Predictive Search Engine Trend Detection Across Multiple Time Periods
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Solution Overview
Problem
Existing search engines fail to accurately predict short-term trending search queries due to reliance on long-term popularity analysis, missing new product launches and seasonal trends.
Innovation Solution
A system that identifies trending search queries by generating user engagement metrics over different time periods and comparing them to a threshold to determine significance, displaying these queries in real-time or near-real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search engine analyzes long-term popularity of search queries to generate typeahead predictions, then the prediction accuracy for stable queries is improved, but the ability to identify short-term trending queries deteriorates
Solution Approach 1:
The patent segments the analysis into multiple time periods (e.g., recent time period vs. extended time period) to separately evaluate long-term stability and short-term trends of search queries. This allows the system to maintain accurate predictions for stable queries while simultaneously detecting emerging trends that occur in specific time windows.
Solution Approach 2:
The system dynamically adjusts its prediction approach by evaluating queries across different time horizons. It transitions from static long-term analysis to dynamic multi-period analysis, enabling the system to adapt to both stable patterns and emerging trends based on the specific query characteristics.
2Stability of the object's composition
If the search engine relies on extended time period data for predictions, then the stability of predictions is improved, but the responsiveness to new products and seasonal trends deteriorates
Solution Approach 1:
The system performs preliminary analysis on recent time period data to identify emerging trends before they become dominant in the extended time period data. This allows the system to prepare predictions for new products and seasonal trends in advance, improving response speed while maintaining stability through subsequent validation against longer-term patterns.
Solution Approach 2:
The recent time period data acts as an intermediary between real-time query input and long-term popularity data. It mediates the detection of emerging trends by providing a buffer zone that captures short-term patterns without the noise and delays inherent in analyzing only the most recent data points.
3Quantity of substance
If the search engine uses historical search query data from the previous year, then the coverage of common queries is improved, but the identification of emerging popular queries deteriorates
Solution Approach 1:
The system applies partial analysis to different time periods for different query types. For established queries with long-term stability, it relies more on extended time period data for comprehensive coverage. For emerging queries showing recent popularity spikes, it applies excessive focus on recent time period data to ensure accurate identification, even if this means less overall coverage in the short term.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform functions relating to detecting trending search queries that are popular in a short-term time period. User engagement metrics are derived from historical search engine data. A trending analysis function executes statistical analyses on the user engagement to identify the trending search queries. Other embodiments are disclosed herein.