Dynamic Web Application Event Prediction Server
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Solution Overview
Problem
Conventional methods for navigating web applications are inefficient and inaccurate, requiring users to manually search through directories or rely on chatbots that rely on keyword searches, limiting their ability to predict users' intentions in real-time based on historical behavior.
Innovation Solution
A server-based system that continuously monitors user interactions, uses artificial intelligence to predict future behavior by analyzing historical data and current interactions, and dynamically displays interactive graphical components to guide users to desired web pages, optimizing the user experience by providing accurate and fine-grained predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional website directories and manual search methods are used, then users can find desired content through structured navigation, but navigation time and user effort increase significantly
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and pre-calculates predicted next actions before users actually navigate. By continuously monitoring user interactions and training prediction models in advance, the system prepares personalized navigation recommendations that are immediately available when needed, eliminating the need for users to manually search through directories.
Solution Approach 2:
The system implements a feedback loop where user interactions are continuously monitored and fed back into the prediction model. The model learns from actual user behavior patterns and adjusts predictions accordingly, creating a dynamic system that improves accuracy over time. This feedback mechanism allows the system to adapt to individual user preferences and behaviors, providing increasingly accurate navigation predictions.
2Ease of operation
If chatbots with keyword searching are deployed, then users can inquire about desired content through natural language, but search accuracy decreases when keywords are too broad or narrow
Solution Approach 1:
The system introduces an intelligent prediction model as an intermediary between user input and content retrieval. Instead of directly searching based on user keywords, the prediction model acts as a mediator that interprets user intent, contextualizes queries, and generates accurate search terms or direct navigation paths. This intermediary layer transforms imprecise user input into precise content location actions.
Solution Approach 2:
The system dynamically changes search parameters based on predicted user intent rather than relying on fixed keyword matching. By analyzing user behavior patterns and contextual information, the system adjusts search criteria, weightings, and query formulations in real-time, transforming static keyword search into a dynamic, intent-aware retrieval process that maintains high accuracy regardless of user input quality.
3Adaptability or versatility
If prediction models based on historical behavior are used, then recommendation services can be provided, but real-time prediction capability and fine-grained accuracy are insufficient
Solution Approach 1:
The system implements continuous monitoring and continuous learning rather than periodic or batch processing. User interactions are tracked in real-time, and the prediction model continuously updates its understanding of user behavior patterns. This continuous action ensures that predictions are always based on the most current data, enabling fine-grained real-time accuracy that captures immediate user intent changes.
Solution Approach 2:
The prediction system transitions from static historical analysis to dynamic real-time prediction. The model adapts its parameters and predictions based on current user state, contextual information, and evolving behavior patterns. This dynamic approach allows the system to capture transient user intentions and provide accurate predictions for immediate next actions rather than general long-term recommendations.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which dynamically predicts future events for web users. The analytic server generates prediction models based on historical click-through analytics data received from the web server. The analytic server captures the current event (e.g., the current operation of the web user) on the web page, and determines the next event by predicting the web user behavior using the prediction models on an event-by-event basis. The analytic server also queries the web user data from a database to better understand the web user's intention, and improve the prediction accuracy. The analytic server modifies the HTML code to display the web page to include a graphical user interface comprising the predicted event. Based on the web users' reactions to the predicted event, the analytic server updates the prediction models.