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US 2019/0102679 A1Published application
Directed trajectories through communication decision tree using iterative artificial intelligence
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 configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree
Claims20
A computer-implemented method comprising:
- accessing a data structure representing a communication decision tree configured to dynamically define individual trajectories through the communication decision tree using a machine-learning technique to indicate a series of communication specifications, the communication decision tree including a set of branching nodes, each branching node of the set of branching nodes corresponding to an action point configured to identify a direction for a given trajectory;
- detecting, at a first time, that a trajectory through the communication decision tree has reached a first branching node of the set of branching nodes, the trajectory being associated with a particular user;
- in response to the detecting that the trajectory has reached the first branching node:
- retrieving first learned data generated by processing first user data using a machine-learning technique, the first user data including user attributes for a set of other users;
- retrieving one or more particular user attributes associated with the particular user;
- identifying one or more first communication specifications based on the first learned data and the one or more particular user attributes;
- and
- causing first content to be transmitted to a user device associated with the particular user in accordance with the one or more first communication specifications;
- detecting, at a second time that is after the first time, that the trajectory through the communication decision tree has reached a second branching node of the set of branching nodes;
- and
- in response to the detecting that the trajectory has reached the second branching node:
- retrieving second learned data generated by processing second user data using the machine-learning technique, the second user data including at least some user attributes not included in the first user data;
- identifying one or more second communication specifications based on the second learned data and at least some of the one or more particular user attributes;
- and
- causing second content to be transmitted to the user device in accordance with the one or more second communication specifications.
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 actions including:
- accessing a data structure representing a communication decision tree configured to dynamically define individual trajectories through the communication decision tree using a machine-learning technique to indicate a series of communication specifications, the communication decision tree including a set of branching nodes, each branching node of the set of branching nodes corresponding to an action point configured to identify a direction for a given trajectory;
- detecting, at a first time, that a trajectory through the communication decision tree has reached a first branching node of the set of branching nodes, the trajectory being associated with a particular user;
- in response to the detecting that the trajectory has reached the first branching node:
- retrieving first learned data generated by processing first user data using a machine-learning technique, the first user data including user attributes for a set of other users;
- retrieving one or more particular user attributes associated with the particular user;
- identifying one or more first communication specifications based on the first learned data and the particular user attributes;
- and
- causing first content to be transmitted to a user device associated with the particular user in accordance with the one or more first communication specifications;
- detecting, at a second time that is after the first time, that the trajectory through the communication decision tree has reached a second branching node of the set of branching nodes;
- and
- in response to the detecting that the trajectory has reached the second branching node:
- retrieving second learned data generated by processing second user data using the machine-learning technique, the second user data including at least some user attributes not included in the first user data;
- identifying one or more second communication specifications based on the second learned data and at least some of the one or more particular user attributes;
- and
- causing second content to be transmitted to the user device in accordance with the one or more second communication specifications.
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 actions including:
- accessing a data structure representing a communication decision tree configured to dynamically define individual trajectories through the communication decision tree using a machine-learning technique to indicate a series of communication specifications, the communication decision tree including a set of branching nodes, each branching node of the set of branching nodes corresponding to an action point configured to identify a direction for a given trajectory;
- detecting, at a first time, that a trajectory through the communication decision tree has reached a first branching node of the set of branching nodes, the trajectory being associated with a particular user;
- in response to the detecting that the trajectory has reached the first branching node:
- retrieving first learned data generated by processing first user data using a machine-learning technique, the first user data including user attributes for a set of other users;
- retrieving one or more particular user attributes associated with the particular user;
- identifying one or more first communication specifications based on the first learned data and the particular user attributes;
- and
- causing first content to be transmitted to a user device associated with the particular user in accordance with the one or more first communication specifications;
- detecting, at a second time that is after the first time, that the trajectory through the communication decision tree has reached a second branching node of the set of branching nodes;
- and
- in response to the detecting that the trajectory has reached the second branching node:
- retrieving second learned data generated by processing second user data using the machine-learning technique, the second user data including at least some user attributes not included in the first user data;
- identifying one or more second communication specifications based on the second learned data and at least some of the one or more particular user attributes;
- and
- causing second content to be transmitted to the user device in accordance with the one or more second communication specifications.
15 more elements
Dependencies are inferred from the claim text.