Widget Search via Adaptive Task Framework
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Solution Overview
Problem
The proliferation of widgets, which are small applications performing specific tasks, leads to difficulties in finding and utilizing the appropriate widget for user queries due to their large numbers, resulting in inefficient search and functionality execution.
Innovation Solution
A combination of the Adaptive Task Framework and Adaptive Semantic Reasoning Engine is employed to create a scalable mechanism for searching and slot filling, allowing users to find and automatically fill in widget functionality using task information and user queries, enabling efficient selection and invocation of widgets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users download more widgets to cover more tasks, then the coverage of available functionality increases, but the difficulty of finding and organizing appropriate widgets increases
Solution Approach 1:
The patent introduces an intermediary system consisting of a calling application, task framework, and semantic reasoning engine that mediates between the user and the large number of available widgets. This intermediary automatically analyzes user queries, matches them with appropriate widgets based on task capabilities and slot requirements, and handles the organization complexity, thereby resolving the contradiction between having many widgets and being able to find them easily
Solution Approach 2:
The system enables self-service by allowing widgets to automatically describe their own capabilities through task frameworks and slot definitions. The semantic reasoning engine autonomously processes user queries and performs widget selection without requiring manual organization or user effort to navigate through numerous widgets, thus maintaining high adaptability while reducing search difficulty
2Reliability
If a full application is launched to accomplish a simple task, then the functionality and reliability are sufficient, but the startup time and resource consumption increase
Solution Approach 1:
The patent segments the functionality of full applications into smaller, purpose-built widgets that perform specific tasks. Each widget is a standalone unit with defined task capabilities and slot requirements, allowing users to launch only the minimal necessary component for a given task rather than a comprehensive application, thus reducing startup time while maintaining sufficient functionality through the task framework matching mechanism
Solution Approach 2:
The system applies partial action by launching only the specific widget needed for the current task rather than a full application with excessive capabilities. The task framework and semantic reasoning engine ensure that the partial functionality of individual widgets is sufficient for their intended purposes, eliminating the need to load unnecessary features and reducing startup time
3Stability of the object's composition
If widgets are stored in folders and databases for organization, then the orderliness improves, but the speed of locating and starting widgets decreases
Solution Approach 1:
The patent replaces the mechanical system of manual folder-based organization with an intelligent information processing system. The task framework and semantic reasoning engine automatically analyze user queries and match them with widgets based on task capabilities and slot requirements, substituting the need for users to manually navigate organizational structures with automated semantic matching, thus maintaining orderliness while dramatically improving location speed
4Power
If more computer resources are allocated to support powerful applications, then the processing capability and functionality improve, but the processing time and startup time increase
Solution Approach 1:
The patent segments powerful application functionality into smaller widget units with specific task capabilities. Each widget requires fewer computer resources to load and execute compared to full applications, while the task framework and semantic reasoning engine coordinate these segmented units to accomplish complex tasks, thereby maintaining high processing capability with reduced resource allocation and faster startup times
Data Source
AI summary
A task framework and a semantic reasoning engine are combined to provide a scalable mechanism for dealing with extremely large numbers of widgets, allowing users to both find a widget and automatically fill-in whatever functionality is available on the widget. Calling applications are employed to obtain task information from each widget. The calling application also receives user queries that can be resolved by a widget. A task reasoning process based on an adaptive semantic reasoning engine utilizes the task information to select a widget best suited to respond to a user's query. The task reasoning process can also be employed to determine “best-guess” slot filling of the selected widget. The calling application can then invoke the selected widget and, if available, fill appropriate slots with information to facilitate user interaction with the selected widget. Instances can be client- and/or server-side based.