Adaptive Machine Learning Platform Automating Retail Workflows

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Solution Overview

Problem

Manual processes in offline retail management are time-consuming and costly, often lacking the accuracy needed for informed business decisions due to insufficient data processing capabilities.

Innovation Solution

An adaptive machine learning platform that automates decision-making by executing multiple machine learning algorithms, allowing for data-driven strategies and independent management of algorithms by business teams, enabling efficient, low-cost, and precise decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processes are used for offline retail management, then human knowledge and expertise can be applied, but the process becomes time-consuming and costly

Engineering Contradiction:

Improvedecision accuracyVSAvoidprocess time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual human processes with an automated machine learning system that executes multiple ML algorithms to generate actionable insights. This substitution eliminates the time-consuming nature of manual data processing while maintaining or improving decision accuracy through systematic algorithmic analysis of retail operations data.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables business teams to independently manage and execute machine learning algorithms without requiring external expert intervention. The automated platform performs data processing, insight generation, and recommendation delivery autonomously, reducing both time consumption and operational costs while maintaining high decision quality.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual processes are used for offline retail management, then human judgment can be applied, but costs increase due to manual labor requirements

Engineering Contradiction:

Improvedecision accuracyVSAvoidoperational cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent replaces expensive manual human labor with cost-effective automated machine learning algorithms. The system processes retail operations data and generates actionable insights automatically, significantly reducing operational costs while maintaining or improving decision accuracy through systematic algorithmic analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the operational parameters from manual human execution to automated algorithmic execution. By transitioning from human-driven processes to machine-driven processes, the patent achieves the same or better decision accuracy at a fraction of the operational cost, as the automated system can process data more efficiently and at scale.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If manual data processing is used, then human analysis can be applied, but accuracy decreases due to insufficient data processing capabilities

Engineering Contradiction:

Improvedata processing capabilityVSAvoiddecision accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent combines multiple machine learning algorithms into a unified system that processes retail operations data comprehensively. This merging of multiple analytical approaches enables thorough data processing while maintaining high decision accuracy, as the system can analyze data from multiple perspectives and synthesize actionable insights that improve upon manual analysis capabilities.

Inventive Principle:

Principle #5Merging (Combining)

4Adaptability or versatility

If multiple machine learning algorithms are executed, then data-driven decision-making is enabled, but system complexity increases

Engineering Contradiction:

Improvedata-driven capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal machine learning platform that can execute multiple different algorithms through a single integrated system. This multi-functional approach enables the system to handle various retail operations analysis tasks (inventory management, demand forecasting, pricing optimization) while presenting a unified interface that simplifies complexity for end users.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an intermediary layer between the multiple machine learning algorithms and the end users. This intermediary platform manages the complexity of executing and coordinating multiple algorithms, translating their outputs into actionable business insights, and presenting results through user-friendly interfaces, thereby hiding system complexity from users while maintaining high adaptability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9454732B1Adaptive machine learning platform

Publication Date: 2016.09.27 AMAZON TECH INC

AI summary

Some examples include receiving data from a source external to an adaptive machine learning platform that includes at least one machine learning component. Some implementations may execute a machine learning component to generate a machine learning component output. The machine learning component output may be generated at least in part based on the received data. A command may be generated based at least in part on the machine learning component output, and the command may be communicated to an action plugin. The action plugin manager may be used to configure one or more parameters of an action representing an automated workflow to be executed by the action plugin in response to receiving the command.