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The analysis of machine learning and natural language processing patents using the PatentView API provides valuable insights into the dynamic evolution of these transformative technologies. The findings highlight the United States as the dominant leader in ML and NLP patent filings, followed by countries like Japan, Germany, and South Korea, reflecting their significant contributions to global innovation. Key organizations and companies are actively driving technological progress, with top assignees holding hundreds of patents. However, identifying the exact names of the key players would require additional mapping of assignee IDs.
The data reveals a consistent upward trend in ML and NLP patent filings over recent years, demonstrating accelerated innovation and growing investment in these fields. Natural language processing patents, in particular, are often cited extensively, with top patents receiving over 100 combined citations. This indicates their foundational role in advancing NLP applications, such as language understanding, text-based analytics, and user interactions. Prominent application areas include language processing, model training, user interaction systems, and data-centric methods, showcasing the wide-reaching impact of NLP across various industries.
Emerging trends in ML patents highlight a strong focus on machine learning models, data-driven innovations, and training systems. Additionally, ML and NLP technologies exhibit significant overlaps with adjacent fields like robotics, computer vision, and autonomous systems, with over 440 patents bridging these domains. This cross-domain innovation underscores the interconnected nature of modern technological progress and the role of ML and NLP as foundational enablers of advancements in other fields.
While direct insights into legal disputes or challenges were unavailable, the analysis of patent citations provides a proxy for influence and utility, identifying highly cited patents as key drivers of innovation. These insights allow stakeholders to gauge the impact of specific patents and identify trends shaping the intellectual property landscape.
By combining advanced analytical techniques like Principal Component Analysis (PCA), clustering, and Association Rule Mining (ARM) with supervised learning models, a comprehensive framework can be created to analyze patent data. PCA identifies influential patterns, clustering groups patents into thematic areas, and ARM uncovers co-occurring technologies or collaborations, offering strategic insights into emerging synergies. Supervised learning models further enhance the analysis by predicting trends in patent filings, key research contributors, and future innovations.
Collectively, this multi-faceted approach enables businesses, researchers, policymakers, and investors to make informed, data-driven decisions. By anticipating technological advancements and identifying key drivers of innovation, stakeholders can capitalize on emerging opportunities, foster strategic collaborations, and maintain a competitive edge in the rapidly evolving landscapes of machine learning and natural language processing.
Answering some questions from the beginning
Answering some questions from the beginning
Which Country/companies/person hold the most machine learning patents globally?
United State has most of the patents. 5379 patents out of 6871. For the Companies, company code[131272b5-c296-4ef9-b2ae-2e34e9055490] has 439 patents. This is larger than the second most patents owned country Japan which are 354 patents.
What are the most cited patents in natural language processing?
These two are most cited patents in the dataset.
Exemplar-based natural language processing 140
Multi-modal natural language processing 131
How have machine learning patent filings increased over the last few years?
about 300 patents per each year
What are the top application areas for natural language processing technologies?
There are several keywords from the description and patents title.
Language Processing (e.g., "language", "natural", "processing")
Data Analysis ("data", "information", "input")
User Interaction ("user", "text", "method")
Model Development ("model", "using", "based")
How many patents are referenced and rated as useful?
For single citations, 21 patents has more than 50 citations. And for combined citations, 304 patents has more than 100 citations.
How do machine learning and NLP patents overlap with other fields, such as robotics or computer vision?
441 patents overlap with keywords like robotics, computer vision, image processing, and autonomy.
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