Graphical Popularity Trend Analysis with Top-Moving Query Identification
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Solution Overview
Problem
Existing methods for analyzing search data are ineffective in detecting trends and managing search results to extract meaningful information about the popularity of entities over time, failing to adequately evaluate and explain adjustments in popularity.
Innovation Solution
A method that identifies candidate points of interest on a graphical depiction of an entity's popularity over time by analyzing peak points, valley points, and slope values, and surfaces top-moving queries that contributed to these points, providing insights into user search behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing methods for retrieving and storing search data are used, then data storage is convenient and data is available for access, but the methods are ineffective for detecting trends and extracting meaningful information about entity popularity
Solution Approach 1:
The patent segments search queries into distinct categories (popular queries, trending queries, rising queries) based on their temporal patterns and popularity metrics. This segmentation allows the system to extract meaningful information about different aspects of entity popularity separately, addressing the inability of existing methods to differentiate between various types of search behavior trends.
Solution Approach 2:
The patent performs preliminary analysis of search query data to identify popular, trending, and rising queries before presenting them to users. By pre-processing the data to categorize queries based on their temporal patterns and popularity changes, the system prepares meaningful information in advance, making trend detection effective without requiring complex real-time analysis when users access the data.
2Measurement precision
If existing search data management techniques are used, then data is stored and accessible, but the techniques do not offer sufficient evaluation to identify and explain adjustments of entity popularity
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors search query patterns and provides explanations for popularity adjustments. By analyzing temporal patterns in search data and comparing current queries with historical data, the system generates feedback that explains why entity popularity is increasing or decreasing, thereby maintaining both precise measurement and explanatory information.
Solution Approach 2:
The system performs preliminary evaluation of search data to identify popular, trending, and rising queries before users access the information. This pre-processing includes calculating popularity metrics, detecting temporal patterns, and categorizing queries, which enables precise measurement and preservation of explanation information without requiring complex real-time computation.
3Loss of information
If all user-submitted queries are processed and analyzed, then complete search data is available, but the complexity of processing and managing the data increases significantly
Solution Approach 1:
The patent extracts only the most relevant features from the complete set of user-submitted queries, specifically focusing on temporal patterns, popularity metrics, and categorical characteristics. By taking out and analyzing only these essential features rather than processing every detail of all queries, the system maintains completeness of search data while significantly reducing processing complexity.
Solution Approach 2:
The system segments the complete set of search queries into distinct categories (popular, trending, rising) based on key characteristics. This segmentation allows the system to process and manage data more efficiently by handling categorized groups separately, reducing overall complexity while maintaining the completeness and usefulness of the entire dataset.
Data Source
AI summary
Computer-readable media and computerized methods for identifying candidate points on a graphical depiction of relative popularity of an entity (e.g., entertainer, sports team, and the like) are provided. Points on the graphical depiction are ranked based on a number of user-submitted web queries that reference the entity that are received during a particular time frame. Peak points and slope values (i.e., derived from an angle of inclination of inclines on the graphical depiction) may be captured by analyzing movements in the rank of an entity over time. An algorithmic process may then be applied to the peak points and slope values to determine points of interest of the entity's popularity, such as the highest-ranked periods and/or dramatic positive movements in rank of the entity. These points of interest are selected as candidate points and are surfaced as icons on a visual representation of the graphical depiction.