Expert Identification via Activity Gauge Scoring
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Solution Overview
Problem
Users on social platforms like Facebook, Twitter, and LinkedIn are not effectively identified or awarded for their skills despite sharing them, leading to a lack of recognition for their expertise.
Innovation Solution
A system comprising a processor and memory with modules for mining activity data from web-based platforms, comparing it to predefined subjects, and assigning performance points based on user feedback and parameters like distance, speed, frequency, and quality, to generate an activity gauge indicating expertise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users share their skills and knowledge on social platforms, then knowledge sharing and platform activity increase, but user expertise recognition and motivation remain insufficient
Solution Approach 1:
The system implements a feedback mechanism where users receive performance points based on their activity quality and quantity. This feedback loop motivates continued knowledge sharing by recognizing and rewarding expert contributions through point accumulation and ranking displays.
Solution Approach 2:
The patent replaces manual expert identification with an automated electronic system that uses algorithms to analyze user activity data, calculate performance points, and generate activity gauges. This substitution enables scalable and objective expert recognition across large user bases.
2Measurement precision
If manual expert identification is used, then recognition accuracy may be high for small groups, but scalability and efficiency deteriorate
Solution Approach 1:
The system performs self-service by automatically collecting user activity data, analyzing it through predefined algorithms, and generating expert rankings without requiring manual intervention. This enables the system to scale efficiently while maintaining consistent evaluation criteria.
Solution Approach 2:
The patent transforms qualitative expert assessment into quantitative measurement by introducing performance points and activity gauges. This parameter transformation enables automated calculation and comparison of user expertise levels across the entire platform.
3Productivity
If automated scoring systems are implemented, then identification efficiency increases, but system complexity and measurement accuracy challenges arise
Solution Approach 1:
The system segments the expert identification process into distinct modules: data collection, activity analysis, performance point calculation, and ranking generation. This segmentation simplifies the overall system architecture and enables independent optimization of each component.
Solution Approach 2:
The activity gauge system serves multiple functions simultaneously: it measures user expertise, motivates continued participation, ranks users for visibility, and provides feedback for self-improvement. This multi-functionality reduces the need for separate systems for each purpose.
Data Source
AI summary
Disclosed is a system for determining an expert of one or more subjects on a web-based platform. The system comprises a mining module for mining activity data of at least one user of a plurality of users from the web-based platform. The mining module may further compare the activity data with one or more subjects. The mining module may further label the activity data to a subject of the one or more subjects. A scoring module may assign performance points to the at least one user associated to the activity data. The scoring module may further assign subject points to the subject. The scoring module may further generate an activity gauge for the at least one user based on the performance points assigned and the subject points. The scoring module may further classify the at least one user as the expert of the subject.