Hierarchical Neural Network for Contextual Search
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Solution Overview
Problem
Conventional search engines face challenges in delivering contextually relevant results due to the ambiguity of search terms and the computational intensity of applying universal neural networks to large document sets, leading to inefficient and often irrelevant search outcomes.
Innovation Solution
A neural network system with a specific topology that includes layers for words, objects, sentences, and documents, where each neuron is connected to a subset of others, allowing for efficient context-based searching by adjusting connection weights based on user input and relevance feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines use traditional keyword matching algorithms, then the search process is computationally efficient, but the search results lack contextual relevance and suffer from the tyranny of the majority problem
Solution Approach 1:
The patent segments the search problem into multiple hierarchical levels: keyword level, sentence level, and document level. Each level is processed independently with specialized algorithms, allowing contextual analysis without requiring full neural network computation for every search query. This segmentation enables the system to capture contextual relevance while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary processing by pre-computing contextual relationships and building hierarchical representations of documents during an indexing phase. This preliminary action stores contextual information in advance, so that during actual search operations, the system can quickly retrieve and compare pre-processed contextual data rather than performing intensive computations in real-time.
2Reliability
If a universal neural network is applied to all documents in the corpus, then contextual relevance can be captured, but the computational complexity and processing time become prohibitive for large datasets
Solution Approach 1:
The patent divides the document corpus into hierarchical segments (sentences within documents, keywords within sentences) and applies different levels of neural network analysis to each segment. This segmentation allows the system to capture contextual relationships at multiple levels without requiring a single universal neural network to process every document in its entirety, thereby reducing overall computational complexity.
Solution Approach 2:
The patent applies different levels of contextual analysis to different parts of the document corpus based on their relevance and size. More computationally intensive neural network analysis is applied selectively to critical segments, while less intensive methods are used for other portions. This local quality approach ensures high contextual accuracy where needed while maintaining computational feasibility overall.
3Productivity
If search engines rely on objective measures such as link structure and document contents, then the ranking algorithm is computationally manageable, but the results fail to account for subjective user interests, knowledge, and attitudes
Solution Approach 1:
The patent incorporates feedback mechanisms that allow the system to learn from user interactions with search results. By analyzing which results users select and how they engage with them, the system adjusts its contextual analysis to better align with individual user preferences, interests, and knowledge levels. This feedback loop enables the system to progressively improve user-specific relevance while building upon the efficient objective measures already in place.
Data Source
AI summary
A system for information searching includes a first layer and a second layer. The first layer includes a first plurality of neurons each associated with a word and with a first set of dynamic connections to at least some of the first plurality of neurons. The second layer include a second plurality of neurons each associated with a document and with a second set of dynamic connections to at least some of the first plurality of neurons. The first set of dynamic connections and the second set of dynamic connections can be configured such that a query of at least one neuron of the first plurality of neurons excites at least one neuron of the second plurality of neurons. The excited at least one neuron of the second plurality of neurons can be contextually related to the queried at least one neuron of the first plurality of neurons.