Adaptive Recommendation System Using Dynamic Persona Classification

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Solution Overview

Problem

Existing systems fail to provide comprehensive, reliable, and adaptive personal interventions and recommendations on a large scale, particularly in customer support, crisis hotlines, therapy, medicine, and education, due to the disjoint nature of current natural language processing and machine learning technologies.

Innovation Solution

A dynamic assessment and classification system using natural language processing and machine learning to classify personas from communication interaction records, enabling personalized recommendations by retrieving, extracting, classifying, and scoring passages to match individuals with effective interventions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If personalized recommendations are provided using prior systems, then individual service quality improves, but scalability and reliability deteriorate due to increased human effort

Engineering Contradiction:

Improveservice reliabilityVSAvoidservice scalability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual human analysis and recommendation processes with automated natural language processing algorithms and machine learning models. The system processes communication interaction records through computational algorithms that extract features, classify personas, and generate recommendations automatically, eliminating the need for human analysts to manually review each interaction while maintaining consistent quality across large volumes of data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the algorithmic model to autonomously perform the complete workflow from data input to recommendation generation without human intervention. The machine learning model automatically learns from training data, adapts to new patterns, and generates personalized recommendations independently, making the system self-sufficient while scaling to handle large volumes of interactions

Inventive Principle:

Principle #25Self-service

2Reliability

If comprehensive personal interventions are provided, then service effectiveness improves, but system complexity increases making large-scale deployment difficult

Engineering Contradiction:

Improveservice effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the complex recommendation system into distinct modular components: communication record ingestion module, natural language processing module, feature extraction module, persona classification module, and recommendation generation module. Each module performs a specific function and can be independently developed, tested, and maintained, reducing overall system complexity while enabling comprehensive personal interventions through the coordinated operation of these specialized components

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If dynamic adaptation to individual needs is implemented, then recommendation quality improves, but processing time and computational resources increase

Engineering Contradiction:

Improvepersonalization adaptabilityVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by pre-processing communication interaction records during off-peak times to extract features and create standardized representations. The natural language processing and feature extraction are performed in advance, so that when recommendations are needed, the system only needs to match the pre-processed data against the trained model, significantly reducing real-time processing delays while maintaining dynamic adaptability

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements parameter changes by dynamically adjusting model confidence thresholds, processing depth levels, and recommendation granularity based on the specific context and time constraints. The system can switch between rapid-response mode (lower processing depth) and comprehensive-analysis mode (higher processing depth), allowing it to adapt processing intensity to match the urgency and complexity of each recommendation request

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11449681B2Data processing system and method for dynamic assessment, classification, and delivery of adaptive personalized recommendations

Publication Date: 2022.09.20 REUP EDUCATION INC

AI summary

A data processing system and method for delivering a personalized recommendation to an individual, using computerized, dynamic assessment and classification of communication interaction records, is described. The system and method comprises a machine learning application to (1) classify a persona based on communication processing of communication interaction records captured over time, and (2) generate an adaptive personalized recommendation for use within a coaching, advisory or other personalized service. The system and method allows for improved assessments and identification of factors affecting achievement of a goal or outcome, thereby allowing for adaptive, personalized recommendations, improving delivery of personalized support services, and improved success rates for achieving a defined outcome. The system and method may also be used to create a collection of persona analytics information that may be used to visualize and evaluate trends associated with an individual or within a given population.