Embedded Browser Imitation Learning for User Training
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Solution Overview
Problem
Enterprises face challenges in managing access to network resources and providing effective user training due to differences in client devices and varying user proficiency levels, especially in a mobile workforce.
Innovation Solution
An embedded browser within a client application monitors user interactions, classifies tasks, identifies proficient users, and generates training content by recording and sanitizing interactions to help less adept users, utilizing 'imitation learning' and 'optimized SaaS session recording' to create efficient training examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an embedded browser monitors and records user interactions to generate training content, then training quality and user proficiency improvement are enhanced, but system complexity and data processing requirements increase
Solution Approach 1:
The system creates simplified copies of proficient user interactions as training content. Instead of monitoring and analyzing all raw interaction data, the system identifies and replicates key successful patterns from proficient users, transforming complex behavioral data into structured training examples that can be easily consumed by less proficient users.
Solution Approach 2:
The embedded browser acts as an intermediary layer between the network application and users. It captures interaction data at the browser level, processes it through classification algorithms, and generates training content without requiring modifications to the underlying network application, thereby reducing overall system complexity.
2Productivity
If the system records detailed user interactions for training purposes, then training effectiveness improves, but data storage and processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant interaction elements from complete user sessions. Instead of storing entire interaction recordings, it identifies and extracts key steps, actions, and decision points that constitute effective task completion, significantly reducing data storage requirements while maintaining training effectiveness.
Solution Approach 2:
User interactions are segmented into discrete tasks and sub-tasks rather than being stored as continuous sessions. The system classifies interactions into task categories, identifies task boundaries, and stores segmented task representations that are more compact and easier to process for training purposes.
3Speed
If the system provides real-time monitoring and training assistance, then user proficiency improvement accelerates, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary classification and analysis of user interactions during task execution. By categorizing interactions and identifying task patterns in real-time, it prepares training content and recommendations ahead of when they are needed, reducing computational burden during critical training moments and enabling faster proficiency improvement.
Data Source
AI summary
Embodiments described include systems and methods for generating training content for completion of tasks. The method includes receiving, from each of a plurality of client applications, interactions recorded by the client application via an embedded browser of the client application. The method includes classifying the interactions received from each client application into one or more tasks. The method includes selecting, for a first task of the one or more tasks, from the interactions classified into the first task, a subset of interactions to be included in a training content including a recorded example of performing the first task across the one or more network application. The method includes generating the training content configured to be transmitted to client applications responsive to receiving a request related to the first task.