or the logic 112 may receive the human-readable content from the LLM 110 and pass, whether as is or with modification, the human-readable content to the computing terminal 104”. Das col. 6 lines 5-22 recite “The LLM 110 may determine whether the bytecode enables the smart contract by the bytecode expressing (1) an offeror identifier (e.g., a unique user network name on the P2P computer network) associated with an offeror network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (2) an offeree identifier (e.g., a unique user network name on the P2P computer network) associated with an offeree network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (3) an offer expression (e.g., a selection of an amount of a good or a service for a price value along with an expiry date if any) associated with the offeror identifier, (4) an acceptance expression (e.g., an indicator of consent for the offer expression) associated with the offeree identifier, and (5) a consideration expression (e.g., an indicator of a benefit to be respectively received) associated with the offeror identifier and the offeree identifier” (i.e., an action taken as part of a negotiation process for creating an agreement among parties in a smart contract)).
Regarding claim 14, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the smart contract is hashed using a cryptographic hash function and digitally signed using a quantum entanglement-based algorithm prior to the storing (Rajan section I para. 3 recites “we propose a conceptual design for a quantum blockchain using entanglement in time”. Rajan section I para. 4 recites “Our novel methodology encodes a blockchain into these temporally entangled states, which can then be integrated into a quantum network for further useful operations”. Rajan section II para. 2 recites “Records about the past, which occurred at around the same time, are received and collected into a data block. These blocks are time-stamped to ensure that the data existed at the specified time. Furthermore, the blocks are linked in chronological order through cryptographic hash functions”. Gidney para. [0047] recites “a stream on the blockchain may maintain a record of UUIDs or hash IDs corresponding to each party. The stream may store a counter that indicates how many different parties (e.g., indicated by encryption keys associated with the records of the stream) are currently within the stream. In some embodiments, this may be done using a smart contract type code, so that when an additional party contributes data to the stream, the count is incremented” (i.e., using a cryptographic hash function and a quantum entanglement algorithm on content, such as data associated with a smart contract, in the blockchain)).
Regarding claim 17, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the generating, the storing, the detecting, and the logging are performed by a distributed resource controller that is in communication with the generative artificial intelligence foundation model and the quantum temporal blockchain (Das col. 4 lines 4-10 recite “FIG. 1 shows a diagram of an embodiment of a computing architecture according to this disclosure. In particular, a computing architecture 100 includes a network 102, a computing terminal 104, a distributed ledger technology (DLT) node 106, and a computing instance 108. The computing instance 108 hosts a large language model (LLM) 110 and a logic 112” (i.e., a controller in communication with a blockchain and a generative artificial intelligence model)).
Regarding claim 18, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the quantum temporal blockchain further stores at least one of: a timestamp indicating when the smart contract was generated, a timestamp indicating when the smart contract was stored in the quantum temporal blockchain (Rajan section II para. 1 recites “The aim of a blockchain is to have a single database of records about the past that every node in the network can agree on”. Rajan section II para. 2 recites “Records about the past, which occurred at around the same time, are received and collected into a data block. These blocks are time-stamped to ensure that the data existed at the specified time”. Gidney para. [0057] recites “Once the model is verified, a further record is written to the blockchain to indicate to all parties that the model has been verified and available for use. In some embodiments, the model can be automatically deployed by each of parties when the event is sensed. One method to enable this is to use a form of smart contract, where code is used to monitor the blockchain ledger for those events and changes and when seen, automatically executes for the stream and model” (i.e., the blockchain stores a timestamp indicating when content, such as data associated with a smart contract, was stored)).
Claim 19 is a non-transitory computer-readable medium claim and its limitation is included in claim 1. The only difference is that claim 19 requires a non-transitory computer-readable medium (Das col. 12 lines 44-49 recite “The present disclosure may be embodied in a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure”). Therefore, claim 19 is rejected for the same reasons as claim 1.
