Word Prediction Filtering via Semantic Context
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Solution Overview
Problem
Conventional word prediction systems are inadequate in preventing the surface of objectionable or inappropriate predictions and fail to handle changing language sensitivities and diverse training data, particularly in multi-language and cultural contexts.
Innovation Solution
A system that modifies word predictions based on context information, determining whether the predicted word sequence corresponds to a predetermined semantic reference, and providing an output accordingly, to ensure appropriate and relevant suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional word prediction systems use diverse training data from social media and pop culture websites, then the system can provide more modern and unconventional language usage predictions, but the system may also surface objectionable or inappropriate predictions that conflict with evolving societal standards
Solution Approach 1:
The patent introduces a semantic reference system as an intermediary layer between the word prediction model and the user interface. This mediator compares predicted words against predetermined semantic references (such as profanity lists, sensitive topic databases, or culturally inappropriate term collections) and filters or modifies predictions that conflict with societal standards, thereby resolving the contradiction between providing modern language coverage and avoiding inappropriate content
Solution Approach 2:
The system performs preliminary semantic filtering of word predictions before presenting them to users. By proactively comparing predicted words against predetermined semantic references and blocking or modifying inappropriate predictions in advance, the system prevents harmful content from reaching the user interface, thus applying preliminary anti-action to counteract the potential harm from diverse training data
2Adaptability or versatility
If word prediction systems are trained on data from multiple languages and cultures, then the system can serve a broader user base, but the system may fail to account for varying cultural sensitivities and language nuances across different regions
Solution Approach 1:
The patent implements location-aware semantic filtering where the system determines the user's geographic location or language preference and applies culture-specific semantic references appropriate to that region. For example, predictions for users in one country are filtered against locally-appropriate semantic references, while predictions for users in another country use different cultural standards, thereby providing locally-adapted content filtering that respects cultural nuances
Solution Approach 2:
The system dynamically adjusts its semantic reference selection based on detected user context such as language, location, or cultural preferences. Rather than using a static filtering mechanism, the system adapts its filtering criteria in real-time to match the user's cultural context, enabling precise cultural sensitivity that changes with user characteristics
3Reliability
If the system modifies word predictions based on context information and semantic references, then the system can filter inappropriate predictions, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The system applies semantic filtering selectively rather than to all predictions uniformly. It focuses computational resources on filtering only those predictions that have potential semantic issues, using efficient comparison algorithms that quickly eliminate obviously appropriate predictions while applying more thorough filtering only when needed, thereby reducing overall system complexity while maintaining high reliability
Data Source
AI summary
Systems and processes for modifying word predictions are provided. In one example, a user input is received including one or more words. A prediction of a word sequence corresponding to one or more words is obtained, and context information associated with the word sequence is obtained. In accordance with a determination, based on the context information, that the prediction of the word sequence corresponds to a predetermined semantic reference, the prediction of the word sequence is modified, and an output is provided corresponding to the modified prediction of the word sequence. In accordance with a determination, based on the context information, that the prediction of the word sequence does not correspond to a predetermined semantic reference, an output is provided corresponding to the prediction of the word sequence.