Thanks for the feedback!


I believe there are a few small datasets that clearly label applied vs basic research, for patents. There are also the official patent classes, which could help inform classification - but they do not contain clear applied vs basic research distinctions. However, ideally, one would create a classifier which would distinguish between many hundreds of classes. This could allow policy makers to take advantage of the fact that within applied or basic research, some areas would have higher social returns, or relate to a specific mission, like climate change. And thanks for that link!


My original plan was to only use publication dates, and patent abstract and description text for estimating impact. This makes the task more challenging but I believe it would remove as much bias as possible. I appreciate the recommendation, I will keep that in mind.


Edit: To clarify, as far as I understand, the typical approach to analyzing the patent/innovation space is to create a network of individual inventors, institutions, and patent IDs. Then linking these nodes via citations, authorship and affiliations.
Whereas, I propose to ignore all of the above and only focus on the content of patents. This could help decrease the influence of the biases associated with citations, and increase the information associated with each patent. This latter point assumes that there is more information about a patent in the language embedding space than the citation network space. To me, its a fair assumption, but I have no evidence yet 

Yup! That’s a cool paper - and I agree a great starting point.

Again, thanks for the feedback. Once Suraj and I decide on a starting point we can update this thread