Graphical Element Selection via Contextual Machine Learning
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Solution Overview
Problem
Users face challenges in efficiently selecting relevant graphical elements, such as emojis and GIFs, for electronic communications, as existing methods require manual navigation through numerous options, leading to increased computational resources and less concise dialog interactions.
Innovation Solution
A method utilizing a trained machine learning model that determines graphical elements based on features like submitted communication history, current user states, and contextual analysis from a corpus of past communications, providing relevant elements before textual input, reducing the need for users to search through numerous options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually navigate through numerous graphical element options, then they can select from a wide variety of emojis and GIFs, but it increases computational resource usage and makes dialog interactions less concise
Solution Approach 1:
The system performs preliminary analysis of dialog context, user preferences, and communication features before the user needs to select graphical elements. By pre-computing and pre-filtering relevant graphical elements based on the current dialog state and user profile, the system reduces the computational burden during actual interaction while still providing a versatile selection of appropriate emojis and GIFs.
2Ease of operation
If users manually search through numerous graphical element options, then they can find relevant elements, but it increases the time and steps required for selection
Solution Approach 1:
The system replaces the mechanical manual search process with an automated machine learning-based recommendation system. The ML model analyzes dialog context, user behavior patterns, and communication features to automatically generate and present a curated subset of relevant graphical elements, substituting the manual browsing mechanism with an intelligent recommendation mechanism that significantly reduces selection time while maintaining ease of use.
3Adaptability or versatility
If the system provides all available graphical elements to users, then users have complete options, but it increases device complexity and computational resource requirements
Solution Approach 1:
The system segments the large set of all available graphical elements into multiple context-specific subsets using machine learning analysis. Instead of presenting all emojis and GIFs at once, the system divides them into relevant categories based on dialog context, user preferences, and communication features, presenting only the appropriate segment to the user. This segmentation reduces system complexity and computational requirements while maintaining access to the full variety of graphical elements when needed.
4Measurement precision
If the system analyzes extensive dialog history and user states to determine graphical elements, then the relevance of suggested elements increases, but the computational processing required increases
Solution Approach 1:
The system applies partial analysis by focusing on the most influential features from dialog history and user states rather than processing all available data. The machine learning model identifies and prioritizes key contextual signals that have the greatest impact on graphical element relevance, performing selective analysis on these critical features while skipping less important data. This approach achieves high relevance in suggestions while significantly reducing computational processing energy requirements.
Data Source
AI summary
Methods, apparatus, and computer readable media related to determining graphical element(s) (e.g., emojis, GIFs, stickers) for inclusion in an electronic communication being formulated by a user via a computing device of the user, and providing the graphical element(s) for inclusion in the electronic communication. For example, the graphical element(s) may be provided for presentation to the user via a display of the computing device of the user and, in response to user interface input directed to one of the graphical element(s), that graphical element may be incorporated in the electronic communication. In various implementations, the electronic communication is a communication to be submitted as part of a dialog that involves the user and one or more additional users.