Automated Interface Customization via User Interaction Analysis

Solution Overview

Problem

Users of cloud business applications face inefficiencies due to repetitive manual interactions with default user interfaces, which are not designed to facilitate specific business logic, leading to time consumption and resource burdens for users, businesses, and cloud application providers.

Innovation Solution

Implementing automated process discovery and facilitation using machine learning to analyze user interactions, generating customized user interfaces that streamline processes by automating repetitive tasks and adapting to individual user preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a default user interface is used, then the system is simple and easy to maintain, but users must perform repetitive manual interactions which consumes time and resources

Engineering Contradiction:

Improveuser efficiencyVSAvoidtime for repetitive interactions

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system automatically discovers business processes by monitoring user interactions with the default interface and generates customized interfaces without requiring user intervention. The machine learning model autonomously analyzes interaction patterns, identifies repetitive tasks, and creates optimized interfaces that eliminate manual repetition, allowing the system to serve itself rather than requiring users to manually customize interfaces.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of user interaction patterns with the default interface before generating customized interfaces. By monitoring and analyzing interactions in advance, the machine learning model identifies repetitive tasks and business logic patterns, then proactively generates optimized interfaces that prevent future time loss from repetitive actions.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If a customized user interface is created to facilitate specific business logic, then user efficiency improves, but the system complexity increases and requires additional user effort to design

Engineering Contradiction:

Improveprocess facilitationVSAvoidinterface customization complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system automatically discovers business processes by monitoring user interactions with the default interface and generates customized interfaces without requiring user intervention. The machine learning model autonomously analyzes interaction patterns, identifies repetitive tasks, and creates optimized interfaces that eliminate manual repetition, allowing the system to serve itself rather than requiring users to manually customize interfaces.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes the parameters of the user interface by generating customized versions tailored to specific business processes. The machine learning model analyzes interaction patterns and modifies interface parameters such as layout, element arrangement, and workflow sequences to optimize for discovered business logic, transforming the generic default interface into process-specific optimized interfaces.

Inventive Principle:

Principle #35Parameter changes

3Loss of time

If machine learning is used to automatically generate customized interfaces, then repetitive interactions are reduced, but computing resources are consumed for analysis and generation

Engineering Contradiction:

Improverepetitive manual interactionsVSAvoidcomputing resources for ML processing

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis of user interaction patterns with the default interface before generating customized interfaces. By monitoring and analyzing interactions in advance, the machine learning model identifies repetitive tasks and business logic patterns, then proactively generates optimized interfaces that prevent future time loss from repetitive actions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors user interactions with customized interfaces and uses this feedback to refine and update the machine learning models. By analyzing ongoing interaction data, the system adapts to changing business processes and optimizes interface generations, ensuring that computing resources are invested in maintaining accuracy and relevance over time.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11294546B2Automated process discovery and facilitation within a cloud business application

Publication Date: 2022.04.05 ORACLE INT CORP

AI summary

Systems, methods, and other embodiments associated with providing automated discovery and facilitation of user business processes are described. Parse a system log of an integrated business system to identify interactions of a user with each event of a selected type of event. For each event, create a data structure that describes the interactions with the event based on the identified interactions and one or more characteristics of the event. Analyze the data structures to train a machine learning model to apply a process applied to events of the selected type by the user. Generate a customized user interface that is configured to present the user with an option to automatically carry out the process for a set of subsequent events of the selected type based on application of the model. Substitute the customized user interface for a standard user interface when transmitting instructions to display one or more subsequent events.