Personalized Location Discovery System with Automated Attribute Ranking
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Solution Overview
Problem
Prospective movers face significant challenges in finding ideal living locations due to the complexity of evaluating numerous attributes, lack of awareness of important factors, and the impracticality of gathering and assembling information from various sources, leading to decision fatigue and unsuitable relocation choices.
Innovation Solution
A machine-implemented architecture that uses an Internet-based system to categorize and display quantitative and qualitative data about locations, allowing users to set attribute limits, rank locations based on their preferences, and share profiles to facilitate interactive discovery of personalized living locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prospective movers manually gather and evaluate information about numerous location attributes, then they can make more informed relocation choices, but the effort and time required becomes impractical and leads to decision fatigue
Solution Approach 1:
The system automatically performs the complex task of gathering, organizing, and evaluating location attributes across multiple sources. The machine learning model autonomously processes data from diverse sources, identifies relevant attributes, and generates personalized location recommendations without requiring manual intervention from the user to collect or assemble information.
Solution Approach 2:
The patent replaces the manual mechanical process of researching and evaluating location attributes with an automated computer-based system. The machine learning algorithm substitutes human cognitive processing and manual data collection, performing complex analysis and pattern recognition automatically to produce personalized relocation recommendations.
2Measurement precision
If the system provides comprehensive information about all location attributes, then relocation choices become more accurate, but the complexity of the system and information organization increases
Solution Approach 1:
The system dynamically adjusts which attributes are considered and how they are weighted based on individual user preferences, historical data, and contextual factors. Rather than treating all attributes equally, the machine learning model adapts the parameter importance to match each user's unique relocation needs, making the comprehensive system manageable and personalized.
Solution Approach 2:
The system incorporates feedback loops where user interactions with location information, preferences, and preliminary selections are used to refine and update the machine learning model. This feedback mechanism allows the system to learn from user behavior and automatically adjust its attribute evaluation methodology, reducing perceived complexity while maintaining comprehensive coverage.
3Adaptability or versatility
If the system asks users to identify important attributes, then personalization improves, but many users are unaware of critical factors and provide insufficient information
Solution Approach 1:
The system performs preliminary analysis by automatically identifying and presenting the most relevant location attributes based on aggregated data from multiple sources and general relocation best practices. Before asking users for their preferences, the system pre-processes and ranks potential attributes, providing users with a structured framework that guides their responses and ensures comprehensive information collection.
Solution Approach 2:
The machine learning model acts as an intermediary between the system's comprehensive attribute database and the user's limited explicit preferences. It translates implicit user behaviors and limited inputs into comprehensive personalized recommendations by inferring unstated preferences and filling in gaps with data-driven insights from similar user patterns.
Data Source
AI summary
A system for assisting prospective movers in interactively finding locations compatible with their needs and interests. In one aspect, the system helps prospective movers discover conscious and subconscious needs and interests. In one aspect, the system helps prospective movers define the limits or boundaries of their needs and interests. In one aspect, the system helps prospective movers discover the limits or boundaries of their needs and interests. In one aspect, the system helps prospective movers discover locations matching or closely matching their needs and interests. In one aspect, the system allows a prospective mover to specify profiles containing their associated customized needs and interests. In one aspect, the system allows a prospective mover to specify profiles containing their associated customized favorite locations. In one aspect, the system allows a prospective mover to broadcast their customized needs and interests to others to obtain feedback. In one aspect, the system allows a prospective mover to broadcast their customized favorite locations to others to obtain feedback. In one aspect, the system ranks locations based on prospective mover-specified filter criteria. In one aspect, the system notifies a prospective mover about newly received information updates or services related to the prospective mover, their needs, interests, or favorite locations.