App Store Infringement Detection via Decompilation and Meta-Content Analysis
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Solution Overview
Problem
The rapid growth of app stores with millions of software applications poses challenges for intellectual property stakeholders to monitor and detect infringement, as existing methods are inadequate for discovering misuse of trademarks and copyrighted content within these vast online repositories.
Innovation Solution
A system and method for monitoring app stores by crawling repositories to collect meta-content about software applications, analyzing decompiled code, and applying trigger rules with weighted ranking to identify potential infringement, enabling users to track and manage misuse of their trademarks and copyrights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If standard online search methods are used to monitor app stores, then the monitoring process is simple and easy to implement, but the ability to discover infringement is insufficient due to the vast number of apps and undiscoverable content
Solution Approach 1:
The patent segments the monitoring task into multiple components: crawling app store data, decompiling applications, extracting meta-content, and analyzing for infringement. This segmentation allows each component to be optimized independently while solving the overall detection problem.
Solution Approach 2:
The patent introduces an intermediary decompilation process that transforms compiled app binaries into analyzable code and resource files. This intermediary step enables access to hidden content that standard searches cannot detect, bridging the gap between simple search and comprehensive analysis.
2Measurement precision
If comprehensive analysis of all app content is performed to detect infringement, then detection accuracy is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary decompilation and extraction of meta-content (descriptions, keywords, ratings, reviews) before the actual infringement analysis. This preliminary action prepares the data in advance, allowing faster and more accurate detection during the monitoring phase without re-processing the entire app binary each time.
Solution Approach 2:
The patent focuses analysis on specific meta-content elements most relevant to infringement detection (descriptions, keywords, publisher information) rather than analyzing every byte of every application. This partial action approach achieves sufficient detection accuracy while significantly reducing computational overhead and time requirements.
3Productivity
If manual monitoring of app stores is performed, then the system complexity is low, but the productivity and efficiency of infringement detection are insufficient
Solution Approach 1:
The patent implements an automated system that performs self-service monitoring by crawling app stores, automatically decompiling applications, extracting meta-content, and analyzing for infringement without human intervention. This automation dramatically increases productivity while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated computational systems. Instead of human reviewers manually examining apps, the system uses automated crawlers, decompilers, and analysis algorithms to perform the same function at scale, significantly improving efficiency.
Data Source
AI summary
Novel tools and techniques for monitoring software applications, and in particular software applications available from app stores, for apps that might infringe the intellectual property of others. Merely by way of example, a tool might identify an application in an app store, download that application, and analyze that application for content that might be infringing. Such analysis can include, but is not limited to, decompiling the application. Meta-content about the application (including portions of the content) can be compared with meta-content about other applications, to identify associations between applications. In other cases, the meta-content can be used to identify acts of intellectual property infringement.