Indexing Rich Internet Content Using Contextual Information
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Solution Overview
Problem
Current indexing methods for rich Internet content, such as Adobe FLASH Search, are inefficient due to ad-hoc selection of interactive entities and state comparison, leading to redundant processes and significant computing power and time spent restarting and simulating events.
Innovation Solution
A method and apparatus that utilize contextual information associated with transitions, states, and entities to guide the indexing process, preventing redundant traversals by comparing contextual information and prioritizing entities based on activation counts and hash codes to efficiently traverse and index rich Internet content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current indexing methods traverse all states and simulate events on all interactive entities, then comprehensive indexing coverage is achieved, but significant computing power and time are spent on redundant processes
Solution Approach 1:
The patent implements feedback mechanisms by continuously comparing the current state with previously visited states and using contextual information to guide traversal decisions. The system learns from past traversal experiences and adjusts its path selection to avoid redundant exploration, thereby maintaining comprehensive coverage while improving efficiency.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing contextual information about states and transitions before full traversal. This includes pre-identifying potential loops and pre-comparing states using contextual features, which allows the indexing process to make informed decisions faster and avoid redundant computations during actual traversal.
2Reliability
If the indexing application restarts rich Internet content frequently to explore different transition paths, then all possible states are visited, but the indexing process becomes time-consuming and computationally expensive
Solution Approach 1:
The system performs preliminary analysis of the state space using contextual information to identify promising traversal paths before actual exploration. By pre-computing contextual features and potential transitions, the system reduces the need for frequent restarts while ensuring comprehensive state coverage.
Solution Approach 2:
The system uses feedback from contextual information and previous traversal results to intelligently select which paths to explore next. This feedback mechanism allows the system to avoid restarting content for already-explored states while ensuring all relevant states are visited, thereby reducing indexing time without sacrificing coverage.
3Reliability
If ad-hoc selection of interactive entities is used for event simulation, then all entities are eventually covered, but redundant event triggering occurs and computing resources are wasted
Solution Approach 1:
The patent implements feedback-based entity selection by tracking which entities have been activated from which states and using contextual information to determine which entities are worth exploring next. This prevents redundant event triggering on entities that have already been充分 explored, while ensuring all unique entities are eventually covered.
Solution Approach 2:
The system changes the parameter of entity selection from random ad-hoc selection to context-driven intelligent selection. By using contextual information about the current state, previously visited states, and entity activation history, the system dynamically adjusts which entities to prioritize, reducing redundant computations while maintaining comprehensive coverage.
4Ease of operation
If state comparison is performed without contextual information, then simple traversal is possible, but looping occurs and indexing accuracy decreases
Solution Approach 1:
The patent introduces contextual information as an intermediary between simple state comparison and accurate loop detection. Instead of directly comparing entire states (which is complex), the system uses contextual features as a mediator to efficiently identify equivalent states and detect loops, maintaining both simplicity and accuracy.
Solution Approach 2:
The system changes the comparison parameter from full state comparison to contextual feature comparison. By focusing on relevant contextual features rather than entire state representations, the system achieves both computational simplicity and accurate loop detection, preventing infinite traversal while maintaining indexing reliability.
Data Source
AI summary
A method and apparatus for facilitating indexing of rich Internet content by accessing a portion of rich Internet content containing a plurality of states and a plurality of transitions that connect various ones of the plurality of states through activation of at least one entity. The method and apparatus accesses contextual information coupled to at least one of a transition, a state or an entity, and examines at least one of the plurality of states using the at least one of the plurality of transitions and the contextual information.