Neural Network Search Visualization for Context Management
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Solution Overview
Problem
Conventional search engines face challenges in providing users with relevant search results due to the 'tyranny of the majority' problem, where non-typical contexts of search queries are overshadowed, and users lack an intuitive way to specify queries, especially for complex searches that cannot be easily represented using simple Boolean operations.
Innovation Solution
A context-based search visualization system using neural networks that associates neurons with words, documents, and objects, allowing users to interactively adjust the relevance of search results through a graphical interface, such as a two-dimensional or three-dimensional visualization, to prioritize relevant documents and exclude irrelevant ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional search engines use standard ranking systems to return search results, then the most frequently used documents are shown at the top, but non-typical contexts of the query are overshadowed and relevant documents may appear far down the list
Solution Approach 1:
The patent segments search results into multiple contextual groups or clusters rather than presenting a single flat ranked list. Documents are organized into different context-based segments, allowing users to explore multiple relevant contexts simultaneously. This segmentation prevents the dominance of a single majority context and surfaces documents from various relevant contexts.
Solution Approach 2:
The patent introduces additional dimensions for organizing search results beyond simple ranking position. Results are displayed in multi-dimensional space where documents can be positioned based on multiple contextual factors, allowing users to navigate through different contextual perspectives. This dimensional approach enables simultaneous presentation of diverse relevant contexts without one overwhelming the others.
2Adaptability or versatility
If users want to specify complex search queries with multiple contexts and constraints, then query languages with logical operations can be used, but the language requires special knowledge and is practically inaccessible for most users
Solution Approach 1:
The patent enables users to build complex queries through intuitive graphical interactions rather than requiring knowledge of query languages. Users can visually select documents, drag them into contextual groups, and define relationships through simple actions. The system automatically translates these visual operations into complex search logic, allowing users to perform advanced querying without learning specialized syntax or operations.
Solution Approach 2:
The patent replaces the mechanical system of typing and parsing query language syntax with a graphical user interface based system. Instead of requiring users to manually construct Boolean expressions and logical operations, the system provides visual tools for defining query constraints and relationships. This substitution maintains complex query capabilities while eliminating the barrier of syntax knowledge.
3Measurement precision
If users manually read annotations to decide whether a document is sought, then they can identify relevant documents, but this requires thinking hard and time-consuming effort
Solution Approach 1:
The patent uses visual indicators such as color coding, highlighting, and graphical markers to immediately convey document relevance and contextual information. Instead of requiring users to read and analyze text annotations, the system visually encodes relevance metrics, contextual relationships, and document attributes through color and graphical elements. This allows users to quickly assess document relevance at a glance without intensive reading and thinking.
Data Source
AI summary
A system, method and computer program product for visualization of context-based search results, including a plurality of neurons, the neurons being associated with words and documents; a plurality of connections between the neurons; a map that displays at least some of the neurons to a user, wherein the display of the neurons on the map corresponds to their relevance to a search query; a display of the links to the relevant documents; and means for changing positions of the neurons relative to each other based on input from the user. Changing a position of one neuron relative to other neurons also changes positions of other contextually relevant neurons, and displays different relevant documents. The map displays the neurons with their relevance identified by any of font type, color, transparency and font size. The map includes icons in proximity to the displayed word neurons for identifying those neurons as irrelevant. Links to the documents are obtained from a search engine having an input query. The map displays annotations and/or keywords to the documents next to the displayed documents.