AI Graphic Recommendation for Context-Aware Messaging
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Solution Overview
Problem
Existing messaging systems fail to capture nuances, subtleties, and variations in message content and context, and do not consider user characteristics, preferences, and behaviors in recommending graphics, leading to irrelevant and unsatisfactory suggestions, with limited diversity, novelty, and relevance.
Innovation Solution
A message graphic recommendation system using AI models to analyze contextual data from messages and user profiles, generating and recommending graphics that are meaningful, relevant, adaptive, and appropriate to the chat content and persona, by employing NLP and generative AI to create personalized graphics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple keyword search or text-to-emoticon conversion is used for graphic recommendations, then the system is easy to operate and quick to respond, but the recommendations fail to capture nuances, subtleties, and variations of message content and context
Solution Approach 1:
The patent introduces an intermediary AI model layer between the simple keyword search and the final graphic recommendations. This intermediary model processes and understands the nuanced context of messages, transforming simple keywords into comprehensive context representations that capture subtleties and variations, thereby improving recommendation accuracy without requiring the entire system to be fundamentally complex
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with an AI-based contextual understanding model. Instead of relying on rigid mechanical rules for text-to-emoticon conversion, the system uses machine learning models to interpret message context, user characteristics, and behavioral patterns, achieving superior precision while managing complexity through automated learning
2Adaptability or versatility
If existing recommendation systems use predefined graphic libraries, then the system is easy to manufacture and maintain, but the selection has limited diversity, novelty, and relevance to message content and context
Solution Approach 1:
The patent transforms the static predefined graphic library into a dynamic recommendation system that adapts to each message's context, user characteristics, and behavioral patterns. The system dynamically generates and updates recommendations based on real-time analysis, allowing the graphic selection to be diverse, novel, and highly relevant while maintaining implementation feasibility through modular AI model integration
Solution Approach 2:
The patent changes the parameters used for graphic selection from simple keyword matching to multi-dimensional parameters including contextual understanding, user characteristics, behavioral patterns, and message nuances. This parameter transformation enables diverse and relevant recommendations while the system manages complexity through structured parameter processing and AI model optimization
3Reliability
If existing systems do not consider user characteristics, preferences, and behaviors, then the system is simple to operate and implement, but the recommendations are irrelevant and unsatisfactory to users
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing user characteristics, preferences, and behavioral patterns in structured formats before they are needed for recommendations. This preliminary data preparation and feature extraction reduces the complexity of real-time processing while ensuring that relevant user information is readily available to improve recommendation reliability and personalization
Solution Approach 2:
The patent introduces an intermediary layer that processes and integrates user characteristics, preferences, and behavioral data between the message input and recommendation generation. This intermediary processing module transforms raw user data into meaningful features that enhance recommendation relevance while managing data processing complexity through structured transformation and filtering
Data Source
AI summary
A method for generating and recommending graphics for inclusion in messages includes delivering message data to a context determining model of a graphic recommendation system, the context determining model being trained to process message data to identify context data pertaining to at least one of the message, the user, and the message partner. A prompt is generated that includes the context data and instructions for causing a model to generate one or more graphics for inclusion in the message. The graphics are delivered to a graphic recommendation model along with a plurality of predefined graphics of the messaging system to rank the graphics using at least one ranking algorithm and to select a predetermined number of graphics to include in a graphic recommendation for the message based on ranks and delivering the graphic recommendation to the messaging client.