Context-Aware Content Assist for Messaging Systems
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Solution Overview
Problem
Existing messaging systems require users to manually search for and attach content, which is tedious and often results in failure to find the desired content in a timely manner.
Innovation Solution
A method that analyzes textual conversations to determine user intent and relationships, evaluates historical intents, and provides suggested content based on overlapping intents between recipients, using natural language processing and predefined message correlations to automate content inclusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually search for and attach content in existing messaging systems, then users can include desired content in messages, but the process is tedious and users often fail to locate desired content in a timely manner
Solution Approach 1:
The system automatically analyzes the conversation context, determines user intent, and retrieves relevant content without requiring manual user action. The messaging system performs self-service by autonomously identifying and suggesting content based on the analyzed intent and conversation history, eliminating the need for users to manually search and attach content.
Solution Approach 2:
The system pre-analyzes conversation context and prepares relevant content suggestions before the user needs to attach content. By performing preliminary analysis of the intent and retrieving potential content in advance, the system reduces the time users would otherwise spend searching for content.
2Productivity
If the system analyzes conversation context and provides automated content suggestions, then content retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of content retrieval into distinct modules: intent analysis module that processes conversation context, relationship determination module that analyzes user connections, content retrieval module that fetches relevant content, and suggestion generation module that presents options to the user. This segmentation manages complexity by distributing functions across specialized components.
Solution Approach 2:
The system introduces an intermediary content suggestion layer between the user and the content repository. This intermediary analyzes intent and relationships, then mediates by presenting curated content suggestions, reducing the complexity burden on the user while managing the sophisticated analysis in the background.
3Measurement precision
If the system determines user intent and relationships to provide personalized suggestions, then content relevance is improved, but processing requirements increase
Solution Approach 1:
The system performs partial analysis by focusing on key conversation elements and relationships rather than processing every aspect of the conversation in full detail. It determines intent and relationships to the degree necessary for generating relevant suggestions, avoiding excessive computational processing while maintaining sufficient accuracy.
Data Source
AI summary
Techniques for content augmentation and assist are provided. A textual conversation between a first user and a plurality of recipients is analyzed to determine a first intent of the first user. A set of relationships is determined between the first user and the plurality of recipients, and a set of historical intents corresponding to each of the plurality of recipients is evaluated to identify similar intents to the first intent. A plurality of predefined messages is identified based on one or more similar intents that overlap between two or more of the plurality of recipients. A first predefined message is selected, from the plurality of predefined messages, based on the first intent and the determined set of relationships, and suggested content is provided based on the first predefined message.