Semantic Classification Engine for Cloud Document Storage
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Solution Overview
Problem
Cloud-based document repositories face challenges in efficiently classifying and retrieving files due to reliance on keyword-based searches that fail to identify concepts, leading to laborious manual searches and inefficient file organization.
Innovation Solution
Integration of natural language processing (NLP) techniques within a semantic classification engine that analyzes textual content to identify concepts and assign relevant tags, allowing for accurate classification and retrieval of files based on conceptual categories, even if the exact keyword is not used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search is used for file retrieval, then implementation is simple, but retrieval accuracy is poor because it cannot identify concepts
Solution Approach 1:
The patent introduces a semantic classification engine as an intermediary between the file storage system and the search function. This engine automatically analyzes document content, extracts key concepts, and assigns semantic tags during the storage process. When users search, they can use conceptual terms rather than exact keywords, and the system retrieves files based on these semantic tags, thereby improving retrieval accuracy without requiring complex user-side processing
Solution Approach 2:
The patent performs content analysis and concept extraction in advance during the file storage process, before retrieval is needed. The semantic classification engine pre-processes documents by identifying key concepts and assigning tags at storage time. This preliminary action ensures that when retrieval occurs, the system already has organized semantic information ready, eliminating the need for complex real-time analysis during search operations
2Measurement precision
If manual file organization is used, then classification accuracy can be high, but user effort and time consumption increase significantly
Solution Approach 1:
The patent implements an automatic semantic classification system that performs file organization without requiring user intervention. The classification engine autonomously analyzes document content, identifies key concepts, and assigns appropriate tags and categories. This self-service approach maintains high classification accuracy through sophisticated NLP techniques while completely eliminating the time and effort users would otherwise spend on manual file organization
Solution Approach 2:
The patent replaces the mechanical manual process of file organization with an automated computational system. Instead of users manually reading, understanding, and categorizing files, the system uses natural language processing and semantic analysis algorithms to automatically classify documents. This substitution maintains or improves classification accuracy while dramatically reducing time consumption by eliminating human labor from the process
3Adaptability or versatility
If full-text keyword search is implemented, then all files can be searched, but concept identification capability is lacking
Solution Approach 1:
The patent adds a semantic dimension to the traditional keyword search space. Instead of operating solely in the keyword matching dimension, the system creates a parallel semantic tagging dimension through concept extraction. Users can search using conceptual terms that map to these semantic tags, enabling searches that transcend exact keyword matches while maintaining the comprehensiveness of full-text search capabilities
Data Source
AI summary
Techniques are disclosed for efficiently and automatically classifying textual documents or files. In some embodiments, the classification process is integrated into or otherwise made part of the storage function, such that when the user initiates a save process for a given file, the file is processed through a classifier prior to (or contemporaneously with) completing the save function. In some such embodiments, textual content of the file is analyzed using natural language processing to identify a main or substantial concept discussed in the file, and one or more corresponding tags are then assigned to that file. Subsequently, the user can access that file based on the one or more tags, for instance, through a user interface that allows the user to select one or more content categories associated with the assigned tags. The files can be text-based, but may include other content as well, such as images, video, and audio.