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 response to application filed on 04/17/2024. Claims 1-20 are pending.
Claim rejections-35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 10 recites the limitation "the node type" in line 1. There is insufficient antecedent basis for this limitation in the claim. It respectfully notes that its ancient is “a cyber threat type”. Appropriate correction is requested.
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, 12-13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lal et al. (US 20250148078) in view of (Bazelgette et al. (CA 3034176 A1)
Regarding claim 1:
A system for providing artificial intelligence (AI) mediated curation of access and content within a cyber threat intelligence platform, the system comprising:
one or more memories storing computer-executable instructions including an AI engine (AI engine: Lal, [0078]); and one or more processors communicatively coupled with the one or more memories that are configured to execute the computer-executable instructions and cause the system to:
receive, from a node having access to a distributed ledger, a cyber threat content input indicating a cyber threat type: (receiving cyber threats with possible threats types: Lal [0068]).
However, Lal does not explicitly teach identify, by a trained AI model of the AI engine, one or more cyber threat intelligence content objects based on the cyber threat type, each of the one or more cyber threat intelligence content objects being (a) contributed by one or more of one or more nodes having access to the distributed ledger or (b) generated by the trained AI model.
In similar art, Bazelgette teaches the analyzer module may form one or more hypotheses on what are a possible set of cyber threats that could include the identical abnormal behavior and/or suspicious activity from the trigger module with one or more AI models trained with machine learning on possible cyber threats (see, Bazelgette [7]);
evaluate, by the trained AI model, each of the one or more cyber threat intelligence content objects to: determine respective relevance values corresponding to each of the one or more cyber threat intelligence content objects, and generate a curated set of cyber threat intelligence content objects based on the respective relevance values: (Bazelgette teaches AI is applied for analyzing cyber security threats, and determining, and generating one or more supported possible cyber threat hypotheses from the possible set of cyber threats that could include the identified abnormal behavior and/or suspicious activity: [3]; [7]).
a recommended cyber threat practice, and transmit the recommended cyber threat practice and an indication of the curated set of cyber threat intelligence content objects to the node: (Bazelgette teaches the defense system is configurable in a user interface enabling what type of automatic response actions for different types of cyber threats: [43]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bazelgette’s ideas into Lal’s system in order to provide an efficient cyber threat defense system with AI model trained with machine learning on possible cyber threats (see Bazelgette [7]).
Regarding claim 2:
In addition to the rejection claim 1, Lal-Bazelgette further teaches one or more cyber threat intelligence content objects are stored on the distributed ledger: (one or more AI models trained with machine learning on possible cyber threats: Bazelgette [7]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bazelgette’s ideas into Lal’s system in order to provide an efficient cyber threat defense system with AI model trained with machine learning on possible cyber threats (see Bazelgette [7]).
Regarding claim 12:
A computer-implemented method for providing artificial intelligence (AI) mediated curation of access and content within a cyber threat intelligence platform, the computer-implemented method comprising:
receiving, at one or more processors from a node having access to a distributed ledger, a cyber threat content input indicating a cyber threat type: (receiving cyber threats with possible threats types: Lal [0068]).
However, Lal does not explicitly teach identifying, by the one or more processors executing a trained AI model of an AI engine, one or more cyber threat intelligence content objects based on the cyber threat type, each of the one or more cyber threat intelligence content objects being (a) contributed by one or more of one or more nodes having access to the distributed ledger or (b) generated by the trained AI model.
