Interaction Outcome Prediction Using Multi-Factor User Profiles
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Solution Overview
Problem
Existing systems for predicting human interactions are often inaccurate and do not consider the vast amount of available data, leading to inefficient use of time and resources in forming relationships and professional connections.
Innovation Solution
A system that aggregates user characteristics using a database and computing devices to predict interaction outcomes through identifiers composed of alphabetic or numeric characters, analyzing communication, intimacy, adaptability, and interactivity to determine compatibility and facilitate interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personality tests are used to determine relationship likelihood, then some prediction capability is provided, but the tests are inaccurate and incomplete leading to lost time and resources
Solution Approach 1:
The patent transforms the prediction approach by changing parameters from simple personality test results to a comprehensive multi-dimensional analysis including over 100 characteristics such as communication styles, emotional intelligence, values, and behavioral patterns. This parameter expansion significantly improves prediction accuracy while the automated system efficiency mitigates time loss
Solution Approach 2:
The patent segments the interaction prediction into multiple independent characteristic dimensions (communication, emotional intelligence, values, behavioral patterns, etc.), each analyzed separately and then integrated. This segmentation allows for more precise measurement of each dimension while maintaining overall system manageability and accuracy
2Loss of information
If image-based systems are used to determine relationship likelihood, then physical appearance matching is provided, but the systems lack understanding of the personal side of individuals
Solution Approach 1:
The patent merges multiple data sources including image analysis, personality assessments, communication patterns, and behavioral data into a unified comprehensive profile. This integration ensures no personal information is lost while the systematic organization of merged data prevents excessive complexity through structured synthesis
Solution Approach 2:
The patent creates a universal analysis framework that handles multiple types of data (visual, psychological, behavioral, contextual) through a single integrated system. This multi-functional approach comprehensively captures personal understanding while avoiding the need for separate specialized systems for each data type
3Measurement precision
If comprehensive data analysis is performed to predict interactions, then prediction accuracy is improved, but data aggregation and processing complexity increases
Solution Approach 1:
The patent segments comprehensive data analysis into distinct modular components: data collection modules for different characteristic types, separate processing modules for each characteristic dimension, and integration modules that synthesize results. This segmentation maintains high prediction accuracy while making the complex data processing manageable and systematic
Solution Approach 2:
The patent transforms raw comprehensive data into standardized characteristic parameters through automated processing algorithms. By changing the data representation from raw unstructured information to standardized parameters, the system achieves high prediction accuracy while reducing processing complexity through systematic parameter transformation
Data Source
AI summary
A system for predicting the outcome of an interaction between users is provided. The system is comprised of a database in communication with one or more computing devices via a network. The database is configured to store user characteristics generated via a processor executing instructions stored in the database. The processor performs the steps of aggregating user characteristics and assigning the user characteristics to one or more identifiers. The identifiers are each comprised of two or more indicators each corresponding to at least one user characteristic.