Configurable Prediction Engine for Text Input Efficiency
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Solution Overview
Problem
Existing text prediction methods for handheld devices are limited by small keypads and specific prediction models, making input operations inefficient and less accurate compared to larger keyboards.
Innovation Solution
A configurable prediction engine system that uses multiple data sources and candidate providers to rank and return predicted candidates, allowing for customizable ranking and dynamic configuration to improve input efficiency across various input methods, including text, speech, and handwriting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If text prediction methods are limited to specific prediction models and resources, then the system complexity is reduced, but the prediction accuracy and adaptability deteriorate
Solution Approach 1:
The patent implements a universal prediction engine that can work with multiple types of data sources and candidate providers simultaneously. The system is designed to accept various input types (text, speech, handwriting) and integrate results from diverse prediction models, making the system multi-functional while maintaining manageable complexity through standardized interfaces and weighted result aggregation.
Solution Approach 2:
The prediction system is segmented into distinct modular components: data sources, candidate providers, and a prediction engine. Each component operates independently with well-defined interfaces, allowing the system to scale by adding or removing specific providers without increasing overall system complexity. This modular architecture enables high prediction accuracy through multiple specialized providers while keeping the integration layer simple.
2Adaptability or versatility
If multiple data sources and candidate providers are integrated, then prediction accuracy and adaptability improve, but the device complexity increases
Solution Approach 1:
The prediction engine implements universal interfaces that work with multiple data source types and candidate providers simultaneously. The system can dynamically select and integrate various prediction models (text-based, speech-based, handwriting-based) through standardized APIs, achieving high adaptability without proportionally increasing complexity through consistent interface design.
Solution Approach 2:
The patent introduces an intermediary prediction engine that mediates between multiple data sources and candidate providers. This mediator layer standardizes communications, manages result aggregation, and handles provider selection, thereby insulating the complexity of multiple providers from the user interface and maintaining system manageability while enabling diverse adaptability.
3Ease of operation
If a customizable ranking component is used to rank candidates based on query type, then the ease of operation improves, but the device complexity increases
Solution Approach 1:
The ranking component is designed to be dynamic and adaptable, automatically adjusting its ranking criteria based on the query type and user preferences. The system can switch between different ranking strategies (e.g., probability-based, frequency-based, custom-weighted) without requiring manual reconfiguration, making it easy to operate while managing complexity through automated adaptation.
Solution Approach 2:
The customizable ranking component implements self-service mechanisms where the system automatically learns and adapts to user preferences over time. The ranking algorithm adjusts its parameters based on user interactions and feedback without requiring manual intervention, thereby improving ease of operation while containing complexity through autonomous self-configuration.
Data Source
AI summary
Embodiments are configured to provide one or more candidates based in part on an input. In an embodiment, a system includes a prediction engine which can be configured to provide one or more ranked candidates using one or more configurable data sources and/or candidate providers. Each data source can be configured to include a candidate provider to predict and return predicted candidates. The prediction engine can use a predicted candidate to rank and return a ranked candidate to a user interface, process, or other application. In one embodiment, a computing device can include and use a prediction engine which can use a customized ranking component to rank and return ranked candidates based in part on a query type. The customized ranking component can use predicted candidates provided by one or more data sources and/or candidate providers when making a ranking determination.