ML Engine for Personalized Content via Semantic Message Segmentation
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Solution Overview
Problem
Current computer-based platforms lack the ability to generate personalized and contextually relevant content for customer communications, leading to inefficiencies in marketing campaigns and customer engagement.
Innovation Solution
A method involving a machine learning engine that analyzes customer messages to determine semantic scores across various categories, identifies impactful categories, and generates personalized content for targeted audiences, incorporating techniques such as splitting messages into subcomponents, analyzing semantic categories, and adjusting content for optimal engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computer-based platforms are used for customer communications, then the system complexity remains low, but the ability to generate personalized and contextually relevant content is insufficient
Solution Approach 1:
The patent segments customer messages into multiple semantic categories (sentiment, emotion, perceived message type, semantic relatedness, feeling, tone, perception, micro structure, and emotional intelligence). This segmentation allows the system to analyze different aspects of customer communications independently and generate personalized content by combining insights from each category, thereby achieving high adaptability without overwhelming system complexity.
Solution Approach 2:
The system changes parameters by introducing semantic numerical scores for each message and category. These scores transform qualitative message attributes into quantifiable metrics that can be processed algorithmically. By adjusting and comparing these numerical scores across multiple semantic categories, the system generates personalized content dynamically while maintaining manageable complexity through standardized parameter transformation.
2Measurement precision
If semantic analysis across multiple categories is performed for each customer message, then content personalization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining the nine semantic categories and their corresponding numerical scoring systems before actual message processing. This pre-configuration allows the system to quickly map incoming messages to established categories without performing complex real-time analysis for category definition, thereby maintaining high measurement precision while reducing processing time through prepared analytical frameworks.
Data Source
AI summary
Systems and methods associated with generation and/or provision of predictive content are disclosed. One exemplary method includes receiving communications associated with a plurality of customers; determining a message type for each message of the communications; splitting first messages of the first message type into a first set of subcomponent text sections; splitting second messages of the second message type into a second set of subcomponent text sections; analyzing the first set and the second set to generate a plurality of semantic numerical scores for each respective subcomponent text section; determining at least one impactful semantic category for a target audience by selecting at least one semantic category corresponding to at least one semantic numerical score of at least one subcomponent text section of the first set or the second set that is equal to or higher than a first pre-determined threshold value; generating personalized textual content targeting the audience.