Draft Message Feedback Using Heuristic Pre-Filtering
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Solution Overview
Problem
Generating new messages in productivity applications often requires significant rewriting and editing, reducing the overall functionality and efficiency of messaging applications.
Innovation Solution
The system uses combinations of heuristics, models, and generative AI models to provide advanced feedback for draft messages, triggering feedback notifications in the user interface and reserving resource-intensive AI model execution for instances where feedback is likely to be useful.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the generative AI model is executed for every draft message to provide feedback, then the quality and relevance of feedback is improved, but the processing time and computational resources are excessively consumed
Solution Approach 1:
The patent applies preliminary action by using lightweight heuristics and models to pre-analyze draft messages before submitting them to the generative AI model. This preliminary filtering identifies only those messages that would benefit from advanced feedback, allowing the resource-intensive AI model to be executed selectively rather than for every draft, thus reducing overall processing time while maintaining feedback quality for messages that need it.
Solution Approach 2:
The patent implements local quality by applying different levels of analysis to different messages based on their characteristics. Simple messages receive basic heuristic feedback, while complex or problematic messages receive comprehensive AI-generated feedback. This differentiated approach optimizes resource allocation by providing high-quality feedback only where necessary, rather than uniformly applying the same level of analysis to all messages.
2Measurement precision
If the generative AI model is executed for every draft message to provide feedback, then the comprehensiveness of feedback is improved, but the computational resources are excessively consumed
Solution Approach 1:
The system performs preliminary analysis using computationally lightweight heuristics and models to assess draft messages before engaging the generative AI model. This preliminary step identifies messages with specific characteristics (e.g., potential issues, complexity thresholds) that warrant comprehensive AI feedback, thereby conserving computational resources by avoiding unnecessary AI model execution for straightforward messages.
Solution Approach 2:
The patent applies differentiated feedback strategies where simple messages receive basic heuristic analysis while complex or problematic messages receive comprehensive AI-generated feedback. This local quality approach ensures computational resources are concentrated on messages that genuinely require advanced analysis, optimizing the balance between feedback comprehensiveness and resource consumption.
3Productivity
If feedback is provided for all draft messages, then the usefulness of feedback to users is improved, but the relevance of feedback is reduced due to information overload
Solution Approach 1:
The patent implements local quality by tailoring the feedback provided to each specific message based on its characteristics. Rather than applying a uniform feedback approach to all drafts, the system analyzes message properties and provides targeted feedback only for messages that exhibit specific patterns or potential issues, ensuring that users receive relevant, actionable insights rather than generic or redundant information.
Solution Approach 2:
The system employs intelligent feedback mechanisms that use heuristics and models to determine when and what type of feedback to provide. This feedback principle ensures that users are notified only when their draft messages would benefit from review, with feedback content specifically tailored to the identified issues, thereby maintaining high signal-to-noise ratio and preventing information overload.
Data Source
AI summary
The technology relates to systems and methods for generating advanced feedback for a draft message. The operations may include receive text for a message being drafted in a messaging application; upon an analysis condition being satisfied, analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback score for the message; and based on the feedback score crossing a feedback threshold, trigger generation of advanced feedback for the message. The operations may also or alternatively include receive an initial sent message from a messaging application; analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback score for the message; based on the feedback score crossing a feedback threshold, transmit a feedback alert message for surfacing in the messaging application; and based on receiving an interaction, trigger generation of advanced feedback for the message.