In similar art, Bazelgette teaches the analyzer module may form one or more hypotheses on what are a possible set of cyber threats that could include the identical abnormal behavior and/or suspicious activity from the trigger module with one or more AI models trained with machine learning on possible cyber threats (see, Bazelgette [7]);
evaluating, by the one or more processors executing the trained AI model, each of the one or more cyber threat intelligence content objects to: determine respective relevance values corresponding to each of the one or more cyber threat intelligence content objects, and generate a curated set of cyber threat intelligence content objects based on the respective relevance values (Bazelgette teaches AI is applied for analyzing cyber security threats, and determining, and generating one or more supported possible cyber threat hypotheses from the possible set of cyber threats that could include the identified abnormal behavior and/or suspicious activity: [3]; [7]), and
a recommended cyber threat practice; and transmitting, by the one or more processors, the recommended cyber threat practice and an indication of the curated set of cyber threat intelligence content objects to the node: (Bazelgette teaches the defense system is configurable in a user interface enabling what type of automatic response actions for different types of cyber threats: [43]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bazelgette’s ideas into Lal’s system in order to provide an efficient cyber threat defense system with AI model trained with machine learning on possible cyber threats (see Bazelgette [7]).
Regarding claim 13:
In addition to the rejection claim 12, Lal-Bazelgette further teaches one or more cyber threat intelligence content objects are stored on the distributed ledger: (one or more AI models trained with machine learning on possible cyber threats: Bazelgette [7]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bazelgette’s ideas into Lal’s system in order to provide an efficient cyber threat defense system with AI model trained with machine learning on possible cyber threats (see Bazelgette [7]).
Regarding claim 20:
tangible, non-transitory computer-readable medium storing instructions for providing artificial intelligence (AI) mediated curation of access and content within a cyber threat intelligence platform that, when executed by one or more processors of a computing device, cause the computing device to:
receive, from a node having access to a distributed ledger, a cyber threat content input indicating a cyber threat type: (receiving cyber threats with possible threats types: Lal [0068]).
However, Lal does not explicitly teach identify, by a trained AI model, one or more cyber threat intelligence content objects based on the cyber threat type, each of the one or more cyber threat intelligence content objects being (a) contributed by one or more of one or more nodes having access to the distributed ledger or (b) generated by the trained AI model.
In similar art, Bazelgette teaches the analyzer module may form one or more hypotheses on what are a possible set of cyber threats that could include the identical abnormal behavior and/or suspicious activity from the trigger module with one or more AI models trained with machine learning on possible cyber threats (see, Bazelgette [7]);
evaluate, by the trained AI model, each of the one or more cyber threat intelligence content objects to: determine respective relevance values corresponding to each of the one or more cyber threat intelligence content objects, and generate a curated set of cyber threat intelligence content objects based on the respective relevance values: (Bazelgette teaches AI is applied for analyzing cyber security threats, and determining, and generating one or more supported possible cyber threat hypotheses from the possible set of cyber threats that could include the identified abnormal behavior and/or suspicious activity: [3]; [7]).
a recommended cyber threat practice, and transmit the recommended cyber threat practice and an indication of the curated set of cyber threat intelligence content objects to the node: (Bazelgette teaches the defense system is configurable in a user interface enabling what type of automatic response actions for different types of cyber threats: [43]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bazelgette’s ideas into Lal’s system in order to provide an efficient cyber threat defense system with AI model trained with machine learning on possible cyber threats (see Bazelgette [7]).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lal- Bazelgette in view of Dupont et al. (US 12,164,664).
Regarding claim 8:
Lal- Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach a retrieval augmented generation (RAG) model, and the computer-executable instructions, when executed by the one or more processors, further cause the system to identify the one or more cyber threat intelligence content objects by: retrieving, by the RAG model, data from at least one of: (i) the distributed ledger or (ii) a source external to the distributed ledger.
In similar art, Dupont teaches Generative AI, semantic search is an increasingly important tool for augmenting model knowledge. AI applications such as Retrieval-Augmented Generation (RAG) leverage semantic search and retrieval processes to provide or “feed” an AI model (e.g., a large language model (“LLM”)) with context (e.g., data) for a specific query, thereby allowing/equipping the AI model to answer questions referencing data that is/was not present in the training data of that AI model (Dupont figure 1; figure 5; column 4, lines 37-48).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Dupont’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Dupont’s ideas into Lal-Bazelgette’s system.
