Classification searching is the original method of patent searching. Physically housed in “shoe boxes,” copies of US patents are organized by class and sub-class at the US Patent and Trademark Office in Alexandria, VA as well as Patent and Trademark Depository Libraries located around the country. Prior to digitization of US patent documents, the only way to search this patent collection was by browsing through the various subclasses by hand (or by eye, through microform).
Today’s post will highlight why classification searching is still necessary and how to go about finding relevant classifications. Next week I will briefly touch on some advanced classification searching methods. For a detailed and thorough course on classification searching as well as many other patent searching methods and techniques, check out Patent Resources Group’s Art and Science of Patent Searching course. This comprehensive three day course takes place from April 11-13 in Bonita Springs, FL as well as from August 16-18 in both Alexandria, VA and Southfield, MI. Designed for patent attorneys and agents, inventors, paralegals and research managers, the course and its materials will be understood by anyone from the inexperienced to advanced practitioners.
Classifications, by definition, are groups of patent documents of similar subject matter. Several major patent classification systems exist, including US, IPC, ECLA, Japanese F-Index and F-Term, and DEKLA.
With the advent and prevalence of keyword searching, most people default to punching a few words in and hoping for the best. This mentality in our every day searching lives can transfer over to patent searching, when we want the best and most relevant art in the fastest way possible. Who would manually flip through tens, hundreds, or thousands of patents when a carefully constructed search string input into Thomson Innovation or PatBase delivers results right away? Keyword searching is not without faults, however. First of all, keyword searching is limited by the digital text data available within the system. The most widely available full-text collection of US patents only dates back to 1976, excluding nearly four million patents published prior to that date. Secondly, searchers are limited by the choice of words themselves. Searchers must try to think of every relevant keyword possible to include in the query, which may be difficult especially when a term is general. What is called a “protrusion” in one case may be called a tongue, nub, nodule, bump, or even the dreaded “member” in another document. Since the patent applicant is his or her own lexicographer, there is almost no limit to what the desired feature may be called. For these reasons, and several others, classification searching is still important to today’s searcher.
In order to search by classification, one must first identify relevant classifications. There are a number of ways to do this including: using classification directories, looking on the face of relevant patents, and utilizing statistical analysis.
Classification directories, as I will be referring to them here, are listings of classifications and subclassifications by definition of subject matter. The USPTO Classification Home Page contains a searchable class schedule, US to IPC or Locarno concordance, and browsable indexes of the classification structure. WIPO’s IPC directory is searchable by IPC code or text. Intellogist offers Best Practice articles divided by subject matter that often include key classification areas, such as Chemical Engineering and Electrical Engineering. Starting out a search by going directly to the classification directory is advantageous because no keyword searching needs to be done up front. One noticeable downside is that classification areas can be hard to understand if one isn’t familiar with not only the subject matter being referenced, but the classifications themselves. Deciding between A61F 2/14 (Prostheses implantable into the body.. Eye parts, e.g. lenses, corneal implants) and its child class A61F 2/16 (…Intraocular lenses) can be difficult without prior experience and no patents to compare and contrast.
For this reason, one good technique for identifying relevant classifications to further investigate is by looking on the face of a patent of interest for the classifications that have been assigned by the patent examiner. If I know that US patent 4,883,485 A is closely related to the prior art I am trying to locate, I can look on the face of the patent and see that it has been classified under IPC A61F 2/16 and begin to look in that classification more narrowly.
A great way to expand this technique is to first find several patents that are related to the prior art desired (through methods like keyword searching or citation searching) and then statistically analyze the batch to find which classifications are most prevalent. PatBase, Thomson Innovation, Qweb, QPAT, and MicroPatent PatentWeb are a few examples of patent search systems that offer analysis features including classification analysis. For example, PatBase allows users to perform classification analysis on US, IPC, ECLA, and F-Term classifications and Thomson Innovation has many different ways to display the classification analyzation results. It is important to be selective in choosing the patents prior to classification analysis. If the subject matter is too broad, the statistical noise generated may obscure the best classifications.
In the second installment I’ll touch on some advanced techniques for classification searching, but if this blog entry has intrigued you at all, you may be interested in Patent Resources Group’s Art and Science of Patent Searching course. This comprehensive three day course takes place from April 11-13 in Bonita Springs, FL as well as from August 16-18 in both Alexandria, VA and Southfield, MI.
Feel free to ask any questions in the comments or share your own classification tips!
Read Part 2.
This post was contributed by Intellogist team member Chris Jagalla.