AI System for Predictive User Behavior Segmentation

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Solution Overview

Problem

Current technologies lack the ability to effectively predict and tailor communications based on individual user behavior patterns, leading to inefficient decision-making and personalized experiences.

Innovation Solution

A system utilizing artificial intelligence and machine learning to process labeled interaction data, segment users, and predict future behavior, triggering concurrent communications and updates to user dashboards with tailored content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If machine learning models process labeled interaction data to predict user behavior, then personalization accuracy is improved, but system processing speed deteriorates

Engineering Contradiction:

Improvebehavior prediction accuracyVSAvoidsystem processing speed

Core Design Contradiction:

Measurement precisionVSSpeed

Solution Approach 1:

The system performs labeling of interaction data in advance before the actual prediction phase. By pre-processing and labeling the data beforehand, the system prepares structured information that can be quickly consumed by prediction models during runtime, thus improving real-time processing speed while maintaining prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent divides the processing workflow into distinct segments: data collection, labeling phase, model training phase, and prediction phase. This segmentation allows each component to be optimized independently - labeling can be done offline while prediction runs efficiently in real-time, resolving the speed-accuracy tradeoff

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If serially applied machine learning models process data to segment users and predict behavior, then personalization effectiveness is improved, but system complexity increases

Engineering Contradiction:

Improvepersonalization effectivenessVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments users into distinct groups based on their behavior patterns and characteristics. By dividing the user base into segments, the system can apply different prediction models and strategies to different segments, improving personalization effectiveness while managing complexity through modular segment-specific processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces labeled interaction data as an intermediary layer between raw user interactions and prediction models. This labeled data serves as a mediator that structures complex user behavior information into meaningful patterns, making it easier for subsequent models to process and reducing overall system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230342588A1Ai system to initiate a concurrent reaction according to predicted user behavior

Publication Date: 2023.10.26 TRUIST BANK

AI summary

A system for employing artificial intelligence to initiate a concurrent reaction according to predicted behavior is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed, causes the at least one processor to receive labeled interaction data for a group of users, receive user information for the group of users, correlate the labeled interaction data with the user information, apply a first algorithm to the correlated data to segment the users, based on the user’s segment apply a second algorithm to make a prediction about the user’s future behavior, and based on the predicted behavior trigger a concurrent communication to the user. Additionally, the system may update a user dashboard.