Feature Prioritization System Using Analytics Vectors

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Solution Overview

Problem

Organizations face challenges in prioritizing feature enhancements and updates for applications to maximize user impact and productivity, as existing methods lack a systematic approach to identify high-priority features based on user interactions and analytics.

Innovation Solution

A computer-implemented method using analytics data to generate vectors for feature performance, engagement, visitor metrics, and adoption, assigning weights to these vectors to determine a priority score for enhancing or improving application features, thereby optimizing business value.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual prioritization methods are used for feature enhancements, then resource allocation can be made, but it lacks systematic approach to identify high-priority features based on user interactions and analytics

Engineering Contradiction:

Improvefeature prioritization efficiencyVSAvoiduser interaction data utilization

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent replaces manual prioritization processes with an automated analytics-driven system. Machine learning models process user interaction data, session recordings, and product analytics to automatically generate priority scores for feature enhancements, eliminating the need for manual assessment and ensuring systematic utilization of user behavior information.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service prioritization by automatically analyzing user interactions and generating enhancement priorities without requiring manual intervention. The analytics engine continuously processes user behavior data and autonomously identifies which features should be enhanced based on actual usage patterns and user impact metrics.

Inventive Principle:

Principle #25Self-service

2Reliability

If resources are allocated without systematic prioritization, then development can proceed, but user impact and productivity enhancement are not maximized

Engineering Contradiction:

Improveresource allocation effectivenessVSAvoiduser productivity enhancement

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements continuous feedback loops where user interaction data, session recordings, and product analytics are constantly processed to update priority scores. This feedback mechanism ensures that resource allocation decisions are based on the most current user behavior information, maximizing the reliability of prioritization and ensuring that enhancements directly address user productivity needs.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of user interactions and analytics data before resource allocation decisions are made. By pre-processing and evaluating user behavior patterns, the system identifies high-impact features in advance, ensuring that resources are allocated to features that will most effectively enhance user productivity before development begins.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If all features are updated simultaneously, then comprehensive improvement is achieved, but resource efficiency is reduced

Engineering Contradiction:

Improvefeature coverageVSAvoidresource utilization efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent segments features into priority tiers based on analytics-driven scoring. Instead of treating all features equally, the system divides them into high, medium, and low priority groups based on user interaction data and impact metrics. This segmentation enables focused resource allocation to high-priority features while maintaining comprehensive adaptability across all feature categories over time.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements partial action by focusing resources on a subset of high-priority features rather than attempting to update all features simultaneously. The analytics engine identifies and prioritizes only the most impactful features for enhancement in each iteration, achieving significant user value with optimized resource utilization while maintaining the capability to address all features progressively.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11363109B2Autonomous intelligent system for feature enhancement and improvement prioritization

Publication Date: 2022.06.14 DELL PROD LP

AI summary

Systems and methods for prioritizing enhancement and/or improvements of features of a user application are disclosed. In at least one embodiment, a method includes retrieving analytics data generated by an analytics engine, where the analytics data includes data relating to user interactions with a feature of the user application. A plurality of vectors is generated from the analytics data. The plurality of vectors include vectors corresponding to user interactions with the feature. A priority is assigned to enhancing and/or improving the feature of the user application based on a weighted sum of the plurality of vectors.