Patent Application: Milk Price Prediction System Using Large Data

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260087531A1) for a system designed to predict the price of raw and fresh milk using large data, game theory models, and adaptive clustering algorithms. The application, filed by inventors Zhuoran Ma et al., aims to improve upon existing prediction methods by incorporating dynamic adjustment mechanisms.

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What changed

This document is a published patent application from the USPTO detailing a novel system for predicting milk prices. The system utilizes large datasets, adaptive clustering, and a game theory model with Nash equilibrium analysis to calculate optimal strategies for price prediction. It addresses limitations in current clustering algorithms by introducing dynamic adjustment mechanisms.

This is a patent application, not a regulatory rule or guidance. It does not impose any direct compliance obligations on regulated entities. However, if granted, the patent could impact companies involved in agricultural data analytics, commodity trading, or supply chain management by potentially granting exclusive rights to the patented technology. Entities operating in this space should be aware of potential intellectual property considerations.

Source document (simplified)

← USPTO Patent Applications

PREDICTION SYSTEM FOR PRICE OF RAW AND FRESH MILK BASED ON LARGE DATA

Application US20260087531A1 Kind: A1 Mar 26, 2026

Inventors

Zhuoran Ma, Yabin Ma, Liyun Zhu, Huiqiu Zhao, Yu Deng

Abstract

Disclosed is a prediction system for a price of raw and fresh milk based on large data, including: collecting and preprocessing original market data to obtain market data; implementing an adaptive clustering algorithm on the market data; constructing a game theory model and introducing Nash equilibrium analysis of mixed strategies to calculate an optimal strategy combination of each market stage; predicting a price of raw and fresh milk using the optimal strategy combination; and modifying the game theory model, predicting the price of raw and fresh milk using the modified game theory model, and visually displaying a prediction result. In the present disclosure, the problems of a fixed algorithm structure mostly adopted in the existing clustering algorithms and lack of a dynamic adjustment mechanism are solved.