Automated Product Classification Engine for Procurement Systems
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Solution Overview
Problem
In procurement systems, employees face difficulties in accurately classifying products that do not fit into existing categories, leading to incorrect or time-consuming categorization, often resulting in products being defaulted to a 'Miscellaneous' category due to human error.
Innovation Solution
A system that employs semantic processing, including part-of-speech tagging, stemming, and name entity recognition, to suggest the most accurate product categories based on item type, description, supplier, price, and quantity, using a hierarchical taxonomy and inline classification engine, which reduces user effort by presenting a pruned set of options with confidence levels, allowing for user review and enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If employees manually classify products themselves, then they can add new categories when needed, but this leads to corruption in the hierarchical structure of product categories due to incorrect classification
Solution Approach 1:
The patent introduces an automated classification system with machine learning models as an intermediary between employees and the product category system. This intermediary automatically classifies products by analyzing product data and matching it with appropriate categories, reducing human error while maintaining the ability to add new categories through proper authorization channels. The system includes a classification engine that processes product information and suggests or assigns categories automatically.
Solution Approach 2:
The patent implements feedback mechanisms where the classification system learns from corrections and adjustments made by users. When employees or administrators correct misclassified products or add new categories, this feedback is used to retrain and improve the machine learning models, enhancing classification accuracy over time while preserving system adaptability.
2Reliability
If employees manually navigate through the whole list of categories to find the correct one, then they can ensure accurate classification, but this takes too much time
Solution Approach 1:
The patent applies preliminary action by pre-processing and indexing product data before classification is needed. The system maintains a pre-organized database of product attributes, descriptions, and historical classification data, allowing the classification engine to quickly retrieve and match products with appropriate categories without requiring employees to manually search through entire category lists.
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated electronic classification system. Machine learning models and algorithms automatically analyze product data and determine appropriate categories, substituting the manual navigation process with computational methods that are both faster and more accurate.
3Ease of operation
If the system defaults the category to Miscellaneous, then classification is quick and easy, but this results in large spend being categorized in the miscellaneous bucket or incorrectly classified due to human error
Solution Approach 1:
The patent implements self-service classification where the system automatically classifies products without requiring employee intervention. The classification engine autonomously analyzes product data, determines the appropriate category, and assigns it, eliminating the need for employees to manually select categories or default to Miscellaneous. This maintains ease of operation while significantly improving accuracy.
4Adaptability or versatility
If the number of product categories is large, then the classification system can be comprehensive, but this makes it difficult and complicated for employees to assign products to the correct category
Solution Approach 1:
The patent extracts the complexity of category selection from the employee's task by implementing an automated classification system. The system handles the complex matching of products to categories in the background, presenting employees with simplified interfaces that show only relevant category options or automatic classifications, thereby maintaining comprehensiveness while improving ease of operation.
Data Source
AI summary
Various embodiments provide solutions to assist in the classification of products in a procurement system. The tools provided by various embodiments include, without limitation, methods, systems, and/or software products. Merely by way of example, a method might comprise one or more procedures, any or all of which are executed by a computer system. Correspondingly, an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments. Similarly, a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations. In many cases, such software programs are encoded on physical and/or tangible computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).