Client-Side Search Engine Using Random Location and Probability Tables
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Solution Overview
Problem
Conventional search engines face performance issues when applied to client-side knowledge bases due to server-side dependencies, processing delays, and inability to learn user preferences, leading to inefficient information retrieval and user experience.
Innovation Solution
A client-side search system that performs natural language-based searches by initiating a primary search at a random location in the knowledge base to find a match answer, determining a look-up association from search history, and using an alternative answer probability table to identify and perform a secondary search for an alternative answer, while updating tables based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If server-side search engines are used for client-side knowledge bases, then search functionality is provided, but processing delays and performance issues occur due to database or application server requests
Solution Approach 1:
The search engine functionality is extracted from the server-side environment and implemented directly in the client-side portable digital assistant. The knowledge base and search processing are localized to the client device, eliminating the need for continuous server communication and reducing processing delays associated with database or application server requests.
Solution Approach 2:
The portable digital assistant performs search operations independently using its own integrated knowledge base and search engine. The system serves itself by processing search queries locally without requiring external server support, thereby improving productivity and eliminating processing delays.
2Measurement precision
If conventional search engines with complex software abstraction layers are used, then search functionality is provided, but the ability to locate exact information requested by users is compromised
Solution Approach 1:
The patent removes complex software abstraction layers such as Berkeley search engine strategy and artificial intelligence algorithms from the search system. Instead, it uses a simplified approach with direct knowledge base indexing and probability-based search, reducing complexity while improving the ability to locate exact information requested by users.
Solution Approach 2:
The search system changes the underlying parameters of information retrieval by using cumulative probability values and direct knowledge base indexing rather than complex software abstraction layers. This parameter change enables more accurate location of exact information while simplifying the overall system architecture.
3Adaptability or versatility
If conventional search engines are used, then search functionality is provided, but the ability to learn from prior searches and provide user preferences is lacking
Solution Approach 1:
The search engine incorporates feedback mechanisms that learn from user search behavior and preferences. By analyzing prior searches and user interactions, the system updates its probability models to better predict and provide relevant information, thereby improving adaptability to user preferences while maintaining high search efficiency.
Data Source
AI summary
A system and method for searching a knowledge base for a match answer and an alternative answer. The method includes the steps of: inputting a search term; beginning a search at a random location in the knowledge base to identify the match answer; determining a match answer category from the match answer; determining a look-up association based on the match answer category and a search history; plugging the look-up association into an alternative answer probability table to identify an alternative answer category; and performing a secondary search at a second random location in the knowledge base to find the alternative answer that belongs to the alternative answer category.