PaECTER: Patent-level Representation Learning using Citation-informed Transformers

Core Concepts

PaECTER is a novel document-level encoder specifically designed for patents, outperforming existing models in similarity tasks by incorporating citation information. The authors introduce contrastive learning to enhance similarity detection in patent texts.

Abstract

The PaECTER model introduces a new approach to encoding patents into numerical representations for semantic similarity detection. By leveraging citation information and contrastive learning, PaECTER surpasses current state-of-the-art models in the patent domain. The model's performance is evaluated against other models, showcasing its superiority in various metrics such as Rank First Relevant (RFR), Mean Average Precision (MAP), and Mean Reciprocal Rank @ 10 (MRR@10). PaECTER offers a valuable tool for researchers working with patent text, enabling enhanced exploration of patent landscapes and innovative breakthroughs.

Stats

PaECTER predicts at least one most similar patent at a rank of 1.32 on average when compared against 25 irrelevant patents. The training dataset involves 300,000 English-language patent families from the European Patent Office (EPO) from 1985 to 2022. SPECTER initially used 684,000 training triplets while SPECTER2 was trained on over 6 million triplets. The test dataset consists of 1000 samples with 1 focal patent, 5 positive citations, and 25 negative citations. PaECTER achieves higher accuracy over BERT for Patents by significant margins in MAP and MRR@10 metrics.

Quotes

"The introduction of the Bi-Directional Encoder Representations from Transformers (BERT) was a major advancement in working with text." "PaECTER fills the gap for a publicly available model with appropriate vocabulary/language and citation-induced similarity." "Our work has been enabled by grant from the European Patent Office under their Academic Research Programme."

Key Insights Distilled From

PaECTER

by Mainak Ghosh... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19411.pdf

Deeper Inquiries

How does the use of contrastive learning impact the performance of models like PaECTER?

The use of contrastive learning significantly impacts the performance of models like PaECTER by enabling them to understand similarity and dissimilarity based on textual content. Contrastive learning helps these models recognize patterns by teaching them to distinguish between similarities and differences among data points during training. By utilizing a triplet loss function with specially crafted training data containing signals for similarity and dissimilarity, models like PaECTER can optimize weights effectively. This optimization minimizes the loss function by comparing numerical representations of documents in each triplet, enhancing the model's ability to identify semantic relationships accurately.

What are the implications of relying on citations versus textual analysis in determining relatedness between patents?

Relying solely on citations for determining relatedness between patents may have limitations due to factors such as citation rarity or strategic manipulation. In contrast, leveraging textual analysis provides a more nuanced understanding of knowledge flows within patent ecosystems. Textual analysis allows researchers to explore patent landscapes comprehensively, even when explicit citations are absent or potentially biased. Models like PaECTER offer scalable benefits by establishing patent relatedness without being constrained by citation availability or strategic biases that may influence traditional citation-based analyses.

How can models like PaECTER contribute to advancements in understanding knowledge flows within patent ecosystems?

Models like PaECTER play a crucial role in advancing our understanding of knowledge flows within patent ecosystems by offering powerful tools for exploring patent landscapes and identifying innovative breakthroughs. These models enable researchers to discern relatedness among patents where citations might be scarce or strategically selected, providing immediate and scalable benefits for analyzing complex patent networks. By facilitating accurate assessments of semantic similarity between patents through advanced text representation techniques, models like PaECTER broaden horizons for patent research and enhance insights into knowledge dissemination dynamics within intellectual property domains.