Text Prediction Integration for Item Filtering
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Solution Overview
Problem
Current text prediction technologies in electronic devices only account for exact input or corrected versions, failing to effectively integrate and utilize multiple predictions for filtering, searching, or ordering items, which limits user interaction and efficiency.
Innovation Solution
A system comprising a text prediction engine and a mechanism that generates and utilizes a distribution of word predictions for sequences of terms to filter, search, or order items, allowing for the generation of query strings and output of likely items without explicit user selection, using operators like 'OR' and 'AND' to combine predictions and calculate probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text prediction technology generates multiple word predictions for user input, then prediction accuracy and user intent capture are improved, but user interaction complexity increases as users must manually select from multiple prediction candidates
Solution Approach 1:
The system automatically uses the generated word predictions to filter, search, or order items without requiring user selection. The mechanism self-services by taking the prediction distribution and directly applying it to item filtering, eliminating the need for users to manually choose from prediction candidates.
Solution Approach 2:
The system performs preliminary filtering and ordering of items based on word predictions before user interaction is needed. By pre-processing items using the prediction distribution, the system prepares results in advance, reducing subsequent user effort to find desired items.
2Ease of operation
If current applications search only on exact input or corrected version, then search simplicity is maintained, but search completeness and information quality deteriorate by missing relevant items
Solution Approach 1:
The system merges multiple word predictions into a unified distribution that combines exact input, corrections, and completions. This merged distribution is then used to filter and search items, combining the benefits of simple operation with comprehensive information retrieval.
Solution Approach 2:
The search mechanism becomes multi-functional by accepting and processing various types of predictions (exact matches, corrections, completions) through a single unified interface. This universal approach maintains simplicity while enhancing search completeness.
3Measurement precision
If the system generates and processes distribution of word predictions for multiple terms, then item filtering and searching quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the sequence of terms and processes word predictions for each term individually, then combines results. This segmentation allows manageable computation for each term while achieving comprehensive multi-term filtering quality through systematic combination.
Solution Approach 2:
The system generates a distribution of predictions for each term in the sequence, which is more computation than a single prediction would require. This excessive action of generating multiple predictions per term improves filtering quality by capturing uncertainty and variability in user intent.
Data Source
AI summary
The present invention provides systems and methods for integrating text prediction from a text prediction engine into a mechanism for searching/filtering/ordering a plurality of items. In one embodiment, the system comprises a text prediction engine configured to receive user text input relating to a sequence of two or more terms, generate a plurality of word predictions for a first term of the sequence of two or more terms and generate at least one word prediction for a second term of the sequence of two or more terms. The text prediction engine is configured to generate a distribution of candidate word predictions for the sequence of two or more terms using the plurality of word predictions for the first term and the at least one word prediction for the second term. The system further comprises a mechanism for filtering/searching/ordering a plurality of items. The mechanism is configured to filter/search/order a plurality of items using the distribution of word predictions for the sequence of two or more terms.