Regarding claim 18:
Lal- Bazelgette discloses the invention substantially as disclosed in claim 12, but does not explicitly teach a retrieval augmented generation (RAG) model, and the computer-implemented method further comprises identifying the one or more cyber threat intelligence content objects by: retrieving, by the one or more processors executing the RAG model, data from at least one of: (i) the distributed ledger or (ii) a source external to the distributed ledger.
In similar art, Dupont teaches Generative AI, semantic search is an increasingly important tool for augmenting model knowledge. AI applications such as Retrieval-Augmented Generation (RAG) leverage semantic search and retrieval processes to provide or “feed” an AI model (e.g., a large language model (“LLM”)) with context (e.g., data) for a specific query, thereby allowing/equipping the AI model to answer questions referencing data that is/was not present in the training data of that AI model (Dupont figure 1; figure 5; column 4, lines 37-48).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Dupont’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Dupont’s ideas into Lal-Bazelgette’s system.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette-Dupont in view of Xu et al. (US 20230042816).
Regarding claim 10:
Lal-Bazelgette-Dupont discloses the invention substantially as disclosed in claim 8, but does not explicitly teach the node type is at least one of: (i) a light node, (ii) a partial node, or (iii) a full node.
In similar art, Xu teaches a cyber defender's full nodes, (Xu, [0067]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Xu’s ideas into Lal-Bazelgette-Dupont’s system in order to save resources and development time by implying Xu’s ideas into Lal-Bazelgette-Dupont’s system.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lal- Bazelgette in view of Smith et al. (US 20190132350) and further in view of George et al. (US 20200111104).
Regarding to claim 3:
Lal- Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach receive a second input from a second node indicating a new cyber threat intelligence content object to be included as part of a set of cyber threat intelligence content objects stored on the distributed ledger; evaluate, by the trained AI model, the second input received from the second node to determine a set of inputs previously stored on the distributed ledger that satisfy a relevance threshold relative to the new cyber threat intelligence content object; generate, by the trained AI model, a composite cyber threat content object by combining portions of the second input and the set of inputs previously stored on the distributed ledger.
In similar art, Smith teaches a user/party can request to implement a transaction in the blockchain. The transactions can be storing data such as a record to a distributed ledger. Once the request has been made, the transaction can be broadcast to other computers (known as nodes) in the network. The network of nodes can then validate the transaction using a mutually a priori agreed upon algorithm. Once each node in the network of nodes validates the transaction, wherein the verified transaction can be combined with other transactions (described in detail below) to create a new block of data for the ledger. Once the verified transaction has been combined, wherein the new block can be added to the network's block chain. The new block can be added in such a way as to make it permanent and unalterable (i.e., through the use of cryptography) (see, [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Smith’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Smith’s ideas into Lal-Bazelgette’s system
However, Lal-Bazelgette-Smith does not teach publicize the composite cyber threat content object to each of the one or more nodes having access to the distributed ledger.
In similar art, George teaches a notification that the distributed secure ledger has been updated (see, George [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify George’s ideas into Lal-Bazelgette-Smith’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette-Smith’s system.
Regarding to claim 14:
Lal- Bazelgette discloses the invention substantially as disclosed in claim 12, but does not explicitly teach receiving a second input from a second node indicating a new cyber threat intelligence content object to be included as part of a set of cyber threat intelligence content objects stored on the distributed ledger; evaluating, by the trained AI model, the second input received from the second node to determine a set of inputs previously stored on the distributed ledger that satisfy a relevance threshold relative to the new cyber threat intelligence content object; generating, by the trained AI model, a composite cyber threat content object by combining portions of the second input and the set of inputs previously stored on the distributed ledger.
