Floating Interactive Box for Quick-Reference Resource Access
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Solution Overview
Problem
Users interacting with entities lack access to necessary resources, leading to inefficient interactions and unnecessary consumption of computing resources, as existing systems fail to effectively provide quick references or resources during interactions.
Innovation Solution
A graphical user interface featuring a floating interactive box is implemented using machine learning, where a back-end server system processes data to determine resource access needs, enabling access through the box and transitioning interactions to teleconference sessions or presenting quick references to improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are provided with access to necessary resources during interactions, then interaction resolution efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by proactively analyzing interaction data and identifying necessary resources before users request them. The back-end server continuously monitors interaction metrics and pre-prepares relevant resources (documentation, tools, information) for immediate presentation, eliminating the need for users to search for resources manually and thereby improving resolution efficiency without requiring complex user-side modifications
Solution Approach 2:
The patent introduces an intermediary back-end server system that acts as a mediator between users and resources. This server analyzes interaction data, determines what resources are needed, and presents them through the floating interactive box interface. This intermediary approach centralizes the complexity management on the server side while keeping the user interface simple and focused on presenting pre-processed resources
2Ease of operation
If a floating interactive box is displayed throughout interaction, then user assistance is improved, but computing resources are unnecessarily consumed
Solution Approach 1:
The floating interactive box is implemented with periodic action by dynamically appearing and disappearing based on interaction needs. The back-end server monitors interaction metrics and triggers the display of the floating box only when analysis indicates users would benefit from additional resources. When resources are successfully provided and interaction efficiency improves, the box is dismissed. This periodic display pattern reduces computing resource consumption compared to continuous display while maintaining user assistance when needed
Solution Approach 2:
The floating interactive box is made dynamic rather than static, with its display state changing based on real-time interaction analysis. The system dynamically adjusts the box's presence, content, and timing based on user behavior patterns detected by the back-end server. This dynamic approach allows the interface to adapt to actual user needs, providing assistance when beneficial while conserving computing resources when the box would not add value
3Loss of information
If machine learning algorithms process interaction data in real-time, then resource recommendations are improved, but processing time increases
Solution Approach 1:
The system applies preliminary action by pre-training machine learning models offline with extensive interaction data before deployment. During real-time interactions, the pre-trained models quickly infer resource needs from basic interaction patterns without requiring complex real-time computation. The back-end server uses these pre-trained models to rapidly analyze current interaction state and match it with previously learned patterns, providing accurate resource recommendations with minimal processing delay
Solution Approach 2:
The patent implements partial action by having the machine learning system focus on analyzing only the most critical interaction features rather than processing all possible data points in real-time. The back-end server identifies and prioritizes key interaction metrics that most strongly correlate with resource needs, processing only these essential features while ignoring less relevant data. This selective processing maintains recommendation accuracy while significantly reducing computation time and resource requirements
Data Source
AI summary
A user device for displaying a graphical user interface comprising a floating interactive box using machine learning for quick-reference to resources for a web-based portal or dedicated application on the user device, initiates display of a graphical user interface comprising a floating interactive box comprising an interaction between the user and at least one entity associate, enables, over the floating interactive box, access to the at least one resource; receives confirmation from the user that the interaction is resolved and transmits the confirmation to the back-end server system; and upon resolution of the interaction, ends display of the floating interactive box, wherein presenting access to the at least one resource resulting in resolution of the interaction and ending display of the floating interactive box quickly frees computing resources for other tasks.