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 .
Claim Status
Claims 1, 4-6, 8, 10-12, 14-15, 18, 21-22, 24-25, and 27-29 are pending.
Response to Arguments
Applicant’s arguments with respect to claims 1, 4-6, 8, 10-12, 14-15, 18, 21-22, 24-25, and 27-29 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 Wenzel et al (US 20170286456 A1) hereafter Wenzel in view of Jayachandran (US 20200380154 A1) hereafter Jayachandran in view of Gholami et al (US 20220068501 A1) hereafter Gholami
Regarding claim 1, Wenzel teaches a computer-implemented method comprising: obtaining data, from one or more data sources, pertaining to one or more events involving a digital asset, 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, (ii) one or more event-based temporal parameters, and (iii) data exchanged in connection with the one or more events, wherein the one or more data sources comprise a plurality of heterogeneous enterprise applications and databases, and wherein the one or more events comprise one or more enterprise asset lifecycle touchpoint interactions between the digital asset and respective stakeholder entities comprising the plurality of heterogeneous enterprise applications and databases (Para 0005, the standards specification and management system may be used in the biotech/pharmaceutical industry to more easily and efficiently manage metadata that specify or are otherwise associated with standards for data collection, data tabulation, data submission and data analysis for a drug study); 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, wherein the at least one resource description framework formats information pertaining to the digital asset information chain as multiple subject-predicate-object triples, wherein each subject-predicate-object triple identifies one or more stakeholder entity nodes, one or more corresponding relationship edges, and one or more digital asset nodes (Para 0056, standards specification and management system 18 may automatically generate ontology schema 24 and/or ontology instance data 26 in a format that conforms to a graph-based data model that represents data as a set of “subject-predicate-object” triples, such as, e.g., the 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 (Para 0104, the techniques of this disclosure may provide an enterprise application with governance, collaboration, work items, notification, standards proposals, external application programming interface (API), and an extension framework to control and produce a standard specification).
Wenzel does not appear to explicitly teach 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, 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.
In analogous art, Jayachandran teaches 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, 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 0059, A common hashing algorithm used on blockchain is Secure Hashing Algorithm 256 (commonly shortened to SHA-256), however, many others are possible such as MD5 (message digest algorithm), and the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel to include the teaching of Jayachandran. One of ordinary skill in the art would be motivated to implement this modification in order to reduce blockchain computation, as taught by Jayachandran (Para 0061, Some benefits of the instant solutions described and depicted herein include reduced computational effort by one or more nodes within a blockchain while still maintaining the overall correctness and security of the blockchain).
Wenzel in view of Jayachandran 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 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; 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.
In analogous art, Gholami 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 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 (Para 0151, In some embodiments, labeled data does not exist (i.e., unlabeled dataset) and algorithms such as, but not limited to, isolation forest, and/or neural autoencoder, are used for outlier and anomaly detection); 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 (Para 0135, For example, one or more decision scores can be calculated for a given transaction by a single or multiple classifiers, and the action would be flagged as potentially fraudulent if its associated decision score exceeds a pre-determined decision threshold, and/or if some or most of the multiple decision scores exceed their corresponding pre-determined decision thresholds). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel in view of Jayachandran to include the teaching of Gholami. One of ordinary skill in the art would be motivated to implement this modification in order to track the lifecycle of digital assets, as taught by Gholami (Abs, The present disclosure describes a system, apparatus, and method involving immutable databases such as blockchains to track and document transportation and administration of sensitive medications and identify potential cases of drug diversion).
Regarding claim 4, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Wenzel, Para 0056, standards specification and management system 18 may automatically generate ontology schema 24 and/or ontology instance data 26 in a format that conforms to a graph-based data model that represents data as a set of “subject-predicate-object” triples, such as, e.g., the Resource Description Framework (RDF)).
