Dynamic Web Content Adjustment via NLP Inputs
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Solution Overview
Problem
Users often struggle to find the information they need on websites, leading to frustration and the need to interact with intelligent virtual assistants (IVAs), which may not provide the desired results.
Innovation Solution
The system dynamically adjusts website content based on natural language processing (NLP) inputs from users, trending information, and user data, providing relevant links and content without requiring user interaction with IVAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users interact with an intelligent virtual assistant (IVA) to find information on a webpage, then they may obtain the desired information, but the user experience becomes unsatisfactory due to the need for additional interaction and potential IVA inability to provide the information
Solution Approach 1:
The system performs preliminary analysis of user needs and proactively presents relevant information and content on the webpage before users need to search for it or interact with an IVA. This is achieved by monitoring user behavior, analyzing data sources, and dynamically adjusting content to anticipate user information needs.
Solution Approach 2:
The webpage system automatically serves information to users without requiring interaction with an IVA. The system monitors user behavior, analyzes data sources, and dynamically adjusts content to meet user needs autonomously, making the IVA interaction unnecessary.
2Ease of operation
If the website provides static content, then the website structure is simple and easy to maintain, but the content does not adapt to user needs and users cannot easily find what they want
Solution Approach 1:
The website transitions from static to dynamic content delivery by continuously monitoring user behavior and data sources, then adjusting content in real-time. The system dynamically generates and presents links, information, and content based on analyzed user needs and trending data, making the website adaptive rather than fixed.
Solution Approach 2:
The website system performs multiple functions: it monitors user behavior, analyzes multiple data sources, determines user needs, generates content, and dynamically adjusts webpage presentation. This multi-functional approach consolidates what would otherwise require separate systems into a unified dynamic content delivery platform.
3Productivity
If the system proactively monitors and adjusts website content based on user data, then user experience is enhanced by providing relevant content without interaction, but the system complexity and data processing requirements increase
Solution Approach 1:
The system continuously monitors user behavior on the webpage and uses this feedback to dynamically adjust content. By analyzing user interactions, time spent on pages, and behavior patterns, the system refines its content delivery to better match user needs, creating a closed-loop system that improves over time.
Solution Approach 2:
The system introduces an intermediary layer between the user and the webpage content - an intelligent system that analyzes data sources and user behavior, then mediates content delivery by dynamically generating and presenting relevant information. This intermediary handles the complexity of data processing while presenting a simplified experience to users.
Data Source
AI summary
Systems and methods are provided for dynamically adjusting a website of an entity using information that has been received, stored, gathered, and/or otherwise obtained about what people want to find on the entity's website. A website may be dynamically adjusted using trending information in response to determining that the usage of the monitored data source is greater than the baseline usage distribution or in response to determining that the usage of the monitored data source is not greater than the baseline usage distribution receiving NLP inputs of the user from the IVA and adjusting dynamic web content displayed on the website based on the NLP inputs.