Listing Title Normalization for Scalable Lot Classification
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Solution Overview
Problem
Existing methods for lot classification, whether manual or machine-learning based, face challenges such as time and resource consumption, scalability issues, and inaccuracies due to the need for large numbers of tagged training cases and inability to generalize across different types of products.
Innovation Solution
An automatic lot classification system that processes listing titles to identify quantity words, converts them to digits, normalizes and tokenizes the text, and uses a supervised machine learning model to classify listings as 'lots' or 'not lots' based on feature vectors and probability assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization approach is used, then classification accuracy can be maintained, but time consumption and resource consumption increase significantly
Solution Approach 1:
The patent replaces the manual mechanical categorization process with an automated machine learning system. The system uses a classifier that processes listing titles automatically, eliminating the need for human operators to manually categorize each listing while maintaining classification accuracy through trained machine learning models.
Solution Approach 2:
The system enables self-service classification by automatically processing and categorizing listings without requiring human intervention. The machine learning model independently analyzes listing titles, identifies quantity words, and assigns categories autonomously, allowing the system to serve itself rather than requiring manual operational support.
2Extent of automation
If machine-learning system with supervised training is used, then automation level increases, but the system requires large numbers of tagged training cases and cannot generalize across different product types
Solution Approach 1:
The patent implements a universal classification system that can handle multiple product types and categories through a single trained model. The system is designed to generalize across different product domains by training on diverse data, enabling one machine learning model to perform effectively across various product types without requiring separate specialized models for each category.
Solution Approach 2:
The system adapts to different product types by dynamically adjusting classification parameters and thresholds. The machine learning model modifies its decision boundaries and classification criteria based on the characteristics of different product categories, allowing it to maintain high automation levels while adapting its behavior to suit various product types without manual retraining.
3Device complexity
If manual categorization is used, then resource consumption is high, but system complexity remains low
Solution Approach 1:
The patent replaces simple manual categorization with an automated machine learning system that processes listings at scale. This substitution dramatically increases classification throughput and productivity while managing system complexity through optimized algorithms and efficient data processing techniques that enable high-volume automated classification.
Data Source
AI summary
Methods, systems, and media for lot classification are disclosed. In one example, a classification system for identifying lot listings receives a description for a listing in a publication system, identifies a string in the listing, identifies a quantity word or digit in the string, and converts an identified quantity word into digit form. A normalized string is tokenized to produce tokens, the tokenizing of the normalized string including splitting the normalized string into a series of substrings using a sequence of delimiters. For each substring, an additional split is performed by separating any digit from any other adjacent character, unless that character is another digit, and maintaining an internal character order of each split substring to produce a flattened list of tokenized tokens.