Automated Sensing and Control for Distribution Data Analytics
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Solution Overview
Problem
Existing product development, manufacturing, and distribution processes are hindered by the inefficiency of human analysts in processing large amounts of information to identify unexpected trends or associations, and hardware testing requires significant human labor, incurring substantial costs.
Innovation Solution
A system utilizing a distributed machine learning network across product distribution components to predict future distribution patterns and sales volumes, and a testing rig to simulate user interactions with machines, reducing human involvement in repetitive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human analysts process large quantities of information to identify trends and associations, then insights can be derived, but the process is slow and limited to easily recognized patterns
Solution Approach 1:
The patent replaces human analysts with automated machine learning models and algorithms that process distribution data. These computational systems can analyze large quantities of information rapidly, identifying trends, associations, and patterns without the limitations of human cognitive processing speed and capacity.
Solution Approach 2:
The system enables self-service through automated machine learning models that independently analyze distribution data, generate insights, and provide recommendations without requiring human intervention for each analysis task. The models continuously learn from data and autonomously identify patterns.
2Reliability
If human personnel are used for thorough hardware testing of user interfaces, then comprehensive testing can be performed, but substantial costs are incurred and human resources are diverted from other tasks
Solution Approach 1:
The patent employs automated testing rigs that replicate human user interactions with hardware interfaces. These rigs use sensors, actuators, and control systems to simulate various user actions, enabling comprehensive testing without requiring actual human operators. The system can repeat actions hundreds or thousands of times under various conditions automatically.
Solution Approach 2:
The patent replaces human testers with automated mechanical testing systems equipped with sensors and actuators. These systems can perform repetitive testing actions, measure responses, and evaluate hardware performance objectively without the costs and resource allocation issues associated with human personnel.
Data Source
AI summary
The present disclosure relates to training a machine learning model based on a dataset comprising sales volume, product distribution logistics records, and product manufacturing data. The present disclosure further relate to extracting from the model a prediction of at least one item selected from a group consisting of future sales volume of an existing product, consumer interest for new products, and failure rates for product distribution equipment.