Dynamic Menus for Multi-Prefix Mobile Search
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Solution Overview
Problem
Mobile devices face challenges in performing targeted searches due to hardware limitations, user interface constraints, and network bandwidth issues, making it difficult to retrieve information quickly and efficiently from a broad domain of information channels with minimal user interaction.
Innovation Solution
The implementation of multi-prefix and multi-tier search techniques that allow users to enter partial queries, with interactive feedback, and dynamic menus that provide context-specific actions, enabling users to select and act on search results with fewer keystrokes and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-prefix and multi-tier search techniques are implemented, then search efficiency and user interaction reduction are improved, but device complexity increases
Solution Approach 1:
The search system is divided into multiple tiers (first tier for broad domain search, second tier for specific channel search) and multiple prefix levels (single prefix, multi-prefix queries), allowing progressive refinement of search results. This segmentation enables efficient information retrieval by breaking down complex search tasks into manageable stages, reducing the number of keystrokes needed while maintaining search effectiveness.
Solution Approach 2:
The system pre-processes search queries by analyzing partial prefixes before the user completes their input. Predictive text algorithms generate suggested completions based on partial queries, and the system proactively presents relevant search results as the user types, rather than waiting for complete query input. This preliminary action reduces user interaction requirements and improves search efficiency.
2Ease of operation
If dynamic menus with context-specific actions are provided, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The menu system dynamically adapts its content and structure based on the current search context, selected channel, and user interaction history. Menus are not static but evolve in real-time to present only relevant actions for the current state, making the interface more intuitive and easier to operate. The dynamic nature allows the system to anticipate user needs and present appropriate actions without requiring complex manual navigation.
Solution Approach 2:
The system continuously monitors user interactions and provides feedback by updating menu options, search results, and predictive text suggestions based on user input patterns. This feedback mechanism allows the interface to learn from user behavior and adjust its complexity accordingly, presenting simple actions when possible and expanding complexity only when needed based on user interaction context.
3Ease of operation
If predictive text is used to generate suggestions, then user interaction requirements are reduced, but computational resources increase
Solution Approach 1:
The predictive text system generates suggestions based on partial user input rather than requiring complete query formulation. The system performs computational analysis on partial prefixes and provides multiple possible completions, allowing users to select from pre-computed options. This partial action approach reduces the computational burden compared to processing complete queries while still achieving significant reduction in user data entry requirements.
Data Source
AI summary
The present invention includes systems and methods for retrieving information via a flexible and consistent targeted search model that employs interactive multi-prefix, multi-tier and dynamic menu information retrieval techniques (including predictive text techniques to facilitate the generation of targeted ads) that provide context-specific functionality tailored to particular information channels, as well as to records within or across such channels, and other known state information. Users are presented with a consistent search interface among multiple tiers across and within a large domain of information sources, and need not learn different or special search syntax. A thin-client server-controlled architecture enables users of resource-constrained mobile communications devices to locate targeted information more quickly by entering fewer keystrokes and performing fewer query iterations and web page refreshes, which in turn reduces required network bandwidth.