DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/18/2025 has been entered.
Claim Status
Claims 1, 4-6, 8, 10-12, 14-15, 18, 21-22, 24-25, 27-29 are pending.
Response to Arguments
Double Patenting Rejection: Double patenting rejection is withdrawn in view of applicant’s amendment.
103 Rejection: Applicant’s arguments with respect to claims 1, 4-6, 8, 10-12, 14-15, 18, 21-22, 24-25, 27-29 have been fully considered and are persuasive. Upon further consideration, and in view of applicant’s amendments, a new grounds of rejection is made in view of newly cited reference Tsou.
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, 4, 6, 8-12, 14-15, 18, 22, 24-25, and 27-29 are rejected under 35 U.S.C. 103 as being unpatentable over Feng et al (US 20200320488 A1) hereafter Feng in view of Dods et al (US 10873456 B1) hereafter Dods further in view of Tsou et al (US 20190213446 A1) hereafter Tsou
Regarding claim 1, Feng teaches a computer-implemented method comprising: obtaining data, from one or more data sources, pertaining to one or more events involving a digital asset (Para 0006, a client device sends a request to a blockchain network to initiate a transfer of a digital ticket from the blockchain network to a target server), wherein obtaining data comprises storing at least portions of the data as object record entities in association with corresponding information identifying (i) one or more event participants (Para 0029, the conceptual architecture 200 includes participant systems 202, 204, 206 that correspond to Participant A, Participant B, and Participant C, respectively), (ii) one or more event-based temporal parameters (Para 0043, an electronic coupon that can be used by consumers at the time of consumption to purchase a product at a discounted price according to a discount rate indicated by the coupon), and (iii) data exchanged in connection with the one or more events (Para 0043, a digital ticket 310 is a virtual instance of a ticket for claiming goods or services); generating a digital asset information chain associated with the digital asset by processing at least a portion of the obtained data using at least one cryptographic function and linking that at least a portion of the obtained data in accordance with at least one temporal parameter(Para 0052, the blockchain network 304 can generate a blockchain transaction corresponding to the transfer request, and withhold the digital ticket 310 based on the blockchain transaction using a smart contract to prevent further operations on the digital ticket 310 for a predetermined time), wherein processing at least a portion of the obtained data using at least one cryptographic function comprises processing the at least a portion of the obtained data using at least one message digest algorithm (Para 0023, Each block in the chain is linked to a previous block immediately before it in the chain by including a cryptographic hash of the previous block) (“cryptographic hash” teaches “message digest algorithm”).
Feng does not appear to explicitly teach performing anomaly detection by processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques, wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing the at least a portion of the digital asset information chain associated with the digital asset using at least one neural network-based autoencoder; and performing one or more automated actions based at least in part on one or more of the digital asset information chain and results from the anomaly detection, wherein performing one or more automated actions comprises storing the digital asset information chain in at least one graph database using at least one resource description framework and implementing permissioned application programming interface-based access to the stored digital asset information chain in connection with at least one designated application programming interface query language related to the at least one graph database; wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
In analogous art, Dods teaches performing anomaly detection by processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques, wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing the at least a portion of the digital asset information chain associated with the digital asset using at least one neural network-based autoencoder (Column 15 lines 45-53, One implementation of an autoencoder semi-supervised statistical machine learner is representative neural network 311 illustrated by FIG. 3B, in which a neural network 311 is used for implementing autoencoding to detect anomalous data, such as a spam, corrupt data, or data indicative of an anomaly in compliance reporting, data from clinical trials, and/or data from manufacturing and distribution processes or other bad data in the block chain); and performing one or more automated actions based at least in part on one or more of the digital asset information chain and results from the anomaly detection, wherein performing one or more automated actions comprises storing the digital asset information chain in at least one graph database using at least one resource description framework and implementing permissioned application programming interface-based access to the stored digital asset information chain in connection with at least one designated application programming interface query language related to the at least one graph database wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Columns 7 lines 9-14, Once compiled, the chain code is uploaded to peer server(s) 136 of the blockchain network 106 which assign a unique address to each chain code. In permissioned blockchain systems, such as Hyperledger Fabric™, a node in the network can read electronic transactions for which it has permission). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Feng in view of Dods teaches processing the at least a portion of the digital asset information chain associated with the digital asset. However, Feng in view of Dods does not appear to explicitly teach wherein performing anomaly detection comprises performing multi-variate anomaly detection by processing the at least a portion of the digital asset information chain associated with the digital asset using at least one isolation forest model, wherein using the at least one isolation forest model comprises (i) creating multiple decision trees over multiple data attributes, within the at least a portion of the digital asset information chain, and (ii) detecting at least one anomaly using the multiple decision trees, based at least in part on determining numbers of splits within respective ones of the multiple decision trees using at least one partitioning algorithm.
In analogous art, Tsou teaches wherein performing anomaly detection comprises performing multi-variate anomaly detection by using at least one isolation forest model, wherein using the at least one isolation forest model comprises (i) creating multiple decision trees over multiple data attributes, within the at least a portion of the digital asset information chain, and (ii) detecting at least one anomaly using the multiple decision trees, based at least in part on determining numbers of splits within respective ones of the multiple decision trees using at least one partitioning algorithm (Para 0113, generate a first instance of the random forest model from the first and second plurality of decision trees, and detect anomalies in data generated by the first set of sensors using the first instance of the random forest model). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Feng in view of Dods to include the teaching of Tsou. One of ordinary skill in the art would be motivated to implement this modification in order to detect anomalies, as taught by Tsou (Abs, The random forest model is used by the particular device to detect anomalies in data generated by the particular device).
