Context-Aware Action Selection via Semantic Entity Recognition
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Solution Overview
Problem
Users of computing devices face the challenge of manually selecting computer applications to perform specific actions, which can be inefficient and time-consuming, especially when multiple applications are involved in contextually relevant tasks.
Innovation Solution
A computing system equipped with a machine-learned model that processes context data to identify semantic entities and provides indicators to users about available actions from multiple computer applications, streamlining the selection process through intelligent prompts and suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select computer applications to perform actions, then users have full control over application selection, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system performs preliminary analysis of context data (messages, documents, media) to identify semantic entities and pre-determines relevant applications before user action is required. This allows the system to present ready-made application suggestions rather than requiring users to manually search and select applications, thereby improving efficiency without sacrificing user control
Solution Approach 2:
The system introduces an intermediary layer between the user and computer applications that automatically analyzes context, identifies relevant semantic entities, and matches them with appropriate applications. This intermediary processing layer handles the time-consuming manual selection task automatically while still allowing users to review and select from presented options
2Ease of operation
If the system provides proactive application suggestions, then user input requirements are reduced, but system complexity increases
Solution Approach 1:
The system segments the complex task of application recommendation into distinct modular components: context data acquisition module, semantic entity identification module (using machine-learned models), application matching module, and suggestion presentation module. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable despite its sophistication
Solution Approach 2:
The system employs machine-learned models as intermediary components that specialize in specific subtasks (e.g., natural language processing, entity recognition, application matching). These intermediary intelligent layers handle the complex analysis work, allowing the overall system to provide proactive suggestions without requiring the entire system architecture to be overwhelmingly complex
3Measurement precision
If the system processes context data through machine-learned models, then relevant actions are accurately identified, but computational resources increase
Solution Approach 1:
The system applies partial processing by using machine-learned models selectively only for the specific subtask of semantic entity identification from context data, rather than applying comprehensive complex analysis to all data. This targeted approach achieves high accuracy in identifying relevant entities while avoiding the excessive computational overhead of more thorough analysis methods
Solution Approach 2:
The system extracts and focuses computational resources on the specific critical function of identifying semantic entities from context data using machine-learned models, separating this intensive task from other system functions. By extracting this specific high-value processing need and dedicating appropriate resources only to it, the system achieves high accuracy without unnecessarily consuming resources on other aspects
Data Source
AI summary
A computing system can be configured to input model input that includes context data into a machine-learned model and receive model output that describes one or more semantic entities referenced by the context data. The computing system can be configured to provide data descriptive of the semantic entity or entities to the computer application(s) and receive application output(s) respectively from the computing application(s) in response to providing the data descriptive of semantic entity or entities to the computer application(s). The application output(s) received from each computer application can describe available action(s) of the corresponding computer application with respect to the semantic entity or entities. The computing system can be configured to provide at least one indicator to a user that describes the available action(s) of the corresponding computer applications with respect to the semantic entity or entities.