Cognitive Inference System for Travel Data Processing
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and extracting insights from large volumes of big data, particularly 'dark data,' which includes neglected or underutilized information, due to difficulties in capture, curation, storage, search, sharing, and analysis.
Innovation Solution
A cognitive information processing system that incorporates multiple processes such as semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, summarization, and entity resolution to iteratively improve insights over time, utilizing a cognitive inference and learning system (CILS) that processes data from various sources to provide cognitively processed travel relevant insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but the processing efficiency is insufficient and cannot handle large volumes of data within tolerable time intervals
Solution Approach 1:
The patent segments the monolithic data processing system into a distributed architecture comprising multiple worker nodes, master nodes, and specialized processing units. Each node handles specific data processing tasks independently, enabling parallel processing of big data while maintaining manageable complexity through modular design. The segmentation allows the system to scale horizontally by adding more worker nodes without proportionally increasing overall system complexity.
Solution Approach 2:
The patent transitions from traditional single-dimension sequential processing to multi-dimensional parallel processing by introducing distributed computing across multiple nodes and layers. The system operates across computational, network, and storage dimensions simultaneously, enabling volumetric data processing that dramatically improves throughput while distributing complexity across spatial and organizational dimensions.
2Loss of information
If comprehensive data collection is performed to capture all available information including dark data, then more insights can be extracted, but the challenges of capture, curation, storage, search, sharing, and analysis increase significantly
Solution Approach 1:
The patent introduces intermediary components including data curators, processing pipelines, and transformation layers that mediate between raw data collection and final analysis. These intermediaries standardize data formats, validate data quality, and transform unstructured dark data into structured formats, reducing management complexity while preserving information completeness through systematic data stewardship.
Solution Approach 2:
The patent replaces manual mechanical data management processes with automated computational systems including machine learning algorithms, natural language processing, and intelligent routing mechanisms. These systems automatically categorize, tag, and route data without human intervention, dramatically reducing the complexity of capturing and managing comprehensive data sets while maintaining high information completeness.
3Loss of information
If cognitive inference and learning operations are implemented to extract difficult-to-discover patterns, then valuable insights can be uncovered, but the processing time and computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing data through cleaning, normalization, and feature extraction before main analysis. Machine learning models are pre-trained on historical data, and data pipelines are pre-configured with optimization rules. This preliminary preparation reduces the computational burden during actual insight generation, enabling faster processing while maintaining high insight quality through proactive data preparation.
Solution Approach 2:
The patent establishes continuous learning operations where the system continuously processes data streams, updates models in real-time, and refines insights without interruption. This continuous action eliminates batch processing delays and enables the system to maintain up-to-date insights with minimal latency, balancing insight quality with processing speed through uninterrupted analytical operations.
Data Source
AI summary
A cognitive information processing system environment comprising: a plurality of data sources, at least some of the plurality of data sources comprising travel relevant data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed travel relevant insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed travel relevant insights over time; and, a destination, the destination receiving the cognitively processed travel relevant insights.