Exploring the Use of Deep Learning and AI in Patent Searches and Analytics

Did you know that deep learning and AI can be used to improve patent search and analytics? We explain here how these technologies can help searchers make better choices when refining queries for free text search and similar document retrieval. Give it a read to learn more!

Introduction

In recent years, deep learning and AI have become increasingly popular for a variety of applications, including patent search and analytics. But what exactly is deep learning and AI, and how can it be used to improve the patent search process? Let’s explore about it in this article.

What is Deep Learning and AI?

Deep learning is a subfield of machine learning that involves training artificial neural networks on a large dataset in order to learn patterns and make decisions. AI, or artificial intelligence, is a broad term that refers to any type of computer program that can exhibit intelligent behavior.

There are several different types of AI, including narrow or weak AI, which is designed to perform a specific task, and general or strong AI, which is designed to be able to perform any intellectual task that a human can. Deep learning, on the other hand, is a specific type of machine learning that involves training artificial neural networks on a large dataset in order to learn patterns and make decisions.

How Can Deep Learning and AI Improve Patent Search and Analytics?

There are two main ways that deep learning and AI can improve patent search and analytics:

Refining queries for free text search: Deep learning and AI can help searchers more effectively identify relevant keywords and phrases for their patent search.

Seeding and refining queries for similar document retrieval: AI can also be used to identify documents that are similar to the one being searched for, allowing searchers to more easily find relevant patents.

Refining Queries for Free Text Search

One way that deep learning and AI can improve patent search and analytics is by helping searchers more effectively identify relevant keywords and phrases for their patent search. This can be particularly useful when searching for patents in a field that is new or unfamiliar to the searcher, as it can help them more accurately identify the specific terms and concepts that are relevant to their search.

There are several different ways that deep learning and AI can be used to refine queries for free text search. For example, natural language processing (NLP) algorithms can be used to analyze the content of a patent and identify important keywords and phrases. These algorithms can also be used to identify synonyms and related terms that might be useful for the search.

Another approach is to use machine learning algorithms to analyze the patterns of words and phrases in a large dataset of patents and patent-related documents. By learning from these patterns, the algorithms can identify the most relevant keywords and phrases for a particular search.

Seeding and Refining Queries for Similar Document Retrieval

AI can also be used to seed and refine queries for similar document retrieval. This can be particularly useful when searching for patents that are related to a particular technology or area of study, as it can help searchers more easily find relevant patents that they may not have been aware of otherwise.

There are several different approaches to using AI for similar document retrieval. One approach is to use machine learning algorithms to analyze the content and characteristics of a particular patent and identify other patents that are similar in terms of subject matter, technology, or other relevant features. This can be done by training the algorithms on a large dataset of patents and patent-related documents.

The algorithms must be trained on a large dataset of patents and patent-related documents. This training process involves feeding the algorithms a large number of example patents and allowing them to learn the patterns and features that are characteristic of similar patents. Once the algorithms have been trained, they can then be used to identify other patents that are similar to the one being searched for.

One of the key benefits of using AI for similar document retrieval is the ability to quickly and accurately identify relevant patents. By analyzing the content and characteristics of a particular patent, AI algorithms can identify other patents that may be related, even if they do not contain the same keywords or phrases. This can be especially useful for researchers and inventors who are looking for inspiration or ideas for their own work.

Another benefit of using AI for similar document retrieval is the ability to save time and effort. Traditional methods of patent search can be time-consuming and require a lot of manual effort, but AI algorithms can quickly and efficiently search through large datasets to find relevant patents. This can allow researchers and inventors to focus on other aspects of their work, rather than spending hours or even days sifting through patents to find the information they need.

AI has the potential to greatly improve the efficiency and accuracy of similar document retrieval, including patent search. By using machine learning algorithms to analyze the content and characteristics of patents, researchers and inventors can more easily and quickly find relevant information, freeing up time and resources to focus on other important aspects of their work.