Claim 20 is a system claim and its limitation is included in claim 1. The only difference is that claim 20 requires a system (Das col. 1 lines 49-50 recite “A system may comprise: a computing instance programmed to: host a large language model (LLM)”). Therefore, claim 20 is rejected for the same reasons as claim 1.
Regarding claim 21, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the block of the quantum temporal blockchain further stores a quantum digital signature of the smart contract that verifies an integrity of the smart contract (Das col. 4 lines 4-10 recite “FIG. 1 shows a diagram of an embodiment of a computing architecture according to this disclosure. In particular, a computing architecture 100 includes a network 102, a computing terminal 104, a distributed ledger technology (DLT) node 106, and a computing instance 108. The computing instance 108 hosts a large language model (LLM) 110 and a logic 112”. Rajan section IV para. 2-3 recites “we replaced the classical network with a quantum network. In addition, digital signatures would be covered by a QKD protocol as stated in Reference [8]. Each node on the quantum network would host a copy of the quantum blockchain (EQ9); hence, if a node tampers with its own local copy, it does not affect the copies at the other nodes analogous to the classical case. New blocks (that come from a sender) need to be verified for their correctness, before being copied and added to each node’s blockchain. Since correct blocks are GHZ entangled states, one needs a verification test to do it” (i.e., the data, which can be data associated with the smart contract from Das, stored on the blockchain includes the quantum digital signatures of each node used to verify the quantum blockchain)).
Regarding claim 22, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the block of the quantum temporal blockchain further stores a model identifier of the generative artificial intelligence foundation model (Gidney fig. 2 and para. [0010] recite “The method further comprises storing the received models on a file system, and recording on a distributed blockchain ledger one or more records. A first record of the one or more records comprises a hash corresponding to a pointer to a first model stored on the file system corresponding to the first party, an identifier corresponding to the first party, and a timestamp indicating a time at which the record was recorded on the blockchain ledger” (i.e., a model identifier stored on a blockchain, which could be a quantum temporal blockchain like the one taught by Rajan)).
Regarding claim 23, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the block of the quantum temporal blockchain further stores an indication of a content type of the smart contract (Gidney para. [0022] recites “a model may be trained on training data comprising a set of clause examples such that the model is able to, when presented with a document, identifying particular types of clauses present in the document. This may allow for the provisions of a contract document to be easily identified and tracked, even as additional amendments and addendums are added in the contract”. Gidney para. [0037]-[0038] recites “The blockchain 200 comprises a plurality of blocks, each corresponding to a record 202. Each record 202 corresponds to one or more actions performed on the data stored at the distributed file system 210. For example, a record 202 may correspond to a set of data added to the distributed file system 210 by a party, a request to delete data on the distributed file system 210 by a party, the training of a model 225 based on the training data 215 stored on the distributed file system 210, and/or the like. Each record 202 of the blockchain 200 comprises a reference (e.g., a pointer) to a previous record of the blockchain 200 and an indication of a transaction type corresponding to the record 202”. Gidney para. [0045] recites “different types of training data may be tracked using different streams of data within a single blockchain 200” (i.e., the blockchain, which could be a quantum temporal blockchain like the one taught by Rajan, can store information related to transaction, or content, types of information related to a smart contract)).
Regarding claim 24, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1, wherein the logging creates an auditable trail of actions against the smart contract (Gidney para. [0062] recites “as the blockchain ledger is append only, the records of the original submission of the data remains in the blockchain. However, the UUID of those records will no longer point to valid data on the distributed file system. The destruction request is recorded in the blockchain, and then a new list of all valid UUIDs is generated for the model to be trained on. This means that a full audit trail and proof of destruction can be generated for the party” (i.e., logging data in a blockchain can create an auditable trail)).
