Proxy-Based Activity Detection in SaaS Applications
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Solution Overview
Problem
Enterprises face difficulties in monitoring and analyzing activities of end-users within Software as a Service (SaaS) applications since they do not own the third-party servers hosting these applications, making it challenging to identify anomalies and improve user performance across a large set of users.
Innovation Solution
An analytics server is implemented with a processor that receives end-user events from client computing devices, correlates them with REST calls and responses, and translates these into event vectors to determine similarities among users, associating similar activities with quality indicators to identify anomalies and recommend corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprises use third-party SaaS applications hosted on external servers, then application functionality and scalability are improved, but the ability to monitor and analyze user activities is reduced
Solution Approach 1:
The patent introduces a proxy server as an intermediary component positioned between the client computing devices and the third-party SaaS application server. This proxy server intercepts and captures API calls and responses, enabling the enterprise to monitor user activities without direct access to the third-party server. The proxy acts as a mediator that allows activity tracking while preserving the use of external SaaS applications.
2Measurement precision
If enterprises implement comprehensive activity monitoring across all users, then anomaly detection capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent creates simplified copies or representations of user activities in the form of structured data records captured at the proxy server. Instead of implementing complex monitoring logic across the entire system, the solution captures essential activity data (API calls, responses, timestamps) and stores them as copyable records that can be analyzed centrally. This reduces system complexity by separating data collection from analysis.
Solution Approach 2:
The patent implements a feedback mechanism where captured activity data is analyzed to generate insights about user behavior patterns and anomalies. The system processes the captured data, compares it against expected patterns, and provides feedback about detected anomalies. This feedback loop enables precise anomaly detection without requiring proportional increases in system complexity.
3Loss of information
If enterprises capture detailed end-user events at the DOM level, then contextual information quality is improved, but data processing overhead increases
Solution Approach 1:
The patent extracts only the essential and relevant contextual information from the comprehensive DOM-level event data. Instead of processing all captured events, the system selectively extracts meaningful attributes such as user actions, page navigation, and key interactions. This extraction approach maintains high contextual information quality while significantly reducing the volume of data requiring further processing and analysis.
Data Source
AI summary
An analytics server receives from client computing devices end-user events. Each client computing device is operated by an end-user to access an application at a web server based on the end-user events resulting in calls being passed through a proxy to the web server. The analytics server receives from the proxy the calls being made to the web server, and receives return responses from the web server being passed through the proxy. The return responses correspond to activities being performed within the application. The end-user events are correlated with the corresponding calls and return responses from the proxy. Respective correlated end-user events, calls and return responses are translated into respective event vectors. The respective event vectors are processed to determine similarities among the client computing devices. The similar activities are associated with a quality indicator to identify anomalies within the application for corrective action to be taken.