Neural Network Search System Handling Complex Keyword Combinations
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Solution Overview
Problem
Current search engines rely on Boolean algebra eigenvector algorithms, which are limited by their static nature and inability to handle complex keyword combinations, leading to hidden optimal results and inefficiencies in processing multiple related search requests, and are burdened by redundancy and exponential complexity as the internet environment grows.
Innovation Solution
The implementation of a neural network-based system, known as the Hive, that transforms interactive input into human knowledge search patterns using informatics set theory, reduces redundancy, and dynamically updates search patterns to stabilize and prioritize results, effectively managing the complexity of the internet environment by organizing information into a managerial hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Boolean algebra eigenvector algorithms are used for search engine processing, then search results can be generated, but the system suffers from static nature and inability to handle complex keyword combinations
Solution Approach 1:
The patent transforms the static Boolean algebra parameters into dynamic neural network parameters that can adapt to complex keyword combinations. The system changes from fixed algorithmic parameters to learnable weight parameters that evolve based on search patterns and user behavior, enabling handling of complex queries while managing computational complexity through distributed processing.
Solution Approach 2:
The patent replaces the mechanical Boolean algebra algorithmic system with a neural network system that uses distributed parallel processing. This substitution allows the system to handle complex keyword combinations through pattern recognition and associative memory mechanisms rather than rigid logical operations, improving adaptability while distributing computational complexity across multiple processing units.
2Productivity
If search engines process each request from ground up statically, then complete search results are obtained, but processing efficiency decreases when handling multiple related requests
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing search patterns, keywords, and potential query variations before they are actually searched. The neural network learns from historical search data and pre-computes relationships between related queries, so when multiple related requests come in, the system can leverage pre-established patterns and partial results rather than processing everything from scratch.
Solution Approach 2:
The patent merges multiple related search requests into unified processing operations. The neural network identifies semantic similarities and relationships between queries, combining their processing into single computational passes. This allows the system to handle multiple related searches simultaneously by sharing computational work and reusing intermediate results, significantly improving productivity while reducing time loss.
3Quantity of substance
If the internet environment grows exponentially, then more information is available, but redundancy and complexity burden increase
Solution Approach 1:
The patent extracts essential patterns and relationships from the exponentially growing internet information environment. The neural network system identifies and extracts meaningful search patterns, keyword associations, and query relationships while filtering out redundant information. This extraction process maintains information availability by preserving essential data while removing complexity through pattern abstraction and selective indexing.
Solution Approach 2:
The patent segments the overwhelming internet information environment into manageable neural network processing units and distributed computational tasks. By dividing the information processing into hierarchical layers and distributed nodes, the system can handle large quantities of information without proportionally increasing overall system complexity, as each segment processes only relevant local patterns.
4Measurement precision
If traditional search engines are used, then basic search functionality is provided, but optimal results are hidden due to narrow keyword combinations
Solution Approach 1:
The patent introduces dynamics into the search system by replacing static keyword matching with dynamic neural network pattern recognition. The system adapts keyword combinations dynamically based on learned relationships, user behavior patterns, and contextual information. This allows the system to maintain high measurement precision for search accuracy while exhibiting great adaptability to various keyword combinations and query formulations.
Solution Approach 2:
The patent introduces neural network pattern recognition as an intermediary between user queries and search results. This intermediary layer transforms narrow keyword combinations into expanded concept networks, bridging the gap between specific search terms and comprehensive results. The intermediary enriches queries by inferring related concepts and relationships, improving both accuracy and flexibility simultaneously.
Data Source
AI summary
A Codex system including a plurality of computers linked into a neural network. The Codex continuously scans and gathers information from, understands, and interacts with, an environment, the environment being an Internet comprising a multitude of websites. Processors of the computers operates in accordance with optimizer software, which executes a software instruction set, based on rules of grammar and semantics, to search a encyclopedia of human knowledge and utilizes the encyclopedia to transform input into a search pattern. Then, the Codex monetizes and commercializes each transformed input and corresponding optimal output. An artificial intelligence interaction software (referred to as Virtual Maestro) utilizes the search pattern and optimal output to interact and engage a scripted communication with an end user.