Proxy Characteristic Mapping for Client Data Onboarding
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Solution Overview
Problem
The onboarding process of client specifications into cloud-based platforms is time-consuming and costly due to the need for individual coding and the use of unstandardized characteristics, leading to complex and tedious tasks.
Innovation Solution
A custom specifications mapping system that creates proxy characteristics with standardized dictionary values, clusters related client specifications, and uses machine learning to map custom specifications to these proxy characteristics, reducing the time and cost of onboarding by standardizing characteristics and facilitating market analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom client specifications are manually coded and mapped individually, then client onboarding can be completed, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables self-service by automatically mapping client specifications to standardized characteristics using machine learning algorithms. The characteristic analyzer autonomously processes client data, clusters specifications, and generates mappings without requiring manual intervention, thereby reducing onboarding time while maintaining reliability
Solution Approach 2:
The patent replaces the mechanical manual coding process with an automated machine learning system. The characteristic analyzer uses algorithms to automatically cluster and map specifications, substituting human manual work with computational processes that are both faster and more consistent
2Adaptability or versatility
If unstandardized characteristics are used for client specifications, then client-specific requirements can be accommodated, but the task becomes complex and tedious
Solution Approach 1:
The system transforms the parameter of characteristic standardization by introducing a standardized vocabulary while maintaining adaptability through machine learning. The characteristic analyzer learns to map diverse client specifications to a common standardized framework, reducing complexity while preserving the ability to accommodate client-specific requirements
Solution Approach 2:
The patent introduces an intermediary layer - the standardized characteristics vocabulary - that mediates between diverse client specifications and the cloud-based platform. The characteristic analyzer acts as a translator, converting various client terminologies into standardized characteristics, thereby simplifying the mapping process while maintaining adaptability
3Measurement precision
If manual mapping of client specifications is performed, then accurate client data can be obtained, but personnel discretion introduces variability and increases cost
Solution Approach 1:
The patent replaces manual human mapping with an automated machine learning system. The characteristic analyzer uses consistent algorithms to map specifications, eliminating personnel discretion and variability while maintaining or improving accuracy through systematic processing of client data
Data Source
AI summary
Methods, systems, articles of manufacture, and apparatus are disclosed to map client specifications to standardized characteristics. An example apparatus includes a cluster identifier to cluster client databases into client clusters based on a threshold quantity of overlapping universal product codes (UPCs) between respective ones of the client databases, a characteristic analyzer to identify custom characteristics from the respective ones of the client clusters, ones of the custom characteristics having dissimilar nomenclature, and a graph builder to cluster the ones of the custom characteristics based on a similarity metric, and normalize the ones of the custom characteristics as a proxy characteristic, the proxy characteristic having a common nomenclature to represent the ones of the custom characteristics, the characteristic analyzer to enable improved product marketing analysis by replacing dissimilar nomenclature with the proxy characteristic.