Context-Aware Chat Text Extraction Using Machine Learning
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Solution Overview
Problem
Current chat messaging and text messaging applications lack the ability to organize and categorize text data effectively, making it difficult for users to identify relevant information without manual searching, and existing keyword search tools fail to aggregate information meaningfully across messages and recipients.
Innovation Solution
A mobile computing device equipped with a machine-learned context determination model and a text extraction model that analyzes user data from various sources, including location, motion, and calendar applications, to determine the user's context and extract relevant text messages, providing them in a structured and automated manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching through chat messages is used to find relevant information, then users can locate specific text data, but users spend substantial time scrolling through long lists of chat recipients and messages
Solution Approach 1:
The system performs automated context determination and text extraction without requiring user intervention. The machine-learned models automatically analyze chat messages, determine user context, and extract relevant text portions, allowing the system to serve itself rather than requiring manual user searching through messages
Solution Approach 2:
The system pre-processes and organizes chat message data by determining user contexts and extracting relevant text in advance. This preliminary organization of information allows users to quickly access pre-sorted relevant content without having to manually search through unorganized message lists when needed
2Measurement precision
If keyword search tools are used to search through text in chat applications, then users can find specific keywords, but the tools lack ability to aggregate information from different messages and recipients in a meaningfully related manner
Solution Approach 1:
The system merges information from multiple chat messages and recipients by determining a unified user context that spans across different communication threads. The machine-learned context determination model integrates data from various sources including location, calendar, and multiple chat conversations to create a comprehensive contextual understanding that aggregates information meaningfully
Solution Approach 2:
The machine-learned context determination model acts as an intermediary between raw chat messages and user queries. It processes and interprets the substance of text data, assigning contextual meanings that enable meaningful aggregation of information across different messages and recipients, rather than simply matching keywords
3Device complexity
If chat messaging applications organize text data with identifiers only (sender, recipient, timestamp), then data structure is simple, but the applications cannot identify relevant portions of text data based on substance or topics
Solution Approach 1:
The system segments text data organization into multiple hierarchical levels: basic identifiers (sender, recipient, timestamp) and contextual categories (user activities, topics, events). This segmentation allows the system to maintain simple data storage while enabling sophisticated categorization based on the substance of text data through machine-learned context determination
Solution Approach 2:
The system changes the organizational parameters of text data from static identifiers only to dynamic contextual parameters. By using machine-learned models to determine user context and assign categorical labels based on text substance, the system transforms how data is organized and retrieved without fundamentally changing the underlying data storage structure
Data Source
AI summary
The present disclosure provides systems and methods that leverage machine learning to implement context determination and/or text extraction in computing device applications. Particular embodiments can include and use a machine-learned text extraction model that has been trained to receive one or more messages containing text and determine one or more portions of extracted text from the one or more messages as well as a corresponding user context assigned to each of the one or more portions of extracted text. In addition, or alternatively, particular embodiments can include and use a machine-learned context determination model that has been trained to receive one or more portions of device data from one or more input sources available at the mobile computing device and determine a current user context indicative of one or more activities in which a user of the mobile computing device is currently engaged.