AcclaimIP HelpRecent Updates

Recent Updates

  • Updated on: Feb 04, 2022

    Classification Searching Overview

    When patents are examined in patent offices around the world a special group of patent classifiers as well as the examiners classify the patents.  They do a remarkable job in my opinion.  Classification searching is the best, and really only way, to do a credible job of isolating technologies for patent landscapes and comparing two portfolios without doing extensive manual classification yourself.  I'll just say it, a lot of patent search professionals don't trust, don't like and rarely use classification searching.  While I think it is a big mistake, I understand it.  Classification analysis is not handled well in other tools that I have seen.

    Clearly, all patents are not classified perfectly, and some patents aren't classified at all and dumped into catch-all classes, such as the 1/1 class in the US Classification system.  But with the vagaries of human language it is your best bet.

    AcclaimIP handles classification in very powerful ways as will be explained in this and other chapters of the Help Manual.

  • Updated on: Feb 03, 2022

    Clustering

    Document clustering is a technique for organizing text documents into clearly labeled thematic folders without the need for an external knowledge base such as a categorization or classification system.

    Some of the benefits of document clustering include:

    • Quick overview of a document set summarizing import subjects, concepts and themes.
    • Fast navigation to relevant documents in your search results.
    • Query refinement:  Many of the themes discovered by clustering will become keywords in your regular searches.

    AcclaimIP clusters up to 1000 documents at a time.  While you can cluster any set, you'll get more useful results if you first narrow down your search results using classification and keyword queries so that the patents broadly cover the same topic.

  • The QueryFlow tool differs from the Keyword Analyzer in several ways:

    • QueryFlow uses a powerful algorithm called TF/IDF (Term Frequency/Inverse Document Frequency) to find "important" terms in the source document.
    • QueryFlow defaults to a Boolean OR operator compared to the Boolean AND in the Keyword Tool.
    • Terms are weighted using Query Flow.

    An "important" term is defined as a term that appears in the patent with a high frequency but does not appear very often in the entire patent corpus.  As a result, TF/IDF doesn't need stop terms.  Terms like the, and, but, with etc appear so often in the corpus that they are never identified as "important" terms.  Even "patentese" terms such as method, apparatus, system appear so frequently in patent data that the algorithm never deems them "important."

    Because of the weighting, and the nature of the Boolean OR operator, QueryFlow does a much better job of creating inclusive lists.  But the list can be very large.  The most relevant patents will be displayed at the top of the search result list.

    Term weighting boosts a term's relevance in the search results and increases the overall precision of your query.  Patents with higher weighted terms appear higher in the search results.  Weighting terms does NOT impact the recall, which means the same number of documents will be returned by the search engine no matter what weighting you use.  But the order in which they appear will be affected.

  • Updated on: Feb 03, 2022

    Advanced Keyword Searching

    When searching for patents, it is important to pick the best keywords that will select for the patents you want to find.  The terms found in these patents may not be obvious, so you'll have to go through an iterative process to identify key terms found in patents you want to isolate.  For these reasons, AcclaimIP includes several powerful tools to help you analyze patent keywords which are discussed later in this chapter.

    What do patents read on?

    You'll find that the vast majority of patents don't read on devices or products such as endoscopes, mobile phones, laser printers or light bulbs per se.  In general, patents also don't even read on components of devices such as touch screens, microprocessors, cameras and fusers, but rather they tend to read on features of components of devices.  This distinction is important because you will likely get better more targeted results if you focus on a single feature at a time.

    A good search strategy will focus keywords at the feature level or possibly the component level, and not on the device level.  AcclaimIP provides a multi-tier matrix query feature which will help you roll-up your feature and component level queries to the device or even product class level.

     

  • Updated on: Feb 03, 2022

    Adding Columns on the Fly

    AcclaimIP has up to 40 columns of data which you can expose in your search results. Depending on the type of search you are doing, you will want to show different column sets by default. You set your defaults in your Preferences.

    However, very often, you may want to add a new column on the fly without having to update your Preferences and redo your search. This is really easy to do in AcclaimIP.

  • Updated on: Feb 02, 2022

    Keyword Crossreferencing Analyzer

    The Keyword Analyzer tool counts the term frequency of specific terms found in a single patent. It is found in the "Keywords" tab on the document details window. Depending on how wide you set your DD window, you may have to scroll to the right a bit to expose the Keywords tab.

    The Keyword Analyzer uses a straight term and string count method. It breaks up the terms counted in the document into two sets, the Claims Only and the Full Text.

    The Keyword Analyzer uses stop-terms to remove common words like articles and prepositions, as well as common "patentese" terms like method, apparatus, claim, invention etc.

    The Keyword Analyzer has a tool for creating new queries from the terms found in the patent of interest.

  • Updated on: Feb 02, 2022

    Keyword Searching Basics

    In this article, we'll review some of the basic ideas behind keyword searching.  

    Patent documents are full of words.  Patents themselves can be relatively short, such as a two page design patent, while other patents can contain over 1000 pages of text!

    Keywords are mainly found in four fields: Title, abstract, claims, and specification (which is also called the description or the disclosure).  When keyword searching, you'll want to keep in mind where in the document you want to search for certain terms, and how the language might vary in different parts of the document.

    A keyword can be either a single term, like "fingerprint," "security," or "financial."  A keyword can also be a string such as "light emitting diode," "wireless communication" or "target molecule."

    Too often patent searchers rely too heavily on keyword searching.  Keyword searching is amazingly powerful, but it should be used in conjunction with classification, and date searching for optimal results.

  • Updated on: Feb 01, 2022

    Combining Searches

  • Updated on: Jan 31, 2022

    Working with Projects

  • Updated on: Jan 31, 2022

    Search History