Semantic Maps for Text Data Analysis
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Solution Overview
Problem
Current search engines and data analysis systems fail to effectively convey the meaning of documents and data to users, requiring manual review and analysis, and existing methods like tag clouds do not allow users to derive meaningful insights from search results.
Innovation Solution
A system and method that utilize a mapping engine with text mining tools for semantics analysis to create maps of values, categories, and core trends, assigning scores to records and associating them with these concepts, and presenting results through a user interface for comprehensive understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return relevant documents based on search terms, then the quantity of information provided to users is improved, but the ability for users to understand and derive meaning from the information deteriorates
Solution Approach 1:
The patent introduces semantic maps as an intermediary layer between raw search documents and user understanding. These maps extract and represent core meanings, values, categories, and trends from documents, serving as a mediator that translates large quantities of unstructured information into comprehensible semantic structures that users can actually understand and derive insights from.
2Measurement precision
If users manually review and analyze each document to understand meaning, then the depth of understanding is improved, but the time required for analysis deteriorates
Solution Approach 1:
The patent performs preliminary semantic analysis by automatically creating semantic maps that extract values, categories, core trends, and concepts from documents before users need to review them. This preliminary action of meaning extraction and organization eliminates the need for users to manually analyze each document, providing deep understanding instantly while eliminating the time cost of manual review.
3Shape
If tag clouds are used to categorize documents, then visual representation of data is improved, but the ability for users to derive meaningful insights deteriorates
Solution Approach 1:
The patent transforms the visual representation from simple word frequency-based tag clouds to semantic maps that incorporate multiple parameters including values, categories, core trends, and conceptual relationships. This parameter enrichment changes the visual representation from superficial word displays to meaningful semantic structures that actually convey insights and relationships, eliminating the loss of meaning while maintaining visual accessibility.
Data Source
AI summary
Systems and methods for analyzing a plurality of data records to provide a comprehensive understanding of the data. For example, one or more public or private databases may be searched based on a user's search term(s). The results from the search may be analyzed to determine values, categories, core trends, concepts, and/or clusters present within the search results. The search results may be grouped or organized based on the values, categories, core trends, clusters, and/or concepts, and may be presented to a user via a user interface. Additionally, systems and methods of the present disclosure relate to analyzing public or private company data, and tracking or monitoring the data over time to provide real time analysis. For example, customer reviews, complaints, social media posts, or other data related to a company or product may be analyzed to determine values, categories, core trends, concepts, and/or clusters.