Editor Service Orchestrating Grammar Checker and ML Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current rule-based grammar checking systems require frequent updates with additional rules to identify various grammatical errors, limiting their effectiveness in addressing a wide range of errors, whereas machine learning fluency models can learn to identify multiple error types without explicit programming, but may not provide sufficient suggestions on their own.
Innovation Solution
An editor service orchestrates the application of both rule-based grammar checkers and machine learning fluency models to textual inputs, aggregates their results, and surfaces more comprehensive suggestions to users, while using user interactions to improve the machine learning model's performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based grammar checking systems are used, then they can identify specific grammatical errors with high precision, but they require frequent updates with additional rules to address a wide range of errors, increasing system complexity and maintenance burden
Solution Approach 1:
The patent combines rule-based grammar checking with machine learning fluency model checking into a unified system. The rule-based checker handles specific grammatical errors with high precision, while the machine learning model addresses a broader range of errors including fluency and style issues. The editor service merges results from both approaches, allowing the system to maintain high accuracy for specific error types while reducing the need for continuous rule updates through the adaptive machine learning component.
2Adaptability or versatility
If machine learning fluency models are used, then they can learn to identify multiple error types without explicit programming, but they may not provide sufficient suggestions on their own, reducing reliability
Solution Approach 1:
The editor service acts as an intermediary that coordinates between the rule-based grammar checker and the machine learning fluency model. It receives results from both systems, aggregates them, and resolves any conflicts or redundancies. This intermediary layer ensures that the machine learning model's broad error type coverage is complemented by the rule-based system's reliable, precise suggestions, creating a more robust overall system that leverages the strengths of both approaches.
Data Source
AI summary
An editor service receives a textual input. The editor service provides the textual input to a rule-based grammar checker to obtain a grammar checker result. The editor service also provides the textual input to a machine learning (ML) fluency model that checks the textual input for errors and provides a ML model result. The editor service aggregates the grammar checker result and the ML model result and generates an editor service output based upon the aggregated results. A representation of the editor service result is provided to the client computing system for surfacing through a user interface.