Proactive Intervention System for Software Assistance
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Solution Overview
Problem
Users of software applications often encounter issues due to improper use, lack of understanding, or configuration errors, and existing assistance mechanisms can be difficult to access or inconvenient, leading to inefficiencies in resolving these issues.
Innovation Solution
A proactive intervention system within the software application selects and processes events based on priorities, receives contextual information, and determines appropriate interventions, such as offering help or support, to provide assistance proactively through the user interface, utilizing rules and machine learning techniques to target interventions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If help content and assistance mechanisms are provided in the application, then users can receive support when needed, but users may have difficulty locating such assistance or find it inconvenient to navigate through multiple screens to access it
Solution Approach 1:
The system proactively detects user issues by monitoring application events and user behavior patterns before users explicitly request help. Intervention content is prepared and presented to users in advance of their needing to search for assistance, eliminating the time users would spend locating help resources.
Solution Approach 2:
The proactive intervention system acts as an intermediary between users and help resources. Instead of users directly navigating to help content, the system intercepts user events, determines intervention needs, and delivers contextualized assistance through the user interface, simplifying the access path.
2Measurement precision
If the system provides proactive interventions based on multiple events and contextual information, then intervention accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of determining interventions into distinct components: event collection, event prioritization, contextual information gathering, intervention determination, and delivery. Each component handles a specific aspect, making the overall system more manageable despite processing multiple data sources.
Solution Approach 2:
An event watcher component serves as an intermediary that filters and prioritizes events before they reach the intervention determination logic. This intermediary layer simplifies the complexity by pre-processing events and presenting only relevant ones to the intervention engine.
3Speed
If the system processes multiple events simultaneously with high priority, then response speed is improved, but processing complexity increases
Solution Approach 1:
Events are prioritized in advance by the event watcher component based on predefined criteria and user context. This preliminary classification allows the intervention system to process high-priority events immediately while deferring or filtering lower-priority ones, achieving fast response for critical issues without overwhelming the processing system.
Data Source
AI summary
Aspects of the present disclosure relate to proactive intervention in a software application. Embodiments include selecting, by a proactive intervention system related to an application, an event of a plurality of events related to use of the application for processing based on event priorities associated with the plurality of events. Embodiments further include receiving, by the proactive intervention system, contextual information related to the event. Embodiments further include determining, by the proactive intervention system, a proactive intervention based on the event and the contextual information. Embodiments further include determining, by the proactive intervention system, that the proactive intervention can presently be provided based on intervention availability data. Embodiments further include providing, by the proactive intervention system, the proactive intervention via a user interface associated with the application.