NLG Content Recommendation for Scalable Personalized Messaging

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Solution Overview

Problem

Existing computer network platforms fail to personalize communications effectively, resulting in irrelevant content delivery to customers due to the mass volume of recipients, leading to reduced customer engagement and satisfaction.

Innovation Solution

Implementing a content recommendation engine utilizing natural language generation (NLG) machine learning models to analyze customer preferences and generate personalized content based on sentiment analysis and correlation identification, enabling targeted and engaging communications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If mass communication is used to reach large volumes of customers, then the quantity of customers served increases, but the relevance and personalization of content deteriorates

Engineering Contradiction:

Improvevolume of customers servedVSAvoidpersonalization of content

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent segments the customer base into distinct groups based on analyzed preferences and behaviors, allowing personalized content delivery to each segment while maintaining scalability. The system divides the mass audience into manageable segments that can receive tailored communications without requiring individualized manual processing of each customer.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically changes communication parameters such as content type, messaging style, timing, and channel based on analyzed customer preferences. By adjusting these parameters according to segment characteristics, the system maintains personalization relevance while serving large volumes of customers through automated parameter optimization.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If automated content delivery is implemented to handle mass volumes, then productivity increases, but the quality and relevance of content personalization deteriorates

Engineering Contradiction:

Improvecontent delivery efficiencyVSAvoidprecision of content personalization

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system performs preliminary analysis of customer preferences, behaviors, and segment characteristics before content delivery. By pre-processing and storing customer segmentation data and preferences in advance, the system enables automated delivery to maintain high personalization precision without real-time processing delays, thus preserving both productivity and personalization quality.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms that analyze customer responses and interactions with delivered content, continuously refining segmentation accuracy and preference profiles. This feedback loop enables the automated system to improve personalization precision over time while maintaining high productivity through scalable automated processing of feedback data.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12475478B2Computer-based systems involving machine learning associated with generation of recommended content and methods of use thereof

Publication Date: 2025.11.18 CAPITAL ONE SERVICES LLC

AI summary

Systems and methods associated with providing content personalization are disclosed. In one embodiment, an exemplary method may comprise receiving first data including content preferences associated with an audience, receiving second data including an initial digital message being proposed for transmission to the audience, generating a recommendation data set based at least in part on the first data and the second data, wherein the recommendation data set identifies at least one recommended content type and at least one recommended message type, determining via a natural language generation machine learning model suggested content for the audience, and providing the suggested content for dissemination to the audience.