Personalized Communication Prioritization via Deep Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional communication management systems fail to personalize communication prioritization based on user preferences, as they do not analyze content effectively and require manual user labeling and separate learning models for each user, leading to inefficiencies in ranking communications by urgency and actionability.
Innovation Solution
A method using deep learning models to extract natural language content from communications, generate representations, and assign priority labels by combining global and personalized learning models with user behavioral information, allowing for context-aware and metadata-aware prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional communication management systems use manual user labeling and separate learning models for each user, then communication prioritization can be customized to user preferences, but system complexity and operational effort increase significantly
Solution Approach 1:
The patent combines multiple separate learning models into a single unified deep learning model that processes communications for all users. This model integrates user behavioral information and preferences internally, eliminating the need for separate models per user while maintaining personalized prioritization. The consolidation reduces system complexity and operational overhead.
Solution Approach 2:
The system automatically extracts user behavioral information from communication patterns and metadata without requiring manual user labeling. The deep learning model self-adjusts to user preferences by analyzing interaction history, response times, and communication patterns, enabling personalized prioritization to emerge autonomously rather than through manual configuration.
2Measurement precision
If conventional systems do not analyze communication content effectively, then processing speed is maintained, but prioritization accuracy based on urgency and actionability deteriorates
Solution Approach 1:
The system extracts only the most relevant features from communication content, such as urgency indicators, actionability signals, and key metadata, rather than processing entire communication texts. This selective extraction maintains processing efficiency while improving prioritization accuracy by focusing computational resources on discriminative features.
Solution Approach 2:
The deep learning model transforms communication content into optimized parameter representations that capture urgency and actionability characteristics. By converting raw communication data into structured feature vectors with specific parameter transformations, the system achieves both high processing speed and accurate prioritization based on content analysis.
3Reliability
If manual user labeling is required for communication prioritization, then training data can be obtained, but time consumption and operational overhead increase
Solution Approach 1:
The system automatically generates training data by extracting user behavioral information from existing communication patterns, response times, and interaction metadata. The deep learning model learns from these naturally occurring behavioral signals without requiring manual labeling, eliminating time-consuming data preparation while maintaining high-quality training data that reflects actual user preferences.
Solution Approach 2:
The system utilizes feedback from user communication behaviors, such as response times, message prioritization actions, and interaction patterns, to continuously refine and update training data. This feedback mechanism ensures training data quality improves over time automatically, without manual intervention for data collection or labeling.
Data Source
AI summary
One embodiment provides a method comprising extracting natural language content from a piece of communication for a user, generating a representation of the piece of communication based on the natural language content extracted, and utilizing a global deep learning model and a personalized learning model for the user to assign a priority label to the piece of communication based on the representation and user behavioral information associated with recent conversations of the user. Another embodiment provides a method comprising, for each piece of communication of a set of multiple pieces of communication for multiple users, extracting natural language content from the piece communication and generating a representation of the piece of communication based on the natural language extracted, and training a deep learning neural network to predict a degree of priority of a subsequent piece of communication based on each representation generated.