Contextual Content Extraction Using Classification and Context Models
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Solution Overview
Problem
The increasing volume of electronically stored information makes it challenging for users to meaningfully extract and transform contextually relevant data across different platforms, as existing systems lack efficient methods for identifying, extracting, and transforming relevant information.
Innovation Solution
A system that utilizes a classification model to calculate the relevance of electronically stored items to a search query, identifies items with a threshold probability, determines contextually relevant portions using a contextual model, and transforms these portions into a target platform, employing machine learning algorithms and a graphical user interface for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review and extract relevant information from vast amounts of electronically stored data, then extraction accuracy can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a classification model and a contextual model that acts as a mediator between the vast electronic data and the user. The classification model初步 filters data based on relevance to search queries, and the contextual model further refines extraction by understanding contextual relationships. This intermediary automated system resolves the contradiction by providing accurate extraction without requiring manual user review of every data item.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. Instead of users manually examining and extracting information from electronic data, the system uses machine learning models (classification model and contextual model) to automatically perform the extraction task. This substitution eliminates time consumption while maintaining extraction accuracy through intelligent algorithms.
2Quantity of substance
If existing systems attempt to process and transform all electronically stored information, then comprehensive data coverage is achieved, but system complexity and computational resources increase
Solution Approach 1:
The patent applies the extraction principle by selectively removing and processing only the relevant portions of electronically stored information rather than attempting to process all data. The classification model identifies and extracts items relevant to search queries, and the contextual model extracts only the contextually relevant portions from those items. This selective extraction approach achieves comprehensive coverage of relevant data while avoiding the complexity of processing entire datasets.
Solution Approach 2:
The patent segments the data processing task into distinct stages: first, the classification model segments data by identifying relevant items from the vast electronic storage; second, the contextual model segments further by extracting only the contextually relevant portions from those items. This multi-level segmentation reduces system complexity by breaking down the overwhelming task of processing all data into manageable, targeted processing steps.
3Productivity
If traditional search methods are used to find relevant information, then system simplicity is maintained, but information retrieval effectiveness decreases
Solution Approach 1:
The patent changes the parameters of information retrieval by moving beyond traditional keyword-based search to a multi-parameter approach. The classification model uses relevance parameters to score and rank items, while the contextual model incorporates contextual relationship parameters to determine the most relevant portions. This parameter transformation significantly improves information retrieval effectiveness by considering both explicit relevance and implicit contextual relationships.
Solution Approach 2:
The patent applies preliminary action by pre-processing electronically stored items through the classification model before the actual retrieval operation. The classification model pre-identifies and scores items based on their relevance to potential search queries, creating a pre-filtered set of candidates. This preliminary classification action improves retrieval effectiveness by ensuring that only potentially relevant items are subjected to more complex contextual analysis during the actual search operation.
Data Source
AI summary
Described herein is a system and method for transforming contextually relevant items. A search query is received, and, for each of a plurality of electronically stored items (e.g., stored in a source platform), a probability that the stored item is relevant to the search query is calculated using a classification model. Stored items having a calculated probability greater than or equal to a threshold probability are identified. Contextually relevant portions of the identified stored items is determined using a contextual model. The determined contextually relevant portions of the identified stored items are extracted. The extracted contextually relevant portions of the identified stored items are transformed into a target platform. An output of the extracted contextually relevant portions of the identified stored items in the target platform is provided.