Handle Parser for Name Variant Extraction and Consumer Attribute Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for parsing handles, such as email addresses and social media identifiers, are inadequate in extracting meaningful consumer-specific attributes like name, gender, ethnicity, and location due to their focus on traditional name components and limited ability to handle variations and contextual information.
Innovation Solution
A handle parser that extracts a ranked list of possible interpretations for name variants, using dictionaries for name-to-name relationships, gender, ethnicity, and contextual phrases to provide insights into consumer attributes, and distinguishes sentinel characters to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fuzzy string matching is used to identify name variations in handles, then traditional name components can be found, but handles containing descriptive phrases or limited traditional name components yield poor results
Solution Approach 1:
The handle parsing process is divided into multiple sequential stages: initial cleaning and normalization, sentinel character identification, name component extraction, and attribute inference. Each stage processes specific aspects of the handle independently, allowing the system to handle diverse handle formats effectively while maintaining high accuracy for traditional names.
Solution Approach 2:
The system transforms handles through multiple parameter changes including case normalization, removal of special characters, and standardization of name formats. These transformations convert various handle representations into a unified format that enables accurate matching across different handle types while preserving the original meaning and intent.
2Ease of operation
If a single best interpretation is returned for handle parsing, then the process is simple, but multiple valid interpretations exist for handles interpreted differently by multiple individuals
Solution Approach 1:
The parser dynamically adjusts its output based on the input handle characteristics. For handles with clear, unambiguous name components, it returns a single definitive interpretation. For handles with multiple valid interpretations or ambiguous components, it generates a ranked list of possible interpretations, allowing the system to adapt its complexity to match the actual needs of each parsing task.
Solution Approach 2:
The system introduces an intermediary ranking mechanism that mediates between multiple valid interpretations. Instead of forcing a single interpretation or presenting all possibilities equally, the ranking system orders interpretations by likelihood and relevance, providing a practical solution that balances simplicity with information preservation.
3Loss of information
If handles are parsed for marketing campaign association, then consumer-specific attributes can be extracted, but incomplete and indirect contact information reduces data value
Solution Approach 1:
The system performs preliminary cleaning and normalization of handle data before parsing, removing irrelevant characters and standardizing formats. This preliminary processing enhances the quality of extracted attributes by ensuring that the parsing stage receives clean, consistent input, thereby maximizing the value that can be extracted from even incomplete contact information.
Solution Approach 2:
The parser incorporates feedback mechanisms that analyze the confidence levels of extracted attributes and adjust the parsing strategy accordingly. When certain attributes are confidently identified, the system leverages this information to improve the extraction of related attributes, creating a feedback loop that enhances overall data completeness and reliability from partial input information.
Data Source
AI summary
A method and apparatus for name variant extraction from individual handle identifiers uses a sequential extraction process to construct contextual information. Last name data, first/middle name data, initials, nicknames, and vanity names, along with numerical information indicating dates, may all be captured in extracting information about an individual associated with a particular handle. When multiple possible interpretations result from the analysis, those interpretations are ranked using optimality rules. The resulting data may be used to look up additional information in a consumer database in order to structure a targeted marketing message to the individual associated with the handle.