Inventory Prediction Confidence Automation
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Solution Overview
Problem
Small businesses face the 'cold start' problem when trying to use predictive models due to a lack of historical data, leading to inaccurate models when using noisy data from other small businesses.
Innovation Solution
A method that identifies product categories using categorization models on product data from internal and external sources, creates a knowledge graph for business entities, generates cohort clusters through clustering algorithms, and uses these clusters to train extrapolation models for predicting inventory levels, providing automated ordering based on confidence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models are trained using historical data from other small businesses, then the cold start problem is addressed, but the model accuracy deteriorates due to noisy data
Solution Approach 1:
The patent segments businesses into distinct cohorts based on industry, size, and other characteristics. This segmentation allows the system to train separate predictive models for each cohort, ensuring that training data comes from similar businesses rather than mixing dissimilar ones. This resolves the contradiction by organizing noisy data into meaningful segments that improve model accuracy for each specific business type.
Solution Approach 2:
Instead of having each small business train its own model independently (which fails due to lack of data), the patent inverts the approach by having businesses train models collectively in cohorts. The model training direction is reversed from individual to group-based, allowing small businesses to leverage aggregated data from similar peers while maintaining relevance through cohort-specific modeling.
2Measurement precision
If cohort clustering is implemented to improve model accuracy, then predictive precision improves, but system complexity increases
Solution Approach 1:
The patent creates a universal cohort clustering framework that can be applied across different businesses and industries. The same clustering algorithm and cohort formation process serves multiple purposes: grouping businesses, training models, and making predictions. This multi-functionality reduces overall system complexity by reusing the same infrastructure rather than creating separate systems for each function.
Solution Approach 2:
The system dynamically adjusts cohort parameters such as cohort size, similarity thresholds, and grouping criteria based on data availability and prediction needs. By changing these parameters, the system can optimize between accuracy and complexity trade-offs without redesigning the entire clustering infrastructure, allowing flexible adaptation to different business scenarios.
Data Source
AI summary
A method identifies a plurality of product categories by applying a categorization model to product data that includes product names and product descriptions. The product data is obtained from internal sources and external sources. The method creates a knowledge graph for a plurality of business entities from inputs that include the plurality of product categories, published data that includes merchant categorizations, and geographical data. The method generates cohort clusters of similar business entities by applying a clustering algorithm to the knowledge graph. The method uses the cohort clusters to learn an extrapolation model for predicting inventory. The method makes a prediction as to the inventory level using the extrapolation model for a user of the merchant application. The method determines a confidence level for the user and orders additional inventory for the user without further input, if the confidence level is above a specified threshold.