Neural Network Annotation System for Contextual Search Results
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Solution Overview
Problem
Conventional search engines generate unsatisfactory annotations for search results, often requiring users to manipulate irrelevant parameters, leading to non-relevant results and overwhelming amounts of text, which distracts from the search query's context.
Innovation Solution
A neural network system that connects neurons associated with words, sentences, and documents, allowing for contextually relevant annotation generation based on user input, with an activity regulator to manage neuron excitement and relevance, enabling efficient and effective searching and annotation of documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search engines use percentage-based or fixed maximum sentence annotations, then users can control annotation size, but users must constantly manipulate irrelevant parameters instead of focusing on search substance
Solution Approach 1:
The system automatically adjusts annotation parameters based on document characteristics and user needs without requiring manual intervention. The neural network analyzes document structure, importance, and relevance to dynamically determine optimal annotation length and content, making the system self-regulating rather than user-regulated
Solution Approach 2:
The system dynamically changes annotation parameters (number of sentences, selection criteria) based on document properties and user context rather than using fixed user-defined parameters. This transforms static user-controlled parameters into dynamic system-optimized parameters that adapt to each document and user interaction
2Productivity
If conventional search engines annotate with sentences containing query words, then annotations are generated automatically, but the annotations may not be contextually relevant to the user's search intent
Solution Approach 1:
The system uses user interactions with annotations as feedback to refine and adjust annotation generation. When users indicate relevance or irrelevance of annotations, the system learns from this feedback and adjusts its neural network parameters to improve future annotation quality and contextual relevance
Solution Approach 2:
The system replaces conventional mechanical keyword-matching annotation methods with neural network-based semantic analysis. This substitution enables the system to understand document meaning and context rather than simply matching query words, significantly improving annotation relevance while maintaining automated generation
3Loss of information
If conventional search engines provide multiple sentences as annotations, then more document information is provided, but users are overwhelmed with excessive text that distracts from the search query context
Solution Approach 1:
The system extracts only the most essential and relevant sentences from documents for annotation, removing unnecessary information. The neural network identifies and extracts key sentences that capture document meaning while filtering out redundant or less important content, providing concise yet complete information
Solution Approach 2:
The system segments annotations into hierarchically organized groups based on document structure and relevance. Rather than presenting a flat list of sentences, the system divides annotations into thematic segments or priority levels, making the information more manageable and easier to navigate while preserving comprehensive document coverage
Data Source
AI summary
A system for generating annotations of a document, including a plurality of neurons connected as a neural network, the neurons being associated with words, sentences and documents. An activity regulator regulates a minimum and/or maximum number of neurons of the neural network that are excited at any given time. The neurons are displayed to a user and identify the neurons that correspond to sentences containing a predetermined percentage of document meaning. The annotations can be also based on a context of the user's search query. The query can include keywords, documents considered relevant by the user, or both. Positions of the neurons relative to each other can be changed on a display device, based on input from the user, with the change in position of one neuron changing the resulting annotations. The input from the user can also include changing a relevance of neurons relative to each other, or indicating relevance or irrelevance of a document or sentence.