Context Dictionary for Input Correction
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Solution Overview
Problem
Current user input correction mechanisms on computing devices do not consider the user's context, leading to irrelevant or inaccurate correction suggestions.
Innovation Solution
A computing device generates context dictionaries based on the user's current or recently viewed content across devices, synchronizing this context to provide relevant correction suggestions, and disambiguate user input by comparing it against both standard and context dictionaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard input correction mechanisms are used without context, then the correction process is simple and fast, but the correction suggestions are irrelevant or inaccurate
Solution Approach 1:
The system performs preliminary actions by generating context dictionaries in advance based on user activity data, content being viewed, and device usage patterns. These context dictionaries are created before correction is needed and stored for rapid retrieval, allowing the system to have correction suggestions ready without adding complexity to the real-time correction process
Solution Approach 2:
The patent introduces context dictionaries as an intermediary layer between the user input and the correction suggestions. These dictionaries act as a mediator that contains pre-analyzed contextual information about the user's current activities, devices, and content, allowing the correction system to query relevant context without directly processing complex user data in real-time
2Adaptability or versatility
If context dictionaries are generated and synchronized across devices, then correction suggestions become contextually relevant, but data synchronization complexity increases
Solution Approach 1:
The context dictionaries are designed to be universal across multiple devices and applications. The same dictionary structure and format can be used on different device types (mobile, desktop, tablet) and within different applications, allowing the system to maintain contextual relevance without creating device-specific synchronization logic for each platform
Solution Approach 2:
The system manages synchronization complexity by changing parameters selectively - not all context data is synchronized across all devices. Instead, the system adjusts which contextual parameters are synced based on device type, user preferences, and network conditions, allowing contextual relevance to be maintained while reducing unnecessary synchronization overhead
3Loss of information
If context data from multiple devices is collected, then the contextual information becomes more comprehensive, but privacy concerns increase
Solution Approach 1:
The system extracts only the specific contextual information needed for correction suggestions from the broader user data ecosystem. Rather than collecting or storing comprehensive user activity data, the system extracts relevant contextual patterns (such as current content being viewed or active applications) and uses only those extracted elements to generate context dictionaries, leaving the rest of the user data private and uncollected
Solution Approach 2:
The context dictionaries are designed as temporary, disposable data structures that are generated, used for correction, and then discarded or updated. They do not persist as permanent storage of user information, and are continuously regenerated based on current context, ensuring that comprehensive contextual information is available when needed but does not accumulate as a privacy risk over time
Data Source
AI summary
In some implementations, a computing device can generate user input correction suggestions based on the user's context. For example, the user's context can include content that the user has open or has recently opened on the computing device or another computing device. For example, when the user opens an item of content, the computing device can generate a context dictionary that includes words, phrases, etc., that describe the opened content. When the user provides input (e.g., text, speech, etc.) the computing device can use the context dictionary to generate input correction suggestions. The computing device can synchronize the context dictionary with other computing devices that the user may be using so that the user's context on one device can be used by another device to generate input correction suggestions.