In similar art, Smith teaches a user/party can request to implement a transaction in the blockchain. The transactions can be storing data such as a record to a distributed ledger. Once the request has been made, the transaction can be broadcast to other computers (known as nodes) in the network. The network of nodes can then validate the transaction using a mutually a priori agreed upon algorithm. Once each node in the network of nodes validates the transaction, wherein the verified transaction can be combined with other transactions (described in detail below) to create a new block of data for the ledger. Once the verified transaction has been combined, wherein the new block can be added to the network's block chain. The new block can be added in such a way as to make it permanent and unalterable (i.e., through the use of cryptography) (see, [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Smith’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Smith’s ideas into Lal-Bazelgette’s system
However, Lal-Bazelgette-Smith does not teach publicize the composite cyber threat content object to each of the one or more nodes having access to the distributed ledger.
In similar art, George teaches a notification that the distributed secure ledger has been updated (see, George [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify George’s ideas into Lal-Bazelgette-Smith’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette-Smith’s system.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lal- Bazelgette-Smith-George in view of Gukal et al. (US 20170353491).
Regarding claim 4:
Lal- Bazelgette-Smith-George discloses the invention substantially as disclosed in claim 3, but does not explicitly teaches disseminating the composite cyber threat content is performed anonymously.
In similar art, Gukal teaches anonymously sent broadcast packets, can also be used by network threats to hide the network threat's network location ([0246]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Gukal’s ideas into Lal-Bazelgette-Smith-George’s system in order to save resources and development time by implying Gukal’s ideas into Lal-Bazelgette-Smith-George’s system.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette in view of Jones et al. (US 20260006054).
Regarding claim 5:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach a large language model (LLM) trained using a plurality of training node inputs and a plurality of training distributed ledger inputs to output training responses, node types, and threat evaluations.
In similar art, Jones teaches a generative AI security engine supports generative AI security management based on security analysis and detection operations of generative-AI-supported applications (“generative AI applications”) associated with a generative AI model (e.g., a Large Language Model “LLM” likes Generative-Pre-trained Transformer) and prompt interfaces. The generative AI security engine provides generative AI security engine operations (“security engine operations”).The generative AI security engine can implement functional components that provide operations for pre-processing security engine operations, post-processing security engine operations, and training dataset security engine operations associated, by way of example, with the following: intent detection, prompt attack detection, restricted data detection and redaction, and prompt context with redaction (Jones, [0003]; [0017]; [0021]-[0022]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Jones’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Jones’s ideas into Lal-Bazelgette’s system.
Regarding claim 15:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 12, but does not explicitly teach a large language model (LLM) trained using a plurality of training node inputs and a plurality of training distributed ledger inputs to output training responses, node types, and threat evaluations.
In similar art, Jones teaches a generative AI security engine supports generative AI security management based on security analysis and detection operations of generative-AI-supported applications (“generative AI applications”) associated with a generative AI model (e.g., a Large Language Model “LLM” likes Generative-Pre-trained Transformer) and prompt interfaces. The generative AI security engine provides generative AI security engine operations (“security engine operations”).The generative AI security engine can implement functional components that provide operations for pre-processing security engine operations, post-processing security engine operations, and training dataset security engine operations associated, by way of example, with the following: intent detection, prompt attack detection, restricted data detection and redaction, and prompt context with redaction (Jones, [0003]; [0017]; [0021]-[0022]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Jones’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Jones’s ideas into Lal-Bazelgette’s system.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette in view of Xu et al. (US 20230042816).
Regarding claim 9:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach receive a node input from a new device indicating a node type to be established on the distributed ledger for the new device.
In similar art, Xu teaches obtaining cyber intelligence input data from a cyber defender computing device. Wherein cyber intelligence input data identifies a cyber attacker or a victim of a cyber-attack on the network (see Xu, abstract);
generate, by the trained AI model, one or more responses to the node input; and establish, by the trained AI model, a new node for the new device on the distributed ledger that corresponds to the node type: (executing one or more Cyber Security Management (CSM) functions with the cyber intelligence input data received from the cyber defender computing device and cyber data stored in the blockchain ledger, wherein the cyber data stored in the blockchain ledger provides details on a cyber-attack on a network that is managed by another cyber defender computing device: Xu, abstract).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Xu’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Xu’s ideas into Lal-Bazelgette’s system.
