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Related Experiment Video

Updated: Jan 9, 2026

Pavlovian Conditioned Approach Training in Rats

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Published on: February 4, 2016

P³L: Patent Prediction With Prompt Learning.

Yi-Hong Lu, Pei-Yuan Lai, Man-Sheng Chen

IEEE Transactions on Neural Networks and Learning Systems

|December 4, 2025

Summary

This study introduces patent prediction with prompt learning (P³L), a new method using pretrained language models to accurately forecast future patent trends by analyzing development paths and similarities. P³L enhances strategic planning and innovation by overcoming limitations of existing trend prediction techniques.

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Area of Science:

Intellectual Property Management
Data Science
Technological Forecasting

Background:

Patents are vital for protecting innovation and driving industrial competition.
Patent prediction aids strategic planning by forecasting technological trends.
Existing methods struggle with patent data complexity and inter-patent dependencies.

Purpose of the Study:

To develop an effective and accurate method for predicting future patent developments.
To improve upon existing approaches for scientific research trend prediction.

Main Methods:

Proposed a novel method: patent prediction with prompt learning (P³L).
Utilized pretrained language models (PLMs) for patent trend forecasting.
Developed a patent similarity path extraction module.
Designed a prompt learning approach integrating development paths, keywords, and similarities.
Implemented an attention mask matrix for prompt denoising.

Main Results:

Demonstrated superior performance of P³L compared to existing methods.
Achieved effective and accurate prediction of future patent developments.
Extensive experiments conducted on multiple patent datasets, including a public one.

Conclusions:

P³L offers a significant advancement in patent prediction accuracy and effectiveness.
The method successfully models patent structures and dependencies for better trend forecasting.
The developed dataset and code are publicly available for further research.