Adaptive Data System with Semantic Preprocessing and Symbolic Compression
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Solution Overview
Problem
Traditional data systems struggle with optimizing resources in large datasets, leading to delays in decision-making and insights extraction, especially in big data environments, and lack adaptability to dynamic data landscapes.
Innovation Solution
An adaptive data system (ADS) that processes cognitive data using an adaptive semantic preprocessor, trigger detector, temporal batching engine, symbolic encoder, and dynamic cognitive transformer engine to simulate, standardize, and derive insights from high-dimensional cognitive data, enabling low-frequency and low-compute processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data systems process large datasets, then data processing capability is provided, but delays in decision-making and insights extraction occur
Solution Approach 1:
The patent segments the data processing workflow into distinct cognitive stages (perception, comprehension, reasoning, decision-making) handled by specialized cognitive data processing units. This segmentation allows parallel processing of different data aspects, reducing overall processing time and eliminating bottlenecks present in traditional sequential systems.
Solution Approach 2:
The system implements dynamic adaptive processing where cognitive data structures evolve and reconfigure based on the specific analytical task. Processing pathways are dynamically adjusted based on data characteristics and query requirements, enabling faster response times compared to rigid traditional architectures.
2Quantity of substance
If traditional data systems manage large datasets, then data storage capacity is achieved, but computational cost and network cost increase
Solution Approach 1:
The patent extracts only the essential cognitive features and semantic attributes from vast datasets, transforming raw data into compressed cognitive representations. This extraction reduces the volume of data requiring active processing while preserving the critical information needed for decision-making, thereby lowering computational costs.
Solution Approach 2:
The system changes the parameter representation of data from traditional structured formats to cognitive data structures that encode semantic meaning and relationships. This parameter transformation enables more efficient processing by operating on meaningful abstractions rather than raw data, reducing computational overhead.
3Stability of the object's composition
If traditional data systems use rigid mappings, then infrastructure stability is maintained, but adaptability to dynamic data landscapes is reduced
Solution Approach 1:
The patent implements dynamic cognitive schemas that automatically adapt to new data patterns and relationships while maintaining core system stability. The cognitive data structures evolve through learned relationships rather than rigid predefined mappings, allowing the system to remain stable yet highly adaptable to changing data landscapes.
Solution Approach 2:
The cognitive data processing infrastructure provides universal processing capabilities that handle diverse data types and query patterns through a unified cognitive framework. This multi-functionality allows the system to maintain stable core operations while adapting to various data scenarios without requiring infrastructure changes.
4Measurement precision
If cognitive data processing is implemented, then contextual and semantic analysis is improved, but processing delay persists
Solution Approach 1:
The patent performs preliminary cognitive processing by pre-computing semantic relationships, contextual associations, and pattern recognition results during data ingestion. This preliminary action prepares cognitive data structures in advance, enabling rapid retrieval and analysis without sacrificing semantic accuracy during actual query processing.
Data Source
AI summary
An adaptive data system (ADS) for cognitive data processing is disclosed. The ADS includes an adaptive semantic preprocessor, a trigger detector, a temporal batching engine, a symbolic encoder, and a dynamic cognitive transformer engine. The adaptive semantic preprocessor is configured to receive input data from one or more databases and identify cognitive data attributes comprising one or more contextual, semantic, and temporal attributes from the received input data. The trigger detector is configured to identify semantic divergence of the identified cognitive data attributes and provide a standardized data. The temporal batching engine is configured to provide a high-dimensional cognitive data from the standardized data. The symbolic encoder compresses the high-dimensional cognitive data. The dynamic cognitive transformer engine is configured to determine decision making rules, analyze the compressed high-dimensional cognitive data based on the decision making rules and provide recommendations based on an outcome of the analysis to a user.