Machine Learning UI Adaptation for CRM Software Latency
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Solution Overview
Problem
Customizable business application software, such as CRM software, often requires manual modifications by human programmers, which can introduce latency, expense, and potential errors, and may not account for user-specific preferences and frequent interactions, impacting user experience and productivity.
Innovation Solution
A system and process that monitors and analyzes user interactions with business software to improve user experience by preloading frequently entered information and optimizing user interfaces using machine learning, reducing the number of steps required for tasks like generating invoices, and auto-filling fields based on patterns identified from user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CRM software is modified by human programmers to increase worker productivity, then functionality and user needs are addressed, but implementation latency increases and costs rise
Solution Approach 1:
The system enables self-service by allowing the CRM software to automatically modify its own interface based on user interaction patterns. The machine learning model analyzes how users interact with the software and automatically adjusts the UI layout, feature visibility, and workflow configurations without requiring external programmer intervention, thus achieving continuous adaptation while eliminating implementation delays.
Solution Approach 2:
The invention implements dynamic adaptation where the CRM software interface continuously evolves based on real-time user behavior analysis. The system dynamically reconfigures UI elements, adjusts feature priorities, and modifies workflow presentations according to observed usage patterns, enabling the software to adapt to changing user needs without fixed programming cycles.
2Adaptability or versatility
If human programmers manually modify CRM software to add features, then functionality is enhanced, but errors and malfunctions may occur
Solution Approach 1:
The system performs self-service modifications where the machine learning model automatically generates and applies UI configurations based on analyzed user patterns. This eliminates manual programming errors while maintaining software stability through systematic, data-driven adjustments rather than ad-hoc code changes. The automatic modification process ensures consistency and reduces the risk of introducing malfunctions.
3Ease of operation
If CRM software is customized to meet individual user preferences, then user experience improves, but system complexity increases
Solution Approach 1:
The invention manages complexity through dynamic adaptation rather than static customization. Instead of creating and maintaining multiple fixed configurations for different users, the system dynamically adjusts the interface for each user based on real-time behavior analysis. This approach provides personalized user experiences while maintaining a single unified codebase, avoiding the complexity of managing multiple customized versions.
Solution Approach 2:
The system optimizes user experience by changing interface parameters such as layout configurations, feature visibility, and interaction patterns based on user behavior data. Rather than adding complex customization logic, the invention adjusts existing parameters dynamically to match user preferences and workflows, achieving personalization through parameter optimization rather than structural complexity.
4Productivity
If machine learning is used to analyze user interactions and automatically optimize CRM software, then productivity and user experience improve, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing machine learning analysis on specific, high-impact user interaction patterns rather than processing all possible data points. The machine learning model targets key behavioral indicators that most significantly influence UI optimization, such as frequently accessed features and common workflow patterns, thereby achieving productivity improvements with reduced computational overhead compared to comprehensive analysis of all user activities.
Data Source
AI summary
Briefly, embodiments of a system, method, and article for receiving data from one or more clients, where the data indicates user interactions with one or more user interfaces (UIs) of an application. The data may be analyzed with machine learning to identify how users of the one or more clients interact with the UIs of the application. One or more parameters may be generated based, at least in part, on the identification of how the users of the one or more clients interact with the UIs of the application. One or more objects of the application may be modified based, at least in part, on the parameters to reduce user interactions with the one or more UIs.