App User Data Extraction via Tracer and Analyzer
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Solution Overview
Problem
Current methods for extracting and analyzing user interaction data from mobile apps are cumbersome, requiring significant developer effort, are computation-heavy, and violate user privacy due to data siloing, limiting the utility and accuracy of behavioral analytics.
Innovation Solution
A system and method using a computing device with a tracer and analyzer component to detect and classify user interface events, generating behavioral data items that can be shared across apps without developer effort, leveraging a device-cloud hybrid approach to infer semantic information and store data securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to track and analyze user interaction, then data accuracy can be improved, but developer time and effort increase significantly
Solution Approach 1:
The system enables self-service by automatically extracting and classifying user interaction data without requiring manual annotation. The analytics service automatically instruments apps, extracts UI events, and classifies them using templates, eliminating the need for developer intervention in data collection while maintaining high accuracy through structured classification.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computational systems. Instead of developers manually tagging and categorizing user interactions, the system uses automated tracing, event detection, and template-based classification to achieve the same analytical goals with significantly reduced human effort.
2Ease of operation
If computation-heavy methods are used to extract user data with minimal developer effort, then ease of operation is improved, but device memory requirements increase
Solution Approach 1:
The system extracts only the essential and relevant user interaction data needed for analytics, rather than storing all raw data. By focusing on extracting specific UI events, user actions, and contextual information through targeted tracing and template matching, the system minimizes memory usage while maintaining analytical effectiveness.
Solution Approach 2:
The patent segments the data extraction and processing into distinct modular components: tracing module for event detection, analysis module for template matching, and classification module for data organization. This segmentation allows efficient processing with minimal memory footprint by handling only necessary data at each stage rather than loading everything into memory simultaneously.
3Speed
If data is stored locally on the device, then access speed is improved, but privacy violations occur
Solution Approach 1:
The system introduces an intermediary analytics service layer that processes and anonymizes user interaction data before storage or transmission. This intermediary layer acts as a mediator between data collection and storage, ensuring privacy protection through automated anonymization and selective data retention while maintaining fast access to essential analytics data.
4Loss of energy
If network overhead is reduced by processing data locally, then network efficiency is improved, but computational burden on device increases
Solution Approach 1:
The system performs partial processing locally and selectively transmits only essential aggregated data to remote servers. By implementing template-based classification and event filtering on-device, the system reduces network traffic to only the most relevant analytics data, balancing local computational effort with minimized network overhead.
Data Source
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AI summary
Systems and methods for extracting and sharing application-related user data are disclosed. A method may include extracting in-app data for at least one of the plurality of apps running on a computing device, the in-app data including content consumed by a user while the at least one app is running, and/or at least one user action taken in connection with the content. Using an entity template associated with the app, a plurality of text strings within the in-app data are classified into at least one of a plurality of data types specified by the template. At least one user data item (UDI) may be generated by combining at least a portion of the classified plurality of text strings, the at least one UDI being accessible by a second app, an operating system running on the, a service of the operating system, and/or a service running on at least another device.