Engagement Waveform Normalization for User Behavior Prediction
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Solution Overview
Problem
Existing AI models struggle with irregular, unstructured, and noisy user engagement data, leading to underfitting, overfitting, and systematic inaccuracies in prediction tasks, particularly in time-sensitive applications like churn prediction and content recommendation.
Innovation Solution
Transform user engagement data into continuous waveforms using signal processing techniques like Short-Time Fourier Transform (STFT) and envelope detection to extract temporal features such as amplitude, frequency, and variability, which are then fed into machine learning algorithms for improved behavioral modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw user engagement data is used directly as input for AI models, then data processing is simple and fast, but model accuracy deteriorates due to irregular, unstructured, and noisy data characteristics
Solution Approach 1:
The patent applies preliminary action by transforming raw user engagement data into continuous waveforms before feeding them into AI models. This pre-processing step converts irregular, unstructured data into a standardized continuous representation, improving model accuracy without requiring complex processing during training or inference.
Solution Approach 2:
The patent introduces an intermediary transformation process that converts discrete user engagement events into continuous waveforms. This intermediary representation serves as a bridge between raw data and AI model input, enabling models to process irregular data as if it were continuous and structured, thereby improving accuracy without directly modifying the model architecture.
2Reliability
If traditional data preprocessing methods are applied to user engagement data, then data structure is improved, but temporal dynamics and nuanced behavioral patterns are lost
Solution Approach 1:
The patent applies the mechanical vibration principle by representing user engagement data as continuous waveforms with temporal variations. This waveform representation preserves the rhythmic and dynamic patterns of user behavior, allowing AI models to capture temporal dynamics and nuanced behavioral patterns that would be lost in traditional discretized preprocessing.
Solution Approach 2:
The patent changes the parameter representation of user engagement data from discrete counts or categories to continuous waveform parameters. This parameter transformation enables the preservation of temporal dynamics by representing engagement intensity, frequency, and timing as continuous signal characteristics rather than discrete values.
3Adaptability or versatility
If more training data is collected to improve model performance, then model generalizability improves, but data storage and processing requirements increase
Solution Approach 1:
The patent applies dimensionality change by transforming discrete user engagement events into continuous waveforms. This transformation adds a temporal dimension to the data representation, allowing models to capture patterns across time without requiring proportionally more data points. The continuous waveform representation extracts more information from the same underlying user interactions.
Data Source
AI summary
A user interaction system may receive user engagement data including data related to a user's interaction with media content. The user interaction system may translate the user engagement data into a time based engagement waveform. The user interaction system may perform signal processing to extract one or more features from the engagement waveform to obtain a user engagement feature set. The user interaction system may provide the user engagement feature set to a machine learning algorithm for predicting user behavior.