Neural Network Session Analysis for Dynamic Web Content

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Solution Overview

Problem

Existing systems struggle to effectively predict user engagement and personalize web page content in real-time based on user session activities, leading to inefficient use of resources and potential loss of user interest.

Innovation Solution

A special-purpose machine learning system that analyzes user session activities using a neural network to predict the next user activity, allowing for dynamic adjustment of web page content to enhance user engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the system provides personalized content recommendations based on user session analysis, then user engagement is improved, but computational resources and processing time are increased

Engineering Contradiction:

Improveuser engagementVSAvoidcomputational resource usage

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis of user session activities and pre-computes engagement predictions before users actually interact with content. By analyzing session flows, page views, and interaction patterns in advance, the system prepares personalized recommendations that can be delivered quickly, reducing real-time computational burden while maintaining high user engagement

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies different levels of analysis and personalization to different users based on their engagement patterns, session duration, and interaction history. Rather than uniformly analyzing all user sessions with equal computational resources, the system focuses intensive analysis on users showing high engagement potential while using lighter models for others, optimizing the balance between engagement improvement and resource consumption

Inventive Principle:

Principle #3Local quality

2Adaptability or versatility

If the system analyzes user session activities in real-time to predict next user activity, then content personalization is improved, but system complexity is increased

Engineering Contradiction:

Improvecontent personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the user session analysis into distinct components: session flow analysis, page view patterns, interaction behavior, and engagement prediction. Each segment is processed by specialized modules that can be independently optimized and maintained. This modular segmentation reduces overall system complexity while enabling sophisticated real-time personalization through coordinated operation of simpler individual components

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary processing layers between raw user session data and final personalization decisions. These intermediaries include session flow graphs, user profile representations, and engagement score calculations that simplify complex raw data into manageable features. This intermediary representation layer reduces the complexity of the final prediction and personalization algorithms while maintaining high adaptability

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If the system dynamically adjusts web page content based on predicted user behavior, then user interest retention is improved, but response time is increased

Engineering Contradiction:

Improveuser interest retentionVSAvoidcontent adjustment response time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary prediction of user next activities and pre-adjusts web page content before users actually request it. By analyzing session patterns and predicting likely next steps, the system proactively prepares personalized content in the background, so when users interact with the system, the personalized content is already ready, minimizing perceived response time while maintaining high user interest retention

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system merges the prediction and content adjustment processes into a unified workflow that operates in the background during normal system operations. Rather than sequentially executing prediction then adjustment as separate time-consuming steps, the system combines these operations into an integrated process that leverages shared computations and data structures, reducing overall response time while maintaining reliable user interest retention

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12210970B2System and method for improving user engagement based on user session analysis

Publication Date: 2025.01.28 EBAY INC

AI summary

A server accesses a plurality of users' sessions with the web server. Each user session indicating a page flow of a corresponding user session for a plurality of web pages provided by the web server. The server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicating one of continuing the current user session by visiting another web page provided by the web server and ending the current user session. The server dynamically adjusts a content of a web page based on the predicted next user activity.