Regarding claim 25, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 5, wherein a further prompt to the generative artificial intelligence foundation model comprises a request to take the action in response to the predefined condition being fulfilled (Das col. 5 line 62 – col. 6 line 22 recite “Once the LLM 110 is trained, the LLM 110 is structured to have a data structure and organized to have a data organization. As such, the data structure and the data organization collectively enable the LLM 110 to be prompted to (i) read a bytecode capable of being executed on a distributed ledger (e.g., the DLT node 106), (ii) determine whether the bytecode enables a smart contract on the distributed ledger, and (iii) take a first action based on the bytecode being determined to enable the smart contract and a second action based on the bytecode being determined to not enable the smart contract. The LLM 110 may determine whether the bytecode enables the smart contract by the bytecode expressing (1) an offeror identifier (e.g., a unique user network name on the P2P computer network) associated with an offeror network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (2) an offeree identifier (e.g., a unique user network name on the P2P computer network) associated with an offeree network address (e.g., a unique user network locator on the P2P computer network) on the distributed ledger, (3) an offer expression (e.g., a selection of an amount of a good or a service for a price value along with an expiry date if any) associated with the offeror identifier, (4) an acceptance expression (e.g., an indicator of consent for the offer expression) associated with the offeree identifier, and (5) a consideration expression (e.g., an indicator of a benefit to be respectively received) associated with the offeror identifier and the offeree identifier” (i.e., the generative model can take an action in response to a prompt, or request if a predefined condition is fulfilled)).
Regarding claim 26, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 25, wherein the generative artificial intelligence foundation model generates an instruction in response to the further prompt that causes a remote system to perform the action (Das col. 13 lines 36-46 recite “The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer” (i.e., a system embodied in figure 1 can be connected to external systems to execute actions, including actions related to the smart contract as described in claim 1 on which claim 26 ultimately depends)).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Das et al (US 11928438 B1, herein Das), in view of Abdelrahman et al (US 20240330927 A1, herein Abdelrahman), in further view of Rajan et al (“Quantum Blockchain using Entanglement in Time”, herein Rajan), in further view of Gidney (US 20230368050 A1, herein Gidney), in further view of Smith et al (US 11477015 B1, herein Smith).
Regarding claim 13, the combination of Das, Abdelrahman, Rajan, and Gidney teaches the method of claim 1.
However, while Rajan teaches a quantum computing system (see section III), the combination of Das, Abdelrahman, Rajan, and Gidney does not explicitly teach wherein the smart contract comprises a sequence of instructions for execution on a quantum computer.
Smith teaches wherein the smart contract comprises a sequence of instructions for execution on a quantum computer (col. 2 lines 41-46 recite “The example blockchain system 10 shown in FIG. 1 produces and maintains a blockchain that serves as a record of transactions. An example blockchain is shown in FIG. 4. The blockchain can be used to secure cryptocurrency transactions or other types of transactions (e.g., contracts, stock trades and settlements, property registration, etc.)”. Col. 9 line 65 – col. 10 line 6 recite “The example server 108 shown in FIG. 2 may include a quantum machine instruction library or other resources that the server 108 uses to produce quantum computing jobs to be executed by quantum computing resources in the computing environment 101 (e.g., by the quantum processor unit 103). The quantum machine instruction library may include, for example, specialized instructions to simplify or expedite solving difficult computational problems required by the blockchain system” (i.e., a contract can include a sequence of instructions for execution on a quantum computer)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by generating the instruction sequences for a quantum computer from Smith using the large language model as taught by Das (as modified by Abdelrahman, Rajan, and Gidney). Das teaches in at least column 5 that its large language model, or generative artificial intelligence model, may be a general purpose model used to generate content related to a range of tasks; accordingly, one of ordinary skill in the art would understand how to modify this model from Das to generate the instruction sequences for a quantum computer taught by Smith.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250078074 A1 (Humpherys et al) teaches a method for using a generative AI model for automated analysis of blockchain smart contracts.
US 20250173724 A1 (Silver) teaches a distributed ledger integrated with AI neural consensus networks, which can develop and deploy smart contracts generated by the neural consensus networks.
US 20190012595 A1 (Beser et al) teaches a method for utilizing neural consensus networks to write and update a model to a blockchain.
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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147