Generative AI Message Suggestion System with Scoring Model
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Solution Overview
Problem
Conventional messaging systems face challenges in optimizing acceptance probabilities and efficiency of message creation, particularly in scaling personalized message generation for large user bases while reducing user input burden and adapting to various hardware platforms and latency issues.
Innovation Solution
A generative message suggestion system leveraging artificial intelligence, including a generator model and scoring model, to recursively machine-generate message suggestions, customize content based on user and recipient data, and improve acceptance probabilities through a collaborative and scalable process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional messaging systems are used for large user bases, then message distribution scale is achieved, but acceptance probability optimization and personalized content generation become inefficient
Solution Approach 1:
The system enables automated self-service through AI models that autonomously generate personalized message suggestions without requiring manual user input for each message. The generator model and scoring model work together to automatically create customized content based on user profiles and communication history, eliminating the need for users to manually craft each message while maintaining high personalization quality.
Solution Approach 2:
The patent replaces manual message composition (mechanical user effort) with AI-based automated generation. The generator model substitutes the mechanical process of users writing messages with an intelligent system that automatically produces personalized content suggestions, significantly improving message creation efficiency while maintaining adaptability to different users and contexts.
2Reliability
If AI-based message generation is implemented, then acceptance probability improves, but system complexity and computational resources increase
Solution Approach 1:
The system segments the complex AI message generation task into distinct functional components: a generator model for creating message suggestions, a scoring model for evaluating and ranking suggestions, and a user interface for interaction. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining high acceptance probabilities through coordinated operation of specialized sub-systems.
Solution Approach 2:
The scoring model acts as an intermediary between the generator model and the user, evaluating and filtering generated suggestions before presentation. This intermediary layer simplifies the user interface by pre-processing and ranking multiple generated options, reducing the complexity of direct user interaction with the full AI generation process while improving acceptance probability through selective presentation of high-quality suggestions.
3Ease of operation
If real-time message suggestions are provided, then user input effort is reduced, but latency and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user profiles, communication history, and contextual information before message generation is requested. The generator model is pre-trained on extensive datasets, and the scoring model is pre-configured with evaluation criteria. This preliminary preparation enables faster real-time suggestion generation with reduced latency while maintaining low user input effort, as the heavy computational work has already been completed in advance.
Data Source
AI summary
Embodiments of the disclosed technologies include receiving first message attribute data and inputting the first message attribute data to a first machine learning model. The first machine learning model is configured to generate and output suggested message content based on first correlations between message content and message acceptance data. The first machine learning model generates a first set of message content suggestions based on the first message attribute data, and selects at least one message content suggestion from the first set of message content suggestions based on message evaluation data. Feedback data related to the selected at least one message content suggestion is received. The first machine learning model is tuned based on the feedback data. The tuned first machine learning model generates a second set of message content suggestions based on the first message attribute data.