Multimodal Object Prediction for Faster Contextual Message Replies
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Solution Overview
Problem
Existing digital conversation tools struggle with efficiently predicting and providing contextually appropriate multimodal objects, such as emojis and GIFs, leading to user inconvenience and reduced engagement due to the time-consuming task of navigating through numerous options.
Innovation Solution
A method and system that utilize a universal database to perform unified searches based on user intent and contextual data, ranking multimodal objects like emojis, stickers, and GIFs using neural networks and Document Term Matrices, and providing personalized predictions based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of supporting objects are provided in digital conversation applications, then the variety and expressiveness of communication is improved, but the time and effort required to detect and select appropriate supporting objects increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing multimodal objects (emojis, GIFs, stickers) with metadata including semantic meaning, visual features, and contextual associations. This preparation enables rapid retrieval during actual conversation without requiring users to manually search through large collections, thus resolving the contradiction between object variety and selection time.
Solution Approach 2:
The patent introduces an intermediary system comprising a database and retrieval mechanism that mediates between the user's textual input and the large collection of multimodal objects. This intermediary layer processes the text, queries the database, and returns relevant object suggestions, eliminating the need for users to manually navigate through numerous options and reducing selection time while maintaining access to diverse objects.
2Adaptability or versatility
If users are provided with numerous supporting objects, then the contextuality and appropriateness of communication is improved, but the ease of operation is reduced
Solution Approach 1:
The system implements self-service by automatically analyzing user input text, determining intent, and retrieving contextually appropriate multimodal objects without requiring user intervention for selection. The system serves itself by processing the text through NLP models, querying the database, and presenting relevant suggestions, thereby maintaining high contextual appropriateness while significantly improving ease of operation.
Solution Approach 2:
The patent replaces the mechanical manual selection process with an automated computational system. Instead of users mechanically browsing and selecting from numerous objects, the system uses NLP processing, database querying, and algorithmic retrieval to automatically determine and present appropriate objects, thus maintaining contextual accuracy while dramatically improving operational ease.
3Ease of manufacture
If existing techniques provide canned answers based on text messages, then the response generation is simplified, but the prediction capability for multimodal supporting objects is insufficient
Solution Approach 1:
The patent implements universality by creating a unified system that handles multiple types of content generation and retrieval functions within a single framework. The same database and retrieval mechanism serve both text-based canned answers and multimodal object predictions (emojis, GIFs, stickers), allowing the system to generate diverse response types appropriately while maintaining ease of operation through a single integrated approach.
Solution Approach 2:
The system extends the traditional text-only response dimension to a multimodal dimension by incorporating visual elements (emojis, GIFs, stickers) into the response generation process. This dimensional expansion allows the system to provide richer, more expressive responses while maintaining ease of manufacture through automated retrieval mechanisms, thus resolving the contradiction between response simplicity and multimodal prediction capability.
Data Source
AI summary
The present disclosure is related to the field of digital communication and provides a method and system for providing personalized multimodal objects in real-time. An object predicting system receives a text input from at least one application installed in a user device associated with a user. Thereafter, the object predicting system determines an intent of the user by analyzing the text input, which is then correlated with contextual data to generate a query. Subsequently, the object predicting system performs a unified search in a universal database, based on the query, wherein the universal database comprises multimodal data. Further, a plurality of multimodal objects predicted in response to the unified search are ranked based on at least one of the contextual data and user preferences. Finally, at least one of the predicted plurality of multimodal objected related to the text input are provided to the user based on the ranking.