Embedding-Based Electronic Catalog Mapping for Tenant-Specific Services
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Solution Overview
Problem
Existing electronic catalog management systems fail to accurately reflect tenant-specific services and materials provided by service technicians, leading to inaccurate tracking of inventory and services.
Innovation Solution
A system utilizing machine learning models to generate primary and secondary embeddings that link generic and tenant-specific line items, enabling accurate mapping and display of tenant-specific materials and services during invoice generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic electronic catalog is used for service jobs, then broad service coverage is achieved, but tenant-specific materials and services cannot be accurately tracked
Solution Approach 1:
The system segments the catalog into generic line items (broad categories) and tenant-specific line items (detailed subcategories). Each generic line item can be associated with multiple tenant-specific line items, allowing the system to maintain broad service coverage while accurately tracking tenant-specific materials and services through this hierarchical segmentation structure.
2Measurement precision
If manual mapping of tenant-specific items to generic catalog entries is performed, then inventory tracking accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system implements self-service automated mapping using machine learning models that automatically match tenant-specific line items to generic line items based on their descriptions and attributes. This eliminates the need for manual mapping while maintaining high accuracy, as the ML models autonomously perform the matching task by analyzing textual similarities and structural relationships between items.
Solution Approach 2:
The system transforms the mapping problem from a manual categorization task to an automated computational task by changing the parameters from human judgment to machine learning algorithms. The ML models process item descriptions, attributes, and relationships to automatically determine the appropriate mappings, significantly reducing time consumption while maintaining or improving accuracy.
3Productivity
If automated machine learning mapping is implemented, then mapping speed is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer of machine learning models that act as mediators between the generic catalog and tenant-specific items. These ML models handle the complex mapping logic, allowing the rest of the system to remain relatively simple. The intermediary layer absorbs the complexity of automated matching while providing clean, standardized outputs to the rest of the system.
Data Source
AI summary
Methods and systems for electronic catalog management are disclosed. A primary embedding is generated using a machine learning model. The primary embedding is associated with a generic line item corresponding to a service job to be performed by a tenant. A plurality of secondary embeddings is generated using the machine learning model. Each of the plurality of secondary embeddings is associated with a tenant-specific line item from a plurality of tenant-specific line items. Based on comparing the primary embedding to each of the plurality of secondary embeddings, a subset of the tenant-specific line items that corresponds to the generic line item is determined. In response to receiving, at a user device associated with the tenant, input data indicating that the service job is to be performed by the tenant for a customer, display of the subset of the tenant-specific line items that corresponds to the generic line item is caused.