Query Analysis System for Time-Independent Search Forecasting
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Solution Overview
Problem
Advertisers face challenges in accurately modeling the frequency of search queries to optimize their advertising strategies, as existing methods struggle to predict time-dependent and time-independent query trends effectively.
Innovation Solution
A query analysis system that determines whether a query is time-dependent or time-independent, forecasting frequencies based on periodicities for time-dependent queries and causal relationships for time-independent queries, using techniques such as frequency spectral analysis and causal score calculations to generate accurate prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query frequency modeling uses traditional time series analysis methods, then time-dependent queries can be predicted, but time-independent queries cannot be accurately forecasted
Solution Approach 1:
The patent segments the query analysis process into two distinct pathways: one for time-dependent queries using periodicity analysis, and another for time-independent queries using causal relationship analysis. This segmentation allows each query type to be handled with the most appropriate method, improving overall prediction accuracy while maintaining versatility across different query types.
2Measurement precision
If the system analyzes all queries using periodicity-based methods, then time-dependent patterns are captured, but computational complexity increases
Solution Approach 1:
The system dynamically adapts its analysis approach based on the characteristics of each query. By determining whether a query exhibits time-dependent or time-independent behavior, the system selectively applies periodicity-based analysis or causal relationship analysis, thereby reducing unnecessary computational complexity while maintaining high detection precision for different query types.
3Productivity
If advertisers increase bid amounts for predicted high-frequency queries, then advertising revenue increases, but risk of wasted spending on inaccurate predictions increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors actual query frequencies against predicted frequencies, and uses this information to refine future predictions. This feedback loop increases prediction reliability over time, allowing advertisers to confidently increase bid amounts for high-frequency queries while minimizing the risk of wasted spending on inaccurate predictions.
Data Source
AI summary
Techniques for analyzing and modeling the frequency of queries are provided by a query analysis system. A query analysis system analyzes frequencies of a query over time to determine whether the query is time-dependent or time-independent. The query analysis system forecasts the frequency of time-dependent queries based on their periodicities. The query analysis system forecasts the frequency of time-independent queries based on causal relationships with other queries. To forecast the frequency of time-independent queries, the query analysis system analyzes the frequency of a query over time to identify significant increases in the frequency, which are referred to as “query events” or “events.” The query analysis system forecasts frequencies of time-independent queries based on queries with events that tend to causally precede events of the query to be forecasted.