Inference-Based Extension Activation for Document Applications
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Solution Overview
Problem
Conventional systems for activating extensions, add-ins, or plug-ins in document applications rely on basic conditional activation methods like content substring matching or pattern-based matching, which are insufficient for accurate activation based on document content and external data, leading to irrelevant or missed activation opportunities.
Innovation Solution
Inference-based extension activation that utilizes complex heuristic analysis of document content and external data sources to conditionally activate extensions by creating meta-data from content analysis, allowing for more precise activation based on inferred data elements and external criteria, such as user intents, associations, and formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic conditional activation using content substring matching or pattern-based content matching is used, then the activation system is simple to implement, but the activation accuracy is insufficient leading to irrelevant or missed activation opportunities
Solution Approach 1:
The system performs preliminary content analysis by extracting and analyzing data elements from documents before activation decisions are made. This pre-processing involves identifying entities, relationships, and contextual information that feed into the activation logic, enabling more accurate activation without requiring complex real-time analysis during the activation moment
Solution Approach 2:
The patent introduces an intermediary layer between the document content and extension activation. This intermediary consists of data element extraction and analysis mechanisms that transform raw document content into structured information that can be evaluated against activation conditions, thereby improving accuracy without directly increasing the complexity of the activation decision itself
2Ease of operation
If extensions are activated automatically based on document content, then the user experience is enhanced through relevant functionality, but the system may activate irrelevant extensions that are not needed
Solution Approach 1:
The system incorporates feedback mechanisms where activation conditions are continuously evaluated based on document content and data elements. The activation decision is not a simple binary state but involves ongoing assessment of whether the document content and external data sources indicate that the extension is truly relevant, allowing the system to correct or prevent irrelevant activations
Solution Approach 2:
The patent changes the parameters used for activation from simple content matching to multi-dimensional analysis including data element extraction, entity recognition, relationship analysis, and external data source evaluation. By changing the activation parameters from basic text patterns to complex contextual indicators, the system achieves better reliability in determining when extensions are actually relevant
3Measurement precision
If complex heuristic analysis of document content and external data sources is used, then the activation precision is improved, but the processing time and computational resources increase
Solution Approach 1:
The system segments the document analysis process into distinct components: data element extraction, entity recognition, relationship analysis, and activation condition evaluation. Each segment processes specific aspects of the document independently, allowing for optimized processing of each component and enabling parallel execution where possible, thereby reducing overall processing time while maintaining high activation precision
Solution Approach 2:
The system performs preliminary extraction and analysis of data elements from document content before the actual activation decision is made. This pre-processing creates a structured representation of the document that can be efficiently queried and evaluated against activation conditions, avoiding the need for repeated full-document analysis and reducing processing time for subsequent activation evaluations
Data Source
AI summary
Extension activation may be provided. A document may be opened within a software application associated with an extension. Upon determining that a data element associated with the document is associated with an activation condition of the extension, the extension may be activated.