Automated Desktop Analytics Trigger Generation

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Solution Overview

Problem

Current desktop analytics triggers require manual configuration and maintenance by trained personnel with specific business and technical knowledge, leading to time-consuming training and delayed return on investment for customers.

Innovation Solution

An automated method and system for producing desktop analytics triggers using data mining and machine learning algorithms to analyze user behavior and create triggers, which can apply retrospectively to historical data, reducing the need for manual setup and maintenance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual configuration and maintenance of desktop analytics triggers is performed by trained personnel, then the triggers can be accurately configured with business knowledge, but the training time and cost increase significantly

Engineering Contradiction:

Improvetrigger configuration accuracyVSAvoidtraining time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating desktop analytics triggers through machine learning algorithms that analyze user behavior data, eliminating the need for manual configuration by trained personnel while maintaining accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual trigger configuration with an automated intelligent system using machine learning and data mining algorithms that process user behavior data to generate triggers automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If manual setup and maintenance of desktop analytics triggers is performed, then the triggers can be customized for specific customer needs, but the time to value and return on investment are delayed

Engineering Contradiction:

Improvetrigger customizationVSAvoidtime to value

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system automatically adapts to customer needs by analyzing their specific user behavior data and generating customized triggers without requiring manual setup, thereby accelerating time to value while maintaining adaptability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of user behavior data to pre-configure appropriate triggers before they are needed, enabling immediate deployment and faster realization of business value

Inventive Principle:

Principle #10Preliminary action

3Reliability

If trained personnel with significant customer business knowledge configure desktop analytics triggers, then the triggers reflect accurate business processes, but the personnel training cost and time increase

Engineering Contradiction:

Improvebusiness process accuracyVSAvoidpersonnel training time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the need for trained personnel with an automated machine learning system that achieves business process accuracy by analyzing actual user behavior data, eliminating training time while maintaining reliability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system uses feedback from actual user behavior data to continuously improve trigger accuracy, replacing the need for human expertise while maintaining or enhancing business process representation accuracy

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11740986B2System and method for automated desktop analytics triggers

Publication Date: 2023.08.29 VERINT AMERICAS INC

AI summary

The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.