Cognitive Hierarchical Content Distribution via Atomic Unit Segmentation
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently identifying and understanding dynamic structures and changes within hierarchical structures, making it difficult to effectively process and respond to cognitive content.
Innovation Solution
A system that partitions file content into atomic units and maps them to a classification model, identifying permissive characteristics to create an amended version of the file, which includes only atomic units with the identified characteristics, allowing for dynamic processing and adaptation to changes in the classification model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing systems use traditional methods to process content in hierarchical structures, then they can maintain system simplicity, but they fail to efficiently identify and adapt to dynamic changes in the structure
Solution Approach 1:
The patent segments file content into atomic units that can be independently processed and mapped to classification model nodes. This segmentation enables the system to efficiently identify and adapt to changes in hierarchical structures by operating on discrete, manageable units rather than treating the entire content as a monolithic structure.
Solution Approach 2:
The patent implements dynamic processing by enabling the system to detect changes in the classification model hierarchy and automatically reprocess affected atomic units. The system dynamically adapts to structural changes by identifying modified nodes and updating only the relevant content portions, rather than reprocessing the entire file.
2Measurement precision
If the system processes all atomic units through the complete classification model, then comprehensive classification is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the classification process into segments corresponding to atomic units and their associated classification model nodes. This allows the system to process only the specific atomic units affected by changes in the hierarchical structure, rather than reprocessing the entire file, thereby maintaining classification accuracy while reducing processing time.
Solution Approach 2:
The patent performs preliminary mapping of atomic units to classification model nodes before actual classification processing. This preliminary action establishes a structure that enables efficient change detection and selective reprocessing, allowing the system to quickly identify which atomic units need classification updates when the hierarchy changes.
3Ease of operation
If the system maintains a complete mapping of all atomic units to the classification model, then comprehensive content control is achieved, but memory requirements and system complexity increase
Solution Approach 1:
The patent maintains mappings at the atomic unit level rather than processing entire files as single units. This segmentation allows the system to track and control only the specific atomic units that are affected by changes in the classification model, reducing the overall complexity of maintaining mappings while preserving comprehensive content distribution control.
Data Source
AI summary
Embodiments relate to a system, program product, and method for use with an intelligent computer platform and cognitive processing and associated distribution. The embodiments support a mechanism for dynamically sharing critical and non-critical information responsive to a classification model, such that only relevant information or part of the information is shared. As the classification model is subject to modification, the dynamic sharing mechanism is dynamically updated to reflect such modification. Similarly, as an associated document or file is subject to modification, dynamic processing of the document or file takes place responsive to the classification model. The dynamic classification and document processing employ NLP and ML models to support the associated functionality.