How can you use AI to improve your R&D patent search?

1 Use natural language processing

Natural language processing (NLP) is a branch of AI that enables computers to understand and generate natural language. You can use NLP to improve your patent search by using natural language queries instead of complex Boolean operators and keywords. This way, you can express your search intent more easily and find relevant patents more quickly. NLP can also help you analyze patent documents by extracting key information, such as claims, citations, inventors, and assignees, and presenting them in a structured and accessible format.

2 Use machine learning

Machine learning (ML) is a branch of AI that enables computers to learn from data and improve their performance without explicit programming. You can use ML to improve your patent search by using algorithms that can classify, cluster, and rank patents based on various criteria, such as similarity, novelty, quality, and relevance. ML can also help you discover hidden patterns, trends, and insights from patent data, such as technology evolution, market opportunities, and competitive intelligence.

3 Use computer vision

Computer vision is a branch of AI that enables computers to process and understand visual information, such as images, videos, and diagrams. You can use computer vision to improve your patent search by using image-based queries instead of text-based queries. This way, you can find patents that match your visual concept or design more easily and accurately. Computer vision can also help you compare and contrast patent images by using features, such as shape, color, texture, and orientation.

4 Use semantic analysis

Semantic analysis is a branch of AI that enables computers to understand the meaning and context of natural language. You can use semantic analysis to improve your patent search by using concepts, topics, and entities instead of keywords and phrases. This way, you can find patents that are related to your research domain or problem more comprehensively and precisely. Semantic analysis can also help you map and visualize patent data by using ontologies, taxonomies, and graphs.

5 Use natural language generation

Natural language generation (NLG) is a branch of AI that enables computers to produce natural language from data or other input. You can use NLG to improve your patent search by using summaries, abstracts, and reports instead of reading full patent documents. This way, you can save time and effort and focus on the most important and relevant information. NLG can also help you create and communicate your own patent applications by using templates, guidelines, and best practices.

6 Use AI-powered tools

AI-powered tools are software applications that use AI techniques to enhance patent search and analysis. You can use AI-powered tools to improve your patent search by using features, such as smart filters, recommendations, alerts, and analytics. These features can help you refine, expand, and monitor your patent search and provide you with actionable insights and feedback. AI-powered tools can also help you integrate and collaborate with other tools and platforms, such as databases, libraries, and cloud services.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?