Entrepreneurial Patent Data Analysis

The problem our capstone project seeks to solve is finding groupings of similar patents using two data sets with information on 5 million previously filed patents. The first data set has records of what previous patents each patent cited, and the second one contains text from each patent. Our solution converts these two data sets into a “samples” and “features” format, which is then usable by a machine learning technique known as clustering. Clustering takes data items in the aforementioned format and groups similar items together. Clustering is a very common practice, and a literature review revealed a plethora of approaches, including spectral clustering, K Means, and hierarchical clustering. We measured the accuracy of each approach by checking how consistent the groupings were with a third data set, a text file containing pairs of patents that blocked each other from being filed. The best approach we found ended up being a modified version of the standard K Means. Due to issues with the citation data set, as well as time and server memory constraints we achieved some success but did not reach the desired accuracy. Overall Project Goals and Technical Definitions Our capstone project is a tool that primarily receives as input a legal patent and outputs previously filed legal patents that are similar enough to block the original patent. Patent blocking at one point involved a new patent being restricted by the fact that it is an improvement upon a previous patent (Adams, 2008). Adams discusses how patent blocking has been an accepted concept in the legal system for a long time, but its particular definition has changed at certain points (2008, pp.7-8). Thus, one should note that determining whether two patents conclusively block, or invalidate, each other is very difficult for someone without a legal background. Entrepreneurial Patent Data Analysis Final Capstone Report Alper Vural 4 Practically speaking, an inventor or lawyer could use our tool to more easily verify that a patent to be filed is sufficiently different from those that already exist, and there are smaller achievements that we will work towards. One of these, an alternate use of our tool, is finding patents that are similar but not similar enough to block the input patent. The European Patent Office describes these similar patents, known as prior art, as “any evidence that your invention is already known” (2015, para. 1). When a patent is filed, it must cite similar already existing patents as prior art. The other achievement is the creation of a patent classification system by dividing the patents into similar groupings. Work Breakdown and Further Technical Definitions We have access to three key data sources in accomplishing our project goal. The first is the ‘claims’ sections of previously filed legal patents stored in a MySQL database that can be programmatically accessed. The claims section of a patent describes what the patent protects. The second data set is essentially a text file where each line corresponds to one patent and contains information about what prior art citations of previously filed patents that patent has. The third data set is a text file where each line contains a pair of patents in which one patent blocked the other. This means that the other patent was too similar to the previous patent to be accepted. Our work with this data can be divided into three tasks: feature extraction, document similarity, and clustering. Feature extraction involves finding a way to concisely represent the text of each patent, document similarity is a way to compute how similar two of these representations are, and clustering involves separating the patents into groups. Feature extraction applies specifically to the patent claims data set, whereas document similarity and clustering apply to both data sets. My partners, Nikhil and Martin, are working on feature extraction, I am working on clustering, and we all play a role in document similarity. Each of these tasks is dependent on the Entrepreneurial Patent Data Analysis Final Capstone Report Alper Vural 5 others. For example, the features Nikhil and Martin extract affect what similarity metric is best. Figure 1 emphasizes how the three tasks are interrelated in this way. Figure 1:Relation of tasks Additionally, Martin conducts business research, including looking up information about potential competitors we would have if we turned our technology into a startup. Constraining Resources We have two main limitations regarding our tool. The first is memory, as we have a server with 64GB of memory, which is large, but this is still a finite amount. The second is time, as we want the tool to be able to accomplish our goals within a reasonable amount of time. For example, it would be unreasonable if our tool took a week to output similar patents to an input patent, and it would be unreasonable if our tool took half a year to find groupings for all the existing patents. Computer science algorithms have a “Big O” notation, which describes how the runtime or space consumption of the algorithm increases relative to its input size. For example, O(n) for time and O(n^2) for space means that the time taken has a linear relation with the input Entrepreneurial Patent Data Analysis Final Capstone Report Alper Vural 6 size and the space used has a quadratic relation with the input size. For the citation data, the input size was 5 million, as we had the citation data for 5 million patents. Purpose of This Paper The purpose of this paper is to justify my use of a relatively simple algorithm for the tasks of document similarity and clustering, as well as my stance on the lack of usability of some of our data. I will first do this by discussing my work in narrowing down our other options for clustering the patents, my final implementation, and my final results. From there, I will discuss why my work is helpful to my team, including how this paper is related to that of Martin and Nikhil, and why my work is helpful to the public.