Search Engine Using Time Series Data for Event Prediction
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Solution Overview
Problem
Current search engines provide only static or historical information that can be manipulated, lacking the ability to retrieve and analyze real-time data relevant to search queries, and fail to predict future events related to the query subject.
Innovation Solution
A system and method for an online search engine that generates search query results by retrieving and assembling time series data related to the query, determining driving factors, and using a neural network model to predict future values, displaying these factors and predictions to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search engines retrieve static or historical information from web pages, then the search results can be maintained and displayed, but the system cannot retrieve real-time data or predict future events related to the query subject
Solution Approach 1:
The patent transforms static search results into dynamic, real-time data streams. The system continuously retrieves and updates time series data from multiple sources, enabling the search engine to provide current information rather than historical snapshots. This dynamic approach allows the system to adapt to changing conditions and provide up-to-date predictions and insights.
Solution Approach 2:
The system performs preliminary data collection and processing by continuously monitoring and storing time series data from various sources before queries are made. This pre-processing enables rapid response to user queries and facilitates real-time analysis and prediction without requiring time-consuming data gathering during the query execution.
2Productivity
If search engines use crawling algorithms to retrieve information from web pages, then data can be indexed and searched, but the retrieved information can be manipulated by content managers to satisfy ranking criteria rather than reflecting actual relevance
Solution Approach 1:
The patent replaces traditional web crawling and indexing mechanisms with direct time series data retrieval from specialized sources. Instead of relying on web page content that can be manipulated, the system directly accesses structured time series data from reliable sources, eliminating the manipulation vulnerability while maintaining efficient retrieval speeds through optimized data querying techniques.
Solution Approach 2:
The system introduces time series data as an intermediary between the search query and the final results. This intermediary layer filters out manipulated or irrelevant content by focusing on objective, time-based data patterns, thereby improving measurement precision while maintaining productivity through efficient time series processing.
3Adaptability or versatility
If search engines provide static search results based on historical data, then the system structure remains simple, but the system cannot provide predictive capabilities or actionable insights for future events
Solution Approach 1:
The patent segments the data processing system into distinct modules: data retrieval from multiple sources, time series assembly, driving factor determination, and prediction generation. This segmentation allows each component to handle specific tasks efficiently, making the overall complex system more manageable and maintainable while enabling predictive capabilities through coordinated operation of these modular components.
Data Source
AI summary
A system, method, and computer program product provide a search engine which may use time series of data relevant to a search query to generate relevant results that may be used for predictions of events related to the query. Embodiments determine the underlying driving factors that most influenced the search query topic. In one aspect, the predictive ability of time series may be used to determine the driving factors. In another aspect, the system and method may employ a neural network to run a prediction model for determining conditional distribution of future values of the search query topic. The information may be displayed in a user interface where the driving factors and other information may be user edited to observe the effects source data has on the search query topic.