Context-Based Content Relevancy Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for locating and interacting with content across multiple devices are cumbersome, time-consuming, and prone to errors, especially on small screen devices like smartphones.
Innovation Solution
A computing device that receives a trigger to surface relevant content by obtaining cross-source contextual information, calculating relevancy weights, and surfacing items of content based on these weights, using a context-based command architecture that includes speech recognition, natural language understanding, and cross-source search components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user manually locates and shares content across multiple devices, then content can be found and shared, but the process is time-consuming and error-prone
Solution Approach 1:
The system automatically performs content location and sharing without requiring manual user intervention. The context-based command system detects user intent through speech or text input, automatically searches across multiple devices and sources, ranks results by relevancy, and prepares sharing options, allowing the system to serve itself in completing the content location and sharing task.
Solution Approach 2:
The system pre-establishes a unified context model that aggregates information from multiple devices, sources, and sensors before a search is initiated. Contextual data including user preferences, device states, and content metadata are prepared and stored in advance, enabling rapid relevancy calculation when a content search or sharing request occurs.
2Productivity
If a user browses through many files to locate relevant content, then content can be found, but the process is cumbersome and inefficient
Solution Approach 1:
The system continuously monitors user interactions with content, devices, and applications, using this feedback to refine and update the context model. This feedback loop allows the system to learn user preferences and behavior patterns, improving the accuracy of relevancy rankings over time and making content retrieval progressively more efficient and intuitive.
Solution Approach 2:
The system dynamically adjusts relevancy weights and search parameters based on contextual factors such as user preferences, device type, application state, and interaction history. By changing these parameters adaptively, the system optimizes content retrieval efficiency for different situations and user needs without requiring manual configuration.
3Productivity
If a user manually identifies recipients and composes email messages for sharing, then content can be shared, but the process is time-consuming
Solution Approach 1:
The context-based command system serves multiple functions within a single unified interface: it performs content search, identifies relevant recipients based on context, composes sharing messages, and initiates sharing across multiple platforms. This multi-functional approach consolidates several manual steps into a single automated process, improving sharing speed without increasing user-facing complexity.
4Measurement precision
If a system searches across multiple content sources, then more relevant content can be found, but the search complexity increases
Solution Approach 1:
The system divides the multi-source search process into separate modular components: a context identification system that gathers data from multiple sources, a relevancy generator that calculates scores, and a content ranking system that orders results. Each component handles a specific aspect of the search, reducing overall system complexity while maintaining high relevancy accuracy through coordinated operation of these specialized modules.
Data Source
AI summary
A computing device receives a trigger to surface relevant content. The device also obtains a variety of different types of cross-source contextual information. Items of content are identified and relevancy weights are obtained based on the contextual information. A relevancy is calculated, based on the relevancy weights, for each item of content. The items of content are surfaced.