Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.

There is a treasure trove of litigation data that for years was virtually inaccessible. While court rulings and filings were available and individual documents could be accessed and viewed, the technology needed to search and analyze the data and provide useful, actionable information simply did not exist. The recent maturation of the foundational technologies needed to support machine learning have made advanced data analytics and sophisticated language processing possible on a scale never before seen. Using these tools, massive amounts of data can be sifted through, organized and analyzed in mere seconds. These capabilities are particularly useful in the litigation arena.

Technology moves fast, and when predicting the future, it can be hard to keep up. Here at Noggle, we believe in analyzing what's happening right now in order to gain a more accurate gauge of what's realistically going to come into being over the next few months and years ahead. To do this, where better to look for the ideas of the future than in the worldwide Patents database? Examining the concepts that have been submitted and protected now, gives a strong indication of where technology is heading and what innovations are taking place. Of course, not all inventions are created equal, and many patents won't last the course and make it into our collective future conscious and culture – this is why we have produced a broad overview of recent patents, and picked up on recurring and common aspects and topics. By detecting clusters and averages of prevalent and frequently appearing themes, our findings represent a more likely look at the ideas that may be entering and shaping our lives in the not too distant future.