Anomaly Detection System for Pricing Accuracy

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Solution Overview

Problem

Retailers face challenges in identifying and correcting price and cost anomalies, which can lead to financial losses due to data entry errors or incorrect pricing, affecting profitability.

Innovation Solution

A system and method for automatically detecting anomalies in item prices and costs using machine learning algorithms, identifying anomaly-causing features, and preventing incorrect price updates from affecting sales, thereby enhancing profitability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automated anomaly detection is implemented, then pricing accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvepricing accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

An automated anomaly detection system is introduced as an intermediary between price updates and the pricing system. This mediator analyzes price changes using machine learning models to detect anomalies before they are applied, thereby improving pricing accuracy without requiring manual intervention while managing system complexity through automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual price monitoring and anomaly detection with an automated computational system. Machine learning models and algorithms are used to automatically detect pricing anomalies, substituting human manual processes with automated mechanical systems that improve accuracy and efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If real-time anomaly detection is performed, then profitability is improved, but processing time increases

Engineering Contradiction:

ImproveprofitabilityVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary anomaly detection on price updates before they are finalized and applied. By detecting and correcting anomalies in advance, the system ensures profitable pricing is maintained without requiring time-consuming manual review processes, as the automated detection happens proactively in the background.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The anomaly detection system operates continuously and automatically in the background, monitoring price updates in real-time without interrupting normal pricing operations. This continuous automated process maintains profitability while minimizing processing time impact by running parallel to existing workflows.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12175505B2Methods and apparatus for anomaly detections

Publication Date: 2024.12.24 WALMART APOLLO LLC

AI summary

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.