Goal-Oriented User Matching System for Social Networks
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Solution Overview
Problem
Current social networking platforms lack mechanisms to efficiently connect users to achieve common goals, requiring users to manually scan through contacts, select criteria, and initiate collaborations, which is time-consuming and often unreliable.
Innovation Solution
A system that prompts users to set goals and criteria, prioritizes responses, and searches social networks to identify and rank potential collaboration partners based on user inputs, allowing for automated matching and invitation of suitable candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually scan through contacts and initiate collaborations, then users can find potential partners, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service by automatically performing the collaboration matching process. Users input their goals and criteria, then the system autonomously searches through social networks, evaluates potential matches based on multiple criteria, and presents ranked results without requiring manual intervention in the search and evaluation process.
Solution Approach 2:
The patent replaces the mechanical manual process of scanning contacts and evaluating compatibility with an automated computational system. The system uses algorithmic processing to evaluate user profiles, goals, and criteria, substituting human manual effort with automated information processing and matching algorithms.
2Reliability
If users manually evaluate and select collaboration partners, then users can make decisions, but the process relies on ad-hoc decision-making and is unreliable
Solution Approach 1:
The system implements feedback mechanisms by continuously evaluating user responses to criteria questions and using this information to refine and re-rank match results. The system provides feedback to users about why certain matches are recommended and allows users to adjust criteria based on initial results, creating an iterative improvement process that enhances reliability.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weight and importance of different matching criteria based on user responses. The system modifies evaluation parameters in real-time as users provide more information about their goals and preferences, allowing the matching algorithm to adapt and improve accuracy throughout the interaction.
3Measurement precision
If the system searches multiple social networks with multiple criteria, then matching accuracy improves, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex matching task into distinct modular components: user profile extraction from different social networks, criteria evaluation modules, scoring algorithms, and result ranking mechanisms. Each component handles a specific aspect of the matching process, making the overall system more manageable and maintainable despite its comprehensive functionality.
Solution Approach 2:
The patent implements universality by designing a multi-functional platform that can search and evaluate user profiles across multiple different social networks simultaneously. The system uses a unified interface and consistent matching logic that works across various data sources, reducing the need for separate specialized systems for each network.
Data Source
AI summary
Embodiments of the present invention provide an approach for goal-based user matching among social networking environments. In a typical embodiment, a querying user is asked to respond to a series of questions related to goals, criteria, and criteria priorities (and, optionally, a desired “degree” of separation among other users). The querying user will submit his/her responses, which are then parsed and prioritized accordingly. Based on the responses, social networking sites are then searched to identify a set (e.g., at least one) of candidate users that are potential collaboration partners with the querying user. This set of candidate users may be arranged in a list and sorted according to a best match to the querying user's responses. The list can then be provided to the querying user. In addition, invites can be sent to the set of candidate users to invite such users to connect/collaborate with the querying user.