XLSCOUT releases Patent ParaBERT-based Novelty Checker

XLSCOUT proudly announces the release of its “Patent ParaBERT”-based Novelty Checker, a tool that utilizes AI and ML to provide first pass prior art analysis.

Prior Art Analysis with Patent ParaBERT:

XLSCOUT launched its proprietary “Patent ParaBERT” technology wherein they fine-tuned the already trained state-of-the-art BERT model on its various multi-domain technology datasets for prior art searching. This technology helps XLSCOUT understand the natural language text from the patent documents. The Novelty Checker uses this fine-tuned Patent ParaBERT Model to understand the context of each paragraph in a patent document to provide more accurate results. As a result of this, we have seen significant improvements in document similarity check and, furthermore, the Novelty Checker app can now generate high-quality prior art search reports.

All the conventional patent search tools are using Boolean keyword-based search which leads to capturing a lot of noise and irrelevant patent results. XLSCOUT “Patent ParaBERT” technology is not only helpful in finding relevant patents and filtering noise but at the same time, it also searches for relevant publications in the domain. Publications from IEEE, Scopus, Springer, Pubmed, etc are searched and compared with the entered ideas in real-time. Patent Literature data include USPTO Patent Data, Korean Patent office Data, Japan Patent Office Data, European Patent Data, Taiwanese Patent Data, Indian Patent Data, and 100+ other countries with text translated into the English language.

Accuracy Check:

We have compared the results with many free patent search tools and various other patent search tools claiming semantic searching. We found significant differences in the quality of the results. We found Novelty Checker is 45% more effective than the competitor tools.

• 45% more relevant results were captured as compared to any other competitive tool available in the market
• Top 20 references of Novelty Checker were Noise Free (Above 90% in-domain results)
• 65% of the examiner cited references were found in Novelty Checker

What is BERT?

BERT, a Deep Learning algorithm related to Natural Language Processing (NLP), stands for Bidirectional Encoder Representations from Transformers. It aids in capturing the context of words in a sentence while considering all of its nuances.

The BERT model is a deep learning model, pre-trained on a huge corpus using two interesting tasks called masked language modeling and next sentence prediction. The model aids in capturing the context of words in a sentence while considering all of its nuances.

By producing better outcomes for numerous NLP tasks, including question answering, text generation, sentence categorization, and many others, BERT has made a significant advancement in the field of NLP. One of the key elements influencing its appeal is its context-based embedding strategy, as opposed to other well-liked embedding models like word2vec, which lack context.

Medium highlights the functioning of a context-based model by considering the following example:

Sentence A: He got bit by a Python.

Sentence B: Python is my favorite programming language.

By comparing both statements, it is clear that the term “Python” has a different connotation in each. While “Python” in statement A refers to a snake, “Python” in sentence B refers to a programming language.

The word “Python” will now have the same embedding in both statements if we use an embedding model like word2vec, rendering the word’s meaning in both sentences. This is due to the fact that word2vec is a context-free model; regardless of context, it will produce the same embedding for the word “Python”.

Contrarily, Bert is a context-based model. It will comprehend the context and then produce the word’s embedding based on the context. So, for the preceding two words, it will give different embedding for the word “Python”. Therefore, it will provide a distinct embedding for the word “Python” for the preceding words.

How does BERT work?

Let’s take sentence A. Bert, in this instance, contextualizes each word by relating it to all the other words in the sentence. This enables Bert to comprehend that the word “Python” refers to the snake. Similarly, Bert in sentence B understands that the word “Python” refers to a programming language.

BERT is undoubtingly one of the most advanced NLP models, which has revolutionized the NLP space and has taken context understanding to the next level.

To learn more about the Patent ParaBERT-based Novelty Checker, Schedule your free demo now!