Knowledge Pattern Machine for Predictive Analytics
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Solution Overview
Problem
Traditional search engines are limited in recognizing knowledge patterns and performing intelligent predictive data analytics, failing to utilize prior searches and dynamically update results based on changing datasets, leading to a stateless querying experience.
Innovation Solution
A knowledge pattern machine that integrates artificial intelligence to analyze user queries, historical data, and supplemental information, generating predictive insights and reports by identifying correlations and dynamically organizing data patterns, and updating them in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search engines are used for data querying, then the system simplicity is maintained, but the ability to recognize knowledge patterns and perform predictive analytics is lost
Solution Approach 1:
The patent introduces a knowledge pattern machine as an intermediary system between traditional search engines and data sources. This mediator component (comprising pattern recognition engines, data aggregation modules, and predictive analytics processors) enables sophisticated knowledge pattern recognition and predictive analytics without requiring the entire search engine system to become complex. The intermediary handles the complex pattern recognition tasks while traditional search components remain relatively simple.
2Measurement precision
If manual research steps are followed to answer predictive questions, then the research process is systematic and thorough, but the time and effort required are excessive
Solution Approach 1:
The knowledge pattern machine performs preliminary actions by continuously pre-processing and analyzing data from multiple sources in the background before queries are submitted. It pre-identifies knowledge patterns, pre-aggregates relevant data, and pre-computes predictive insights. When a user submits a query, the system quickly retrieves and combines pre-computed results rather than performing all analysis from scratch, dramatically reducing research time while maintaining accuracy.
Solution Approach 2:
The system maintains continuous useful action by constantly monitoring data sources, continuously updating knowledge patterns, and perpetually refining predictive models. This ongoing background processing ensures that when predictive questions are asked, the system already has current, relevant insights ready, eliminating the need for repetitive manual research steps while maintaining high accuracy.
3Adaptability or versatility
If traditional search engines process queries independently, then the querying process is simple and fast, but the ability to utilize prior searches and provide stateful results is lost
Solution Approach 1:
The knowledge pattern machine implements feedback mechanisms where results from previous queries and analyses are fed back into the system to refine future pattern recognition and predictive analytics. The system maintains state by storing and utilizing knowledge patterns discovered in prior searches, allowing subsequent queries to build upon previous insights. This feedback loop enables stateful querying where the system remembers and adapts based on historical interaction data.
Data Source
AI summary
The disclosure below describes a knowledge pattern machine that goes beyond and is distinct from a traditional search engine as simple information aggregator. Rather than acting as a search engine of the data itself, the knowledge pattern machine use variously layers of artificial intelligence to discover correlations within the queries and historical data, and to derive and recognize data patterns based on user queries for predictively generating new knowledge items or reports that are of interest to the user. Previous patterns and knowledge items or reports are accumulated and incorporated in identification of new data patterns and new predictive knowledge items or reports in response to future user queries, thus providing a stateful machine. The predictive knowledge items are updated in real-time without user interference as the underlying data sources evolve overtime. The data patterns and knowledge items are organized hierarchically and dynamically and may be shared among different users at various levels. This disclosure thus provides a pattern recognition machine with predictive analytics for enabling users to conduct research and to obtain and share unique real-time predictive data report based on intelligently processing user input queries.