Regarding claim 5, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Gholami, Para 0188, An autoencoder is a neural network apparatus (with an input layer, one or more hidden layers, and an output layer) and it is capable of encoding data (i.e., performing dimensionality reduction) in an unsupervised manner). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel in view of Jayachandran to include the teaching of Gholami. One of ordinary skill in the art would be motivated to implement this modification in order to track the lifecycle of digital assets, as taught by Gholami (Abs, The present disclosure describes a system, apparatus, and method involving immutable databases such as blockchains to track and document transportation and administration of sensitive medications and identify potential cases of drug diversion)..
Regarding claim 6, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Gholami, Para 0182, a computer vision-based software system automatically confirms if the vial or ampule or pack is empty in an image using deep learning and other classification methods). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel in view of Jayachandran to include the teaching of Gholami. One of ordinary skill in the art would be motivated to implement this modification in order to track the lifecycle of digital assets, as taught by Gholami (Abs, The present disclosure describes a system, apparatus, and method involving immutable databases such as blockchains to track and document transportation and administration of sensitive medications and identify potential cases of drug diversion).
Regarding claim 8, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Jayachandran, Para 0059, Hashing in blockchain refers to the process of having an input item of whatever length reflecting an output item of a fixed length). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel to include the teaching of Jayachandran. One of ordinary skill in the art would be motivated to implement this modification in order to reduce blockchain computation, as taught by Jayachandran (Para 0061, Some benefits of the instant solutions described and depicted herein include reduced computational effort by one or more nodes within a blockchain while still maintaining the overall correctness and security of the blockchain).
Regarding claim 10, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Jayachandran, Para 0059, A common hashing algorithm used on blockchain is Secure Hashing Algorithm 256, however, many others are possible such as MD5 (message digest algorithm), and the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel to include the teaching of Jayachandran. One of ordinary skill in the art would be motivated to implement this modification in order to reduce blockchain computation, as taught by Jayachandran (Para 0061, Some benefits of the instant solutions described and depicted herein include reduced computational effort by one or more nodes within a blockchain while still maintaining the overall correctness and security of the blockchain).
Regarding claim 11, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 10, 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 (Jayachandran, Para 0059, A common hashing algorithm used on blockchain is Secure Hashing Algorithm 256, however, many others are possible such as MD5 (message digest algorithm), and the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel to include the teaching of Jayachandran. One of ordinary skill in the art would be motivated to implement this modification in order to reduce blockchain computation, as taught by Jayachandran (Para 0061, Some benefits of the instant solutions described and depicted herein include reduced computational effort by one or more nodes within a blockchain while still maintaining the overall correctness and security of the blockchain).
Regarding claim 12, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, 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 (Gholami, Para 0151, In some embodiments, labeled data does not exist (i.e., unlabeled dataset) and algorithms such as, but not limited to, isolation forest, and/or neural autoencoder, are used for outlier and anomaly detection). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Wenzel in view of Jayachandran to include the teaching of Gholami. One of ordinary skill in the art would be motivated to implement this modification in order to track the lifecycle of digital assets, as taught by Gholami (Abs, The present disclosure describes a system, apparatus, and method involving immutable databases such as blockchains to track and document transportation and administration of sensitive medications and identify potential cases of drug diversion).
Regarding claim 14, Wenzel in view of Jayachandran in view of Gholami teaches the computer-implemented method of claim 1, wherein obtaining 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 (Wenzel, Para 0193, The business logic layer includes a local Java API (e.g., beanshell and Jython), a Lightweight Directory Access Protocol (LDAP) interface for authentication, and a Simple Mail Transfer Protocol (SMTP) interface for mail).
Claim 15 is the medium claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 21 is the apparatus claim corresponding to the method claim 5, 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 is the apparatus claim corresponding to the method claim 8, and is analyzed and rejected accordingly.
Claim 27 is the apparatus claim corresponding to the method claim 5, and is analyzed and rejected accordingly.
Claim 28 is the apparatus claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claim 29 is the apparatus claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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