Semantic Activity Awareness for Context-Aware Task Planning
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Solution Overview
Problem
Conventional context-aware recommendation mechanisms fail to provide procedural guidance by not understanding task context and actions within a larger task context, limiting the adoption of semantic modeling due to high knowledge investment and requiring specialized skills, making it impractical for enterprises with accumulated history and context.
Innovation Solution
A method that stores task patterns, defines domain-specific tasks based on initial input, and uses interaction information to select and represent action items with metadata, allowing for the creation of domain-specific tasks in diagrammatic and knowledge representation languages, incorporating a semantic engine to provide meaningful recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If semantic modeling technologies are adopted to provide procedural guidance based on task context, then the quality of recommendations improves, but the investment in knowledge feeding and specialized skills increases
Solution Approach 1:
The patent segments the semantic modeling task into two distinct phases: design-time modeling where domain experts define task patterns using high-level abstractions, and runtime execution where the system automatically instantiates these patterns. This segmentation allows complex semantic knowledge to be captured once during design time without requiring continuous specialized input during operation.
Solution Approach 2:
The patent applies preliminary action by having domain experts pre-define task patterns and their associated action items during the design phase. These pre-modeled patterns are then stored and automatically instantiated during runtime, eliminating the need for specialized skills during operation and reducing continuous knowledge feeding requirements.
2Measurement precision
If domain-specific task models are created using specialized semantic representation methods, then the accuracy of task understanding improves, but the ease of operation decreases due to required specialized skills
Solution Approach 1:
The patent uses copying by providing domain experts with templates and examples of task patterns during the design phase. Experts can instantiate new domain-specific tasks by copying and adapting existing patterns rather than creating models from scratch using complex semantic representation methods, significantly improving ease of operation while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between domain experts and the semantic modeling system. This intermediary provides high-level abstractions and automated instantiation mechanisms, allowing experts to define tasks using simple domain-specific concepts without needing to directly manipulate complex semantic representation syntax.
3Ease of operation
If conventional recommendation mechanisms are used to recommend content based on current location and profile, then the ease of operation is maintained, but the understanding of task context is insufficient
Solution Approach 1:
The patent applies nested doll by organizing the recommendation system in multiple layers: the outer layer handles simple content recommendations based on current context, while the inner layer automatically instantiates and executes complex task patterns when detected. This nested structure allows the system to maintain simplicity for common cases while automatically providing sophisticated task-aware recommendations when needed.
Data Source
AI summary
A domain-specific task may be defined from a library of domain-independent task patterns. A task pattern may be selected based on inferences made from a user's initial input, or just based on receiving sensor information through events or contextual information, or a combination of user input and sensor information. One or more elements of a selected task pattern may be defined in the context of a given domain to define the domain-specific task.