Home Score Generation Using Machine Learning Data Evaluation

The patent introduces a machine learning model as an intermediary between raw property data and potential homeowners. The model processes and anonymizes data, generating a home score that conveys essential information without exposing sensitive details. This intermediary layer simultaneously improves security/privacy by hiding raw data and maintains efficiency by providing processed insights directly.

The patent transforms multiple raw property parameters into a single aggregated home score parameter. The machine learning model takes various input features (location, amenities, property characteristics) and converts them into one comprehensive metric that captures essential information. This parameter transformation reduces information loss while keeping the system relatively simple by providing a unified score rather than requiring complex multi-parameter analysis by users.

The patent performs preliminary actions by pre-training the machine learning model on extensive property data before actual home score generation. The model learns patterns and relationships during offline training, enabling it to quickly generate accurate scores during online inference. This preliminary training separates the computationally intensive learning phase from the time-sensitive scoring phase, achieving both accuracy and efficiency.