Screen Image Analysis for Cross-Application User Intent Inference
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Solution Overview
Problem
Conventional user behavior monitoring systems are limited to analyzing data within specific application programming interfaces (APIs) or web interactions, failing to capture multitasking scenarios and requiring multiple systems to track user interactions across multiple applications, with inadequate correlation of data across different computing devices.
Innovation Solution
A machine learning system that analyzes image data from computing screens using pattern recognition, OCR, and video information to infer user intent and action, capable of continuous recording across multiple devices with minimal CPU usage, and employs recursive learning algorithms to optimize actions and identify bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional user behavior monitoring systems analyze data within specific APIs or web interactions, then data analysis within a single application context is improved, but the ability to track user behavior across multiple applications and devices deteriorates
Solution Approach 1:
The system employs a universal screen capture and analysis platform that can monitor multiple applications, web browsers, and devices simultaneously through a single integrated system. The machine learning model is designed to process diverse data types from different application contexts and unify them into a comprehensive user behavior profile, eliminating the need for separate monitoring systems for each application.
Solution Approach 2:
The patent merges data from multiple sources including different applications, web interactions, and device states into a unified user behavior timeline. The system combines screen images, application data, user actions, and contextual information into a single integrated structure that enables holistic analysis of user behavior across all platforms and applications.
2Adaptability or versatility
If multiple conventional monitoring systems are implemented to monitor user interactions in multiple programs, then coverage of multiple applications is improved, but system complexity and data correlation difficulty worsen
Solution Approach 1:
A single monitoring system is designed to handle multiple applications and devices through unified screen capture and machine learning analysis. The system processes diverse data from different sources through common pipelines and produces correlated outputs, eliminating the need for multiple separate monitoring systems while maintaining comprehensive coverage.
Solution Approach 2:
The system introduces a central machine learning-based analysis layer that acts as an intermediary between data collection and user behavior inference. This intermediary layer receives data from multiple sources, normalizes formats, correlates timelines, and generates unified user behavior models, simplifying the overall system architecture.
3Quantity of substance
If conventional screen recording methods are used, then basic screen capture is improved, but continuous recording capability and data processing efficiency deteriorate
Solution Approach 1:
The system implements continuous screen capture and analysis that operates uninterrupted over extended periods. The machine learning model continuously processes screen images, identifies user actions, and updates behavior profiles in real-time, enabling unlimited continuous recording without manual intervention or processing delays.
Solution Approach 2:
The machine learning system automatically processes, analyzes, and correlates data without requiring manual intervention. The system self-regulates its own analysis priorities, identifies relevant patterns, and maintains continuous operation with minimal resource consumption, improving processing efficiency while enabling extended recording durations.
Data Source
AI summary
System(s) and method(s) that analyze image data associated with a computing screen operated by a user, and learns the image data (e.g., using pattern recognition, historical information analysis, user implicit and explicit training data, optical character recognition (OCR), video information, 360°/panoramic recordings, and so on) to concurrently glean information regarding multiple states of user interaction (e.g., analyzing data associated with multiple applications open on a desktop, mobile phone or tablet). A machine learning model is trained on analysis of graphical image data associated with screen display to determine or infer user intent. An input component receives image data regarding a screen display associated with user interaction with a computing device. An analysis component employs the model to determine or infer user intent based on the image data analysis; and an action component provisions services to the user as a function of the determined or inferred user intent. In an implementation, a gaming component gamifies interaction with the user in connection with explicitly training the model.