Deep Generative Models for Real-Time User Interaction Insights
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Solution Overview
Problem
Conventional data analysis methods struggle to provide comprehensive and nuanced insights into user behavior from complex and vast datasets, lacking generalizability and scalability, and often require manual interpretation of basic data outputs.
Innovation Solution
A unified pipeline using a foundational generative model with a core sequence-to-sequence model and a high-level model to process natural language queries, enabling real-time analysis and prediction of user interactions, filling in missing steps, and generating coherent responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used to analyze user behavior data, then basic data outputs can be obtained, but comprehensive and nuanced insights cannot be provided and manual interpretation is required
Solution Approach 1:
The generative model automatically generates comprehensive insights and interpretations from raw user behavior data without requiring manual analysis. The system performs self-service by transforming basic data outputs into nuanced behavioral insights, prediction outcomes, and actionable recommendations autonomously, eliminating the need for manual interpretation while maintaining high accuracy
Solution Approach 2:
The patent replaces manual interpretation mechanisms with an automated generative AI system. The mechanical process of human analysts manually examining and interpreting data is substituted with a computational generative model that automatically produces detailed insights, predictions, and interpretations, thereby improving both accuracy and operational ease
2Adaptability or versatility
If conventional data analysis methods are used, then analysis can be performed on limited datasets, but generalizability and scalability are lacking
Solution Approach 1:
The generative model is designed with universal applicability to handle diverse and vast datasets across different domains and contexts. It performs multiple functions including pattern recognition, prediction generation, insight synthesis, and adaptive learning, enabling it to generalize effectively from training data to new, unseen data while scaling to accommodate large dataset volumes
Solution Approach 2:
The system dynamically adapts to varying dataset sizes and complexities. The generative model adjusts its processing capabilities and computational resources based on the quantity and nature of input data, enabling scalable performance from smaller to larger datasets while maintaining generalizability through adaptive learning mechanisms
3Measurement precision
If complex queries about user behavior are analyzed, then detailed insights can be obtained, but real-time analysis capability is compromised
Solution Approach 1:
The generative model performs preliminary learning and pattern recognition during the training phase on historical user behavior data. This preliminary action enables the model to quickly generate accurate insights and predictions in real-time when queried, as the complex analytical work has already been prepared in advance through training on diverse datasets
Solution Approach 2:
The patent replaces slow, sequential manual or conventional analytical processing with parallel computational processing using generative AI. The system substitutes traditional step-by-step analysis mechanisms with neural network-based inference that can rapidly process complex queries and generate detailed insights in real-time by leveraging pre-trained patterns and relationships
Data Source
AI summary
Systems and methods for generating user insights include obtaining a query about a user interaction with a software application. The query can be in the form of a natural language question. Embodiments then select a task from a plurality of event prediction tasks based on the query. Next, embodiments generate, using a machine learning model, an event prediction based on the query and the task, where the machine learning model is trained to predict an event based on a sequence of user interactions with the software application. Embodiments then generate a natural language response to the query based on the task and the event prediction.