Regarding claim 19:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 12, but does not explicitly teach receiving, at the one or more processors, a node input from a new device indicating a node type to be established on the distributed ledger for the new device.
In similar art, Xu teaches obtaining cyber intelligence input data from a cyber defender computing device. Wherein cyber intelligence input data identifies a cyber attacker or a victim of a cyber-attack on the network (see Xu, abstract);
generating, by the one or more processors executing the trained AI model, one or more responses to the node input; and establishing, by the one or more processors executing the trained AI model, a new node for the new device on the distributed ledger that corresponds to the node type: (executing one or more Cyber Security Management (CSM) functions with the cyber intelligence input data received from the cyber defender computing device and cyber data stored in the blockchain ledger, wherein the cyber data stored in the blockchain ledger provides details on a cyber-attack on a network that is managed by another cyber defender computing device: Xu, abstract), wherein the node type is at least one of: (i) a light node, (ii) a partial node, or (iii) a full node: (Xu teaches a cyber defender's full nodes: [0067]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Xu’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Xu’s ideas into Lal-Bazelgette’s system.
Claims 6, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette in view of George et al. (US 20200111104).
Regarding claim 6:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach receive, from a node having access to the distributed ledger, an update to a set of contacts stored on the distributed ledger.
In similar art, George teaches update the secure distributed ledger with changes in contact information (abstract).
determine, by the trained AI model, an estimated adjustment to the set of contacts based on the update; broadcast the estimated adjustment to each of one or more nodes having access to the distributed ledger: (update the secure distributed ledger and notify the other members of the peer-to-peer network. In this way, all members can be informed and updated as the change occurs: George abstract).
responsive to receiving a consensus regarding the estimated adjustment, update the set of contacts based on the estimated adjustment; and broadcast an updated set of contacts indication to the one or more nodes: (a first server to update a distributed secure ledger based on the received update for the contact information associated with the user. The distributed secure ledger can comprise a plurality of entries, each entry storing identity information for a user of the plurality of users. For example, each entry in the distributed secure ledger can comprise a customer name for the user, the contact information, and a status indicator. The contact information can comprise a phone number and the status indicator indicating activated or deactivated. In some cases, the distributed secure ledger can comprise a block chain. The instructions executed by the processor of the first server can then cause the first server to send to each other server of the plurality of servers a notification that the distributed secure ledger has been updated: George [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify George’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette’s system.
Regarding claim 16:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 12, but does not explicitly teach receiving, at the one or more processors from a node having access to the distributed ledger, an update to a set of contacts stored on the distributed ledger.
In similar art, George teaches update the secure distributed ledger with changes in contact information (abstract).
determining, by the one or more processors executing the trained AI model, an estimated adjustment to the set of contacts based on the update; broadcasting, by the one or more processors, the estimated adjustment to each of one or more nodes having access to the distributed ledger: (update the secure distributed ledger and notify the other members of the peer-to-peer network. In this way, all members can be informed and updated as the change occurs: George abstract);
responsive to receiving a consensus regarding the estimated adjustment, updating, by the one or more processors, the set of contacts based on the estimated adjustment; and broadcasting, by the one or more processors, an updated set of contacts indication to the one or more nodes: (a first server to update a distributed secure ledger based on the received update for the contact information associated with the user. The distributed secure ledger can comprise a plurality of entries, each entry storing identity information for a user of the plurality of users. For example, each entry in the distributed secure ledger can comprise a customer name for the user, the contact information, and a status indicator. The contact information can comprise a phone number and the status indicator indicating activated or deactivated. In some cases, the distributed secure ledger can comprise a block chain. The instructions executed by the processor of the first server can then cause the first server to send to each other server of the plurality of servers a notification that the distributed secure ledger has been updated: George [0006]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify George’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette’s system.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette in view of Bitragunta et al. (US 12,282,962) and further in view of Kaidi et al. (US 20240104177)
Regarding claim 7:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach identify, by the trained AI model, a block conflict associated with the distributed ledger.