Regarding claim 4, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein storing the digital asset information chain in at least one graph database comprises storing the digital asset information chain using at least one labeled property graph (Feng, Para 0023, A Merkle tree is a data structure in which data at the leaf nodes of the tree is hashed, and all hashes in each branch of the tree are concatenated at the root of the branch).
Regarding claim 6, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing at least a portion of the digital asset information chain associated with the digital asset using at least one deep learning algorithm (Dods, Column 12 lines 7-9, Para 5, a method for establishing a trained machine learning classifier using a blockchain network). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Regarding claim 8, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein processing at least a portion of the obtained data using at least one cryptographic function comprises processing at least a portion of the obtained data using at least one hashing algorithm (Feng, Para 0023, Each block in the chain is linked to a previous block immediately before it in the chain by including a cryptographic hash of the previous block. Each block also includes a timestamp, its own cryptographic hash, and one or more transactions).
Regarding claim 9, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein processing at least a portion of the obtained data using at least one cryptographic function comprises processing at least a portion of the obtained data using at least one message digest algorithm (Feng, Para 0039, For example, if two nodes want to keep a transaction private, such that other nodes in the blockchain network cannot discern details of the transaction, the nodes can encrypt the transaction data).
Regarding claim 10, Feng in view of Dods Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein generating a digital asset information chain comprises, for each of multiple records within the obtained data, creating one of a unique message digest and a hash of a temporally preceding record and storing the unique message digest or hash in conjunction with a temporally subsequent record (Feng, Para 0033, Before storing in a block, the transaction data is hashed. Hashing is a process of transforming the transaction data (provided as string data) into a fixed-length hash value).
Regarding claim 11, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 10, as shown above. Feng in view of Dods further teaches further comprising: creating one of a unique message digest and a hash of the temporally subsequent record and storing the unique message digest or hash of the temporally subsequent record with the unique message digest or hash of the temporally preceding record (Feng, Para 0033, Before storing in a block, the transaction data is hashed. Hashing is a process of transforming the transaction data (provided as string data) into a fixed-length hash value).
Regarding claim 12, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein performing one or more automated actions comprises automatically training the one or more machine learning techniques using at least a portion of the results from the anomaly detection (Dods, Column 4 lines 65-66, Para 34, Disclosed are system and method implemented machine learning driven detection, classification, resolution and root cause analysis and blockchain-validated reporting enabling implementations to track and respond in near-real-time to anomalies such as out-of-spec asset reports in critically important supply scenarios without sacrificing security). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Regarding claim 14, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods further teaches wherein processing data comprises implementing multi-party authentication, in connection with (i) at least one entity associated with at least one of the one or more data sources and (ii) the digital asset, with respect to the data (Feng, Para 0044, In response to the request, the blockchain network 304 authenticates the client device 302 based on the identity information in the request).
Claim 15 is a medium claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 18 is an apparatus claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 22 is the apparatus claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claim 24 is the apparatus claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 25 id the apparatus claim corresponding to the method claim 8, and is analyzed and rejected accordingly.
Claim 26 is the apparatus claim corresponding to the method claim 9, and is analyzed and rejected accordingly.
Regarding claim 27, Feng in view of Dods further in view of Tsou teaches the non-transitory processor-readable storage medium of claim 15, wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing at least a portion of the digital asset information chain associated with the digital asset using at least one unsupervised decision tree-based shallow learning algorithm (Dods, Column 12 lines 7-9, establishing a trained machine learning classifier using unsupervised machine learning in a blockchain network). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Regarding claim 28, Feng in view of Dods further in view of Tsou teaches the non-transitory processor-readable storage medium of claim 15, wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing at least a portion of the digital asset information chain associated with the digital asset using at least one deep learning algorithm (Dods, Column 12 lines 7-9, establishing a trained machine learning classifier using unsupervised machine learning in a blockchain network). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Regarding claim 29, Feng in view of Dods further in view of Tsou teaches the non-transitory processor-readable storage medium of claim 15, wherein storing the digital asset information chain in at least one graph database comprises storing the digital asset information chain using at least one labeled property graph (Dods, Column 15 lines 16-19, a trained machine learning classifier using semi-supervised machine learning for classifying nodes in a blockchain network in a healthy blockchain network state(s) or an unhealthy blockchain network state(s)). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify to modify Feng to include the teaching of Dods. One of ordinary skill in the art would be motivated to implement this modification in order to perform blockchain validation, as taught by Dods (Column 5 lines 10-12, The technology disclosed relates to machine learning-based systems and methods used for block chain validated documents, and more particularly to machine learning-based systems and methods using block chain to validate documents).
Claims 5 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Dods further in view of Tsou further in view of Klein (US 20180211115 A1) hereafter Klein
Regarding claim 5, Feng in view of Dods further in view of Tsou teaches the computer-implemented method of claim 1, as shown above. Feng in view of Dods does not appear to explicitly teach wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing at least a portion of the digital asset information chain associated with the digital asset using at least one unsupervised decision tree- based shallow learning algorithm.
In analogous art, Klein teaches wherein processing at least a portion of the digital asset information chain associated with the digital asset using one or more machine learning techniques comprises processing at least a portion of the digital asset information chain associated with the digital asset using at least one unsupervised decision tree-based shallow learning algorithm (Para 0032, The API services further comprise analytics API 204 that uses machine learning and non-machine learning techniques such as decision tree learning, supervised learning, unsupervised learning, etc.,). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Feng in view of Dods to include the teaching of Klein. One of ordinary skill in the art would be motivated to implement this modification in order to conduct analysis, as taught by Klein (Para 0032, the analytics API 204 can utilize user preference settings and/or derive data from past or similar missions to conduct analysis).
Claim 21 is the apparatus claim corresponding to claim 5, and is analyzed and rejected accordingly.
Conclusion
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166