Implicit Feature Detection via User Behavior Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for adding new features to products are inefficient, as they require explicit user requests and manual processing, leading to delayed identification of customer needs and resource-intensive development processes, often missing opportunities to provide features that enhance product usage.
Innovation Solution
A computer-implemented method that collects and analyzes user data to determine implicit requests for new features using machine learning techniques, including pattern detection and natural language processing, to automatically generate and prioritize feature candidates without explicit user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used to add new features to products, then explicit user requests are processed, but the process is inefficient and delays identification of customer needs
Solution Approach 1:
The system performs preliminary analysis of user behavior data continuously in the background, identifying implicit feature requests before users explicitly express them. This proactive approach captures customer needs earlier in the discovery process, eliminating the delay inherent in waiting for explicit requests.
Solution Approach 2:
The system automatically analyzes user data and generates feature candidates without requiring manual intervention from product managers or direct user input. The automated analysis of usage patterns, events, and behaviors enables the system to self-identify feature opportunities, significantly improving productivity while reducing time loss.
2Productivity
If manual processing is used for explicit user requests, then feature development can proceed, but the process is resource-intensive
Solution Approach 1:
The patent replaces manual mechanical processing of feature requests with automated computational analysis. Machine learning models and data processing algorithms automatically analyze user behavior, identify patterns, and generate feature candidates, substituting human manual effort with automated systems that require fewer resources.
Solution Approach 2:
The system performs self-service by automatically generating feature candidates from user data without requiring manual triage, prioritization, or initial analysis by product teams. This automation reduces the quantity of human resources needed while improving development productivity.
3Adaptability or versatility
If explicit user requests are required for feature identification, then development can proceed, but opportunities to provide enhancing features are missed
Solution Approach 1:
The system introduces an intermediary layer of automated analysis between user behavior and feature identification. This intermediary processes user data, detects patterns, and translates implicit behaviors into actionable feature candidates, capturing information that would otherwise be lost without explicit user requests.
Solution Approach 2:
The system performs preliminary identification of feature opportunities by analyzing user behavior patterns before users consciously recognize or articulate their needs. This proactive detection captures implicit requirements that conventional explicit-request systems would miss, improving overall feature coverage and adaptability.
Data Source
AI summary
A computer-implemented method, according to one embodiment, includes collecting user data associated with a user's behavior with respect to a predetermined product and analyzing the user data for determining a subset of the user data that constitutes an implicit request for a new candidate feature for the predetermined product. A new candidate feature for the predetermined product that satisfies the implicit request is determined based on the subset of the user data. The method further includes outputting an indication of the determined new candidate feature to a device associated with development of the predetermined product. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.