AI Decision Tree for Dynamic Communication Trajectories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content delivery systems fail to optimally handle variability in user data ingesters by using static rules, leading to suboptimal communication and content transmission strategies.
Innovation Solution
A computer-implemented method utilizing a machine-learning-based communication decision tree that dynamically defines communication specifications for individual user trajectories, retrieving learned data to tailor content transmission based on user attributes and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static rules are used to indiscriminately provide content through the same communication channel to each data ingester, then configuration simplicity and deterministic operation are achieved, but the system fails to react to variability across users and suboptimally handles requests
Solution Approach 1:
The patent transforms the static communication decision system into a dynamic one by implementing a communication decision tree that adapts its trajectories based on user data. The system dynamically selects communication channels and content based on real-time user attributes, preferences, and behaviors, allowing the configuration to change adaptively while maintaining operational determinism through the structured tree architecture.
Solution Approach 2:
The system changes parameters such as communication channel selection, content type, and transmission timing based on user-specific data. By modifying these parameters dynamically according to user attributes and preferences, the system achieves adaptability without sacrificing configuration manageability, as the changes follow predefined decision tree paths.
2Reliability
If static rules are applied consistently to all data requests, then deterministic operation is maintained, but communication effectiveness is reduced due to inability to personalize content delivery
Solution Approach 1:
The patent segments the user population into different trajectories within the communication decision tree based on user data characteristics. Each segment receives personalized communication specifications appropriate to their attributes, preferences, and behaviors. This segmentation enables personalized content delivery while maintaining deterministic operation within each segment through the structured decision tree paths.
Solution Approach 2:
The system applies different communication specifications to different user segments based on their local characteristics. Each user trajectory through the decision tree receives customized communication parameters tailored to their specific attributes, achieving local optimization of communication effectiveness while maintaining global system reliability through the consistent tree structure.
3Adaptability or versatility
If a communication decision tree with multiple branching nodes is implemented to handle user variability, then adaptability to user data is improved, but system complexity increases
Solution Approach 1:
The communication decision tree is implemented as a dynamic structure that adapts to user data while maintaining a manageable complexity through its hierarchical organization. The tree dynamically selects appropriate trajectories based on user attributes, allowing the system to handle user variability without requiring complex ad-hoc decision logic for each user.
Solution Approach 2:
The decision tree structure is pre-configured with branching nodes and trajectories before operation. This preliminary structuring of decision logic allows the system to handle user variability through pre-planned adaptation paths, reducing the complexity of real-time decision-making while maintaining high adaptability to different user characteristics.
4Productivity
If machine learning techniques are used to dynamically define communication specifications, then personalized content delivery is achieved, but computational resources and processing time are increased
Solution Approach 1:
Machine learning models are trained in advance to learn patterns in user data and predict optimal communication specifications. This preliminary training allows the system to make personalized communication decisions during operation by applying the pre-learned models to user trajectories, achieving personalization effectiveness while reducing real-time computational resource consumption.
Solution Approach 2:
The system uses learned patterns and models as copies of optimal communication strategies that can be rapidly applied to multiple users. Instead of performing complex computations for each user in real-time, the system copies and applies pre-learned communication specifications based on user similarity and trajectory matching, reducing computational overhead while maintaining personalization effectiveness.
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.