Communication Attribute Assignment Using Learning Decision Engine
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Solution Overview
Problem
The increasing volume of electronic communications received by devices is time-consuming for users to organize, as existing systems lack automation in categorizing and responding to various types of multimedia communications.
Innovation Solution
A system and method that includes a decision engine with a communication analyzer to assign attributes to communications, using natural language processing and learning or rule-based systems to classify and prioritize actions, allowing users to efficiently manage communications by selecting from attributed folders or actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually analyze and organize each communication, then classification accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-service by automatically analyzing communication content and assigning attributes without requiring manual user intervention. The decision engine autonomously processes communications, identifies concepts, and determines appropriate folders or actions, eliminating the time-consuming manual analysis while maintaining classification accuracy through automated intelligent processing.
Solution Approach 2:
The patent replaces the mechanical manual process of reading and categorizing communications with an automated decision engine that uses natural language processing and artificial intelligence. This substitution eliminates human labor while improving speed and consistency in classification, directly addressing the time consumption issue.
2Productivity
If the system processes large volumes of communications, then productivity is improved, but complexity of the system increases
Solution Approach 1:
The system segments the communication processing task into distinct functional modules: the decision engine for analysis, the monitoring module for tracking user actions, and the feedback mechanism for learning. This segmentation allows each component to handle specific aspects of processing large communication volumes independently, improving overall productivity while keeping individual module complexity manageable.
Solution Approach 2:
The feedback mechanism allows the system to learn from user actions and improve its classification accuracy over time. By monitoring which folders or actions users select and using this feedback to refine the decision engine's criteria, the system handles large communication volumes effectively while adapting to user needs, managing complexity through intelligent learning rather than rigid predetermined rules.
3Loss of time
If the system automatically classifies communications, then time consumption is reduced, but reliability of classification decreases due to lack of human judgment
Solution Approach 1:
The feedback mechanism monitors user actions and uses this information to continuously improve the decision engine's classification criteria. By learning from actual user selections and corrections, the system maintains high reliability in automated classification, adapting its intelligence to match user judgment standards over time.
Solution Approach 2:
The classification system is dynamic rather than static, continuously evolving its criteria based on feedback from user interactions. This dynamic adaptation allows the automated system to improve its reliability by learning from actual usage patterns, ensuring that automated classifications align with user expectations and needs.
Data Source
AI summary
A system and method for determining a set of attributes to a communication includes a decision engine, a monitoring module, and application software. The decision engine receives communications and assigns a set of attributes to each received communication. Each communication and associated set of attributes is sent to the communication's corresponding application which processes the set of attributes for performing an action, such as display. The monitoring module monitors an item selected by the system user. The monitoring module may feed the selected item and associated communication back to the decision module. The decision engine may process the feedback on-line or in real-time. The decision module is a learning system that updates classification criteria using feedback. Classification scores associated with each set of attributes may represent an estimate of the statistical likelihood that each attribute is the proper response to the communication.