ML Preference Modeling for Candidate Matching

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing matching processes lack efficiency in identifying mutual interest between individuals, often relying on one-sided perceptions and user control, which can lead to suboptimal matches and wasted time and emotion.

Innovation Solution

A system and method using machine learning to model user preferences based on interaction data, creating an improved candidate pool by iteratively refining user preferences and providing initial match scores for selecting interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If machine learning models are used to model user preferences and optimize candidate pools, then match quality and mutual interest identification are improved, but user control and autonomy over the selection process are reduced

Engineering Contradiction:

Improvemutual interest identification accuracyVSAvoiduser control over selection process

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system segments the matching process into multiple components: user-controlled preference setting, automated machine learning-based candidate optimization, and iterative feedback loops. This allows the system to provide precise mutual interest identification through ML while maintaining user control through preference configuration and feedback mechanisms.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-modeling user preferences using machine learning algorithms before the user actively engages in selection. The ML model proactively optimizes candidate pools based on inferred preferences, reducing the user's selection burden while maintaining accuracy through subsequent feedback adjustments.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If machine learning models iteratively refine user preferences based on interaction feedback, then candidate pool quality is improved, but system complexity and computational resources increase

Engineering Contradiction:

Improvecandidate pool optimization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms where user interactions with candidates (acceptance, rejection, engagement metrics) are continuously fed back into the machine learning model. This iterative feedback loop refines preference modeling and optimizes candidate pools over time, improving productivity while managing complexity through incremental learning rather than complete system redesign.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system adopts dynamic preference modeling where user preferences are not static but evolve iteratively based on interaction feedback. The ML model dynamically adjusts candidate recommendations as it learns from user behavior patterns, improving optimization efficiency while adapting to changing user needs without requiring complete system restructuring.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If the system focuses on one-sided user perception of interest, then user autonomy is maintained, but match optimality and mutual interest detection deteriorate

Engineering Contradiction:

Improveuser autonomy in selectionVSAvoidmatch optimality

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The machine learning model acts as an intermediary between user preferences and candidate selection. It translates user autonomy into optimized matches by inferring preferences from interactions and mediating the selection process, thereby maintaining user autonomy while improving match optimality through data-driven insights that users alone may not perceive.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by transitioning from explicit user-stated preferences to implicitly inferred preferences based on interaction data. This parameter transformation allows the system to maintain user autonomy in setting initial preferences while improving match optimality by incorporating behavioral patterns and subtle signals that users may not consciously recognize or articulate.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250131334A1Machine learning for modeling preference data to optimize interactions with candidate partners

Publication Date: 2025.04.24 SENSTASY INC

AI summary

Technologies are provided for optimizing candidate partners for a user interaction. The technologies can facilitate a trust in facts and identify mutual interest. The technologies can identify the location of users, share personalized information, provide tools for matching users to candidates, exchange data, advertise to users with tracking algorithms, create avatars, host digital interactions between users, and provide user assessments of other users. Nodes of users, and information on user behavior patterns can help identify matches. Users can share their current moods to communicate with other users. Machine learning is included for modeling a user's own feedback from actual interactions in a pool of candidates. The input to the model includes sets of interaction data on users, and the output from the model is an improved, modeled set of user preferences to improve the user's candidate pool. Images of virtual candidates can be created at each iteration of the model.