Missing-Field Search Ranking for Genealogy Database Expansion
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Solution Overview
Problem
Existing historical content systems produce shallow search results that do not consider how the results impact database expansion, leading to repetitive and inefficient searches, and require excessive user interactions to add data to content items.
Innovation Solution
A missing-field system that prioritizes content items with data fields missing from a search query, using statistical and machine-learning models to rank results based on new information and reduce redundant processing by identifying and surfacing content items with missing data fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems apply algorithms that consider only certain factors to identify relevant genealogical content items, then search results match fields of the search query, but the results are shallow and repetitive without considering database expansion
Solution Approach 1:
Instead of only considering how well content items match the search query fields, the patent inverts the approach by prioritizing content items that have data fields MISSING from the query. The system identifies which data fields are not present in the user's existing database and surfaces content items that contain those missing fields, thereby transforming the search from a matching exercise to a database expansion exercise.
Solution Approach 2:
The system incorporates feedback loops where search results are evaluated not just for immediate relevance but for their potential to expand the database. The algorithm learns from previous searches and user interactions to identify patterns of missing information and prioritizes content items that fill those gaps, creating a feedback-driven search system that continuously improves database comprehensiveness.
2Loss of information
If users perform repeated or serial searches to identify additional information for genealogy trees, then more information can be found, but redundant processing occurs consuming CPU cycles, memory access, and disk I/O
Solution Approach 1:
The system performs preliminary analysis of the user's existing database to identify missing data fields before the actual search executes. By pre-computing which fields are absent and what types of content items would fill those gaps, the system avoids redundant searches by directing the search algorithm specifically toward content items with the needed fields, thereby eliminating unnecessary CPU cycles, memory access, and disk I/O operations.
Solution Approach 2:
The patent changes the parameters of the search algorithm from traditional relevance-based sorting to missing-field-based prioritization. Instead of searching with fixed parameters and hoping to find useful information, the system dynamically adjusts search parameters based on the specific gaps in the user's database, transforming the search from a brute-force approach to a targeted operation that minimizes redundant processing.
3Adaptability or versatility
If existing systems search for content items specific to a user, then personalized results are provided, but the same data is traversed repeatedly wasting bandwidth
Solution Approach 1:
The system extracts and caches the profile of missing data fields for each user after the first search. Instead of re-traversing the entire database to identify what's missing, the system takes out and stores the missing field information separately, then uses this extracted profile to guide subsequent searches. This extraction approach eliminates repeated traversal of the same data while maintaining personalized results.
4Loss of information
If existing systems require multiple navigations across multiple interfaces to add a single fact, then data can be added to content items, but excessive user interactions are required
Solution Approach 1:
The patent merges the search results display with direct data addition functionality. Instead of requiring separate navigation to profiles and event types, the search results interface is combined with editing capabilities, allowing users to add missing data fields directly from the search results page. This merging of functions eliminates multiple navigations and reduces the number of user interactions required to add facts to content items.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for utilizing an improved search algorithm which prioritizes content items that include data fields (or other information) missing from a search query. For example, in response to a search query, the disclosed systems can prioritize or rank candidate content items to focus on candidate content items which include new information. Indeed, the disclosed systems can prioritize content items that include new information by ranking according to which content items include data fields missing from the search query. In some cases, the disclosed systems can prioritize content items with new information by determining which content items include data fields not already stored within a database associated with a user account (e.g., the user account performing the search) and/or for a particular entity or record within a genealogical database.