Personalized Provider Search Using Precomputed Member-Provider Vectors

The system pre-computes member vectors and provider vectors before the actual search query is executed. Member vectors are generated by encoding member characteristics (demographics, health conditions, preferences) and provider vectors are generated by encoding provider characteristics (specialty, location, cost, quality metrics). This preliminary encoding allows the search to quickly compute relevancy scores without processing raw data during the query phase, thus maintaining speed while enabling personalized accuracy.

The patent introduces vectors as an intermediary representation between raw member/provider data and the search ranking process. Instead of directly comparing raw characteristics during search, the system uses pre-computed vectors that capture essential features in a condensed format. This intermediary layer enables efficient computation of personalized relevancy scores while maintaining the ability to incorporate granular member and provider information.

The system performs complex computational work in advance by pre-computing member vectors and provider vectors that encode granular member characteristics, provider characteristics, and interaction history. This preliminary encoding transforms complex raw data into condensed vector representations, reducing the computational burden during actual search operations while preserving personalization accuracy.

The patent transforms complex multi-dimensional member and provider data into vector space representations with specific dimensionalities (e.g., 50-dimensional vectors). This parameter transformation allows the system to work with condensed numerical representations rather than raw complex data, enabling efficient computation of personalized relevancy scores while capturing nuanced information about member-provider compatibility.