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US 2019/0102574 A1Published application
Machine-learning-based processing of de-obfuscated data for data enrichment
AssigneeOracle International Corporation
InventorsTara U. Roberts, Jonathan Stanesby, Michael Pearmain, Kevin Yordy, Karen Sutthakorn
Life of this patent
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Abstract
Embodiments relate to de-obfuscated data to process at one or more artificial-intelligence (AI) decision nodes throughout a communication decision tree. Through processing directed by the communication decision tree, additional de-obfuscated data is collected and securely transmitted in de-obfuscated or re-obfuscated form to enrich underlying data structures.
Claims20
A computer-implemented method comprising:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifiers of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- retrieving learned data generated by training a machine-learning model using other user data;
- executing the machine-learning model configured with the learned data to process at least part of the user data;
- identifying one or more communication specifications based on the execution of the machine-learning model configured with the learned data;
- causing content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications;
- and
- generating non-obfuscated communication-activity data for the non-obfuscated profile identifier based on any communications detected in response to the content transmission;
- obfuscating the non-obfuscated communication-activity data to generate a set of obfuscated data for the set of obfuscated identifiers;
- transmitting at least part of the set of obfuscated data to the client system;
- and
- causing, for each of at least some of the set of obfuscated identifiers, at least some of the non-obfuscated communication-activity data to be stored in association with the non-obfuscated profile identifier to the data management system.
12 more elements
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifiers of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- retrieving learned data generated by training a machine-learning model using other user data;
- executing the machine-learning model configured with the learned data to process at least part of the user data;
- identifying one or more communication specifications based on the execution of the machine-learning model configured with the learned data;
- causing content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications;
- and
- generating non-obfuscated communication-activity data for the non-obfuscated profile identifier based on any communications detected in response to the content transmission;
- obfuscating the non-obfuscated communication-activity data to generate a set of obfuscated data for the set of obfuscated identifiers;
- transmitting at least part of the set of obfuscated data to the client system;
- and
- causing, for each of at least some of the set of obfuscated identifiers, at least part of the non-obfuscated communication-activity data to be stored in association with the non-obfuscated profile identifier to the data management system.
12 more elements
A system comprising:
- one or more data processors;
- and
- a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including:
- receiving, from a client system, a request to perform a machine-learning communication workflow;
- receiving, in association with the request, a set of obfuscated identifiers for which processing via the machine-learning communication workflow is requested, wherein each obfuscated identifiers of the set of obfuscated identifiers corresponds to an identification of an obfuscated version of a profile stored at a data management system, the obfuscated version lacking personally identifiable information (PII);
- for each obfuscated identifier in the set of obfuscated identifiers:
- mapping the obfuscated identifier to a non-obfuscated identifier that identifies a non-obfuscated version of the profile that includes PII;
- retrieving, from the data management system, user data from the non-obfuscated version;
- retrieving learned data generated by training a machine-learning model using other user data;
- executing the machine-learning model configured with the learned data to process at least part of the user data;
- identifying one or more communication specifications based on the execution of the machine-learning model configured with the learned data;
- causing content to be transmitted to a destination address identified in the user data in accordance with the one or more communication specifications;
- and
- generating non-obfuscated communication-activity data for the non-obfuscated profile identifier based on any communications detected in response to the content transmission;
- obfuscating the non-obfuscated communication-activity data to generate a set of obfuscated data for the set of obfuscated identifiers;
- transmitting at least part of the set of obfuscated data to the client system;
- and
- causing, for each of at least some of the set of obfuscated identifiers, at least some of the non-obfuscated communication-activity data to be stored in association with the non-obfuscated profile identifier to the data management system.
15 more elements
Dependencies are inferred from the claim text.