In similar art, Bitragunta teaches a distributed ledger may be implemented with rules for resolving block conflicts (column 6, lines 54-67);
determine, by the trained AI model, a correlated block position to resolve the block conflict: (the distributed ledger may be implemented with rules for resolving block conflicts: Bitragunta column 6, lines 54-67).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bitragunta’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Bitragunta’s ideas into Lal-Bazelgette’s system.
However, Lal-Bazelgette- Bitragunta does not explicitly teach analyze at least one of (i) a timestamp corresponding with one or more of the one or more nodes having access to the distributed ledger or (ii) a block nonce of one or more blocks on the distributed ledger.
In similar art, Kaidi teaches block nonce of one or more blocks on the distributed ledger, (see Kaidi [0046]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Kaidi’s ideas into Lal-Bazelgette- Bitragunta’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette- Bitragunta’s system.
Regarding claim 17:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach identifying, by the one or more processors executing the trained AI model, a block conflict associated with the distributed ledger.
In similar art, Bitragunta teaches a distributed ledger may be implemented with rules for resolving block conflicts (column 6, lines 54-67);
determining, by the one or more processors executing the trained AI model, a correlated block position to resolve the block conflict: (the distributed ledger may be implemented with rules for resolving block conflicts: Bitragunta column 6, lines 54-67).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Bitragunta’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Bitragunta’s ideas into Lal-Bazelgette’s system.
However, Lal-Bazelgette- Bitragunta does not explicitly teach analyzing, by the one or more processors executing the trained AI model, at least one of (i) a timestamp corresponding with one or more of the one or more nodes having access to the distributed ledger or (ii) a block nonce of one or more blocks on the distributed ledger.
In similar art, Kaidi teaches block nonce of one or more blocks on the distributed ledger, (see Kaidi [0046]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Kaidi’s ideas into Lal-Bazelgette- Bitragunta’s system in order to save resources and development time by implying George’s ideas into Lal-Bazelgette- Bitragunta’s system.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Lal-Bazelgette in view of Zhu et al. (CN 113347162)
Regarding claim 11:
Lal-Bazelgette discloses the invention substantially as disclosed in claim 1, but does not explicitly teach receive, from a new node, an indication of a new input associated with cyber threat intelligence; evaluate a proof-of-contribution for the new node based on the indication; and allocate an increased level of voting power to the new node, in accordance with the proof-of-contribution.
In similar art, Zhu teaches a blockchain node contribution degree proof consensus method for group intelligent service, the method comprises: according to three aspects of node online time, local model quality and data contribution, competing the billing right of the blockchain distributed account book; based on the workload proof mechanism, the mining difficulty coefficient is dynamically adjusted, the greater the contribution degree, the lower the mining difficulty of the node is, the node participation fairness is improved; the participating nodes compete for the accounting right through the contribution degree proof consensus algorithm, so as to obtain the platform reward. the reward score can be used for downloading the shared parameter recorded on the blockchain for improving the quality of the local model; In order to avoid fake of the local model quality parameter, the intelligent contract is triggered to automatically verify the local model parameter of the group intelligent service participating node (see, abstract).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Zhu’s ideas into Lal-Bazelgette’s system in order to save resources and development time by implying Zhu’s ideas into Lal-Bazelgette’s system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAN DAI T TRUONG whose telephone number is (571)272-7959. The examiner can normally be reached Monday-Friday 7:00 Am to 3:00 PM.
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/LAN DAI T TRUONG/ Primary Examiner, Art Unit 2444