Travel Listing Data Consolidation via Attribute Parsing and Confidence Scoring
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Solution Overview
Problem
The difficulty in comparing travel listings across multiple websites arises from differing data structures, unique identifiers, and varying levels of detail, making it challenging for consumers to determine if listings describe the same inventory item and ensuring high-quality data is displayed.
Innovation Solution
A computer-implemented system that matches and consolidates data records from multiple sources by parsing attributes, creating confidence scores, and translating terms to identify matching records, ensuring only records describing the same listing are consolidated, and providing high-quality, unified information to consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data records from multiple sources are consolidated to provide comprehensive information, then the quantity and quality of information available to consumers is improved, but the difficulty in determining whether records describe the same listing increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between multiple data sources and consumers. This system receives data records from various sources, applies normalization techniques through intermediate processing layers (parsing, attribute identification, confidence scoring), and presents unified results to consumers. The intermediary handles the complexity of record matching internally while presenting simplified information to end users.
Solution Approach 2:
The patent implements feedback mechanisms through confidence scores that indicate the reliability of record matches. The system continuously evaluates and scores the likelihood that records from different sources describe the same listing, using this feedback to refine matching algorithms and improve accuracy over time. Consumer interactions and corrections also provide feedback loops for system improvement.
2Ease of operation
If data structures and identifiers are standardized across sources to ease comparison, then the ease of operation is improved, but the adaptability to handle varying data formats decreases
Solution Approach 1:
The patent dynamically changes parameters during data processing based on the source and format of incoming records. The system adjusts parsing rules, attribute weighting, and matching thresholds according to the specific data structure being processed. This allows the system to maintain standardized output formats while adapting to handle diverse input formats from different data sources.
Solution Approach 2:
The patent implements dynamic processing where the matching and normalization procedures adapt in real-time based on the characteristics of the data being processed. The system can switch between different matching strategies, adjust confidence thresholds, and modify attribute prioritization dynamically depending on the data source and record type, rather than applying rigid static rules.
3Loss of information
If all available information from different data sources is displayed to consumers, then the loss of information is reduced, but the complexity of the presentation increases
Solution Approach 1:
The patent extracts and separates essential matching attributes from comprehensive data records, presenting only the most relevant information for comparison to consumers. The system identifies and extracts key attributes (such as price, location, basic amenities) that are necessary for listing comparison, while storing or omitting less critical detailed information. This extraction approach maintains information completeness internally while simplifying consumer-facing presentations.
4Reliability
If confidence scoring is implemented to ensure accurate record matching, then the reliability is improved, but the time required for processing increases
Solution Approach 1:
The patent applies partial confidence scoring by focusing computational resources on evaluating the most critical attributes for matching rather than exhaustively analyzing every piece of data. The system identifies key attributes that have the greatest impact on matching accuracy and concentrates processing effort on these, accepting partial evaluation of less critical attributes. This approach achieves sufficient reliability for practical purposes while significantly reducing processing time compared to complete analysis.
Data Source
AI summary
A system and method for translating and matching attributes in data records that describe travel items is provided. In an embodiment, a plurality of records is received from a plurality of data sources. Record parsing logic is used divide strings in the records into individual words and match single words in the plurality of records to attributes. Using the matched attributes, record comparison logic creates a confidence score that describes the likelihood that two records describe the same listing or inventory item. If the confidence score exceeds a given threshold, the records are determined to match. A consolidated record is then created from the two matched records.