AI Decision Tree Trajectories for Adaptive Content Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems indiscriminately provide content through static rules, failing to react to variability across a population of data ingesters and thus sub-optimally handle data requests.
Innovation Solution
A communication decision tree is configured using machine-learning techniques to dynamically define individual trajectories, making iterative decisions based on user attributes and learned data to optimize content transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules are used to provide content through communication channels, then configuration simplicity and deterministic operation are achieved, but the system fails to react to variability across data ingesters and sub-optimally handle requests
Solution Approach 1:
The patent transforms static communication rules into dynamic decision nodes that adapt to individual data ingester characteristics. Machine learning models are trained on ingester data to generate personalized communication specifications, allowing the system to dynamically adjust content delivery based on observed patterns and behaviors of different ingesters.
Solution Approach 2:
The system changes communication parameters (timing, channel selection, content type) based on learned characteristics of individual data ingesters. By modifying these parameters dynamically rather than using fixed rules, the system achieves better adaptation to variability while maintaining manageable complexity through parameterized decision nodes.
2Productivity
If static rules are configured to consistently respond to data requests, then deterministic operation is maintained, but the system may sub-optimally handle requests due to inability to react to population variability
Solution Approach 1:
The system enables self-service by automatically training machine learning models on ingester data and generating optimized communication specifications without requiring manual configuration for each ingester. The decision nodes autonomously learn from data patterns and make adaptive decisions, improving productivity while reducing manual automation setup.
Solution Approach 2:
The system implements feedback loops where communication outcomes are observed and used to refine machine learning models. This continuous feedback mechanism allows the system to automatically improve its content delivery effectiveness over time, balancing automation extent with productivity enhancement.
3Adaptability or versatility
If machine-learning techniques are used to dynamically define individual trajectories, then adaptability to user-specific characteristics is enhanced, but system complexity increases
Solution Approach 1:
The patent segments the communication decision-making process into discrete decision nodes within a tree structure. Each node handles a specific aspect of communication specification (timing, channel, content), allowing complex adaptation to user characteristics to be broken down into manageable, modular components that reduce overall system complexity.
Solution Approach 2:
Different decision nodes in the communication tree are configured with specialized machine learning models tailored to specific communication aspects. This local quality approach allows each node to adapt to user characteristics in its specific domain without requiring the entire system to be uniformly complex, optimizing adaptability while managing complexity through specialization.
Data Source
AI summary
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.