AcclaimIP HelpRecent Updates

Recent Updates

  • Updated on: Jul 07, 2017

    Matrix Query Patent Landscape Overview

    A matrix is a table or a grid, containing both rows and columns that hold patent queries.  Each cell contains the intersection of the two queries.

  • Updated on: Jul 07, 2017

    Saving Searches & Setting Alerts

    If you work on a specific search query, you'll want to save it so you can use it again in the future.  AcclaimIP supports a feature where you can save searches for easy recall later.

    I personally use the feature to save all my base queries.  A base query is a search that contains all the keywords and strings that describe one specific aspect of an invention.  I strongly recommend you build a library of relevant base queries, and save them to a folder called "Base Queries."  In this example, I expose my NFC base query.  I use this as my starting point when searching any technology related to NFC (Near Field Communications), and I never have to research all the terms which mean "NFC;" I just load my saved search.

  • AcclaimIP offers another method for finding similar documents using a class profiling technique.

    The logic is simple. If, for example, your source patent is classified in one primary (Original) class and cross references another four classifications, doesn't it stand to reason that patents with the most similar class profiles will also be similar to the target patent?

    This technique is possible because AcclaimIP's search engine is aware of each classification's relative position in its hierarchy including all parent and children classes.

  • Updated on: Jul 07, 2017

    How to Find Similar Documents

    AcclaimIP offers users several methods to find similar documents to a source document. We designed our system to be as useful and flexible as possible, and as a result, you should be aware how the basic search operations are modified to help make your automated searches most effective.

    There are several tools built into AcclaimIP to help you find similar documents:

    1. Find Similar Documents Using Full Text
    2. Find Similar Documents Using Claims Text
    3. Find Similar Documents Using Class Fingerprinting
    4. QueryFlow, Keyword and Clustering Tools

    This chapter will show you how to use the first three tools. The other tools listed in 4 above require manual refinement and are covered in their own sections of the help manual.

  • Updated on: Jul 07, 2017

    Sharing Research Folders

    Research folders may be shared between other AcclaimIP users on your team.  You can also invite external AcclaimIP users to share folders with you are your team.

  • Updated on: Jul 07, 2017

    Research Folders Overview

    Research folders store two types of data.  

    • Saved Searches.
    • Patent Documents.  

    Research Folders can hold up to 50,000 documents each depending on the plan you signed up for.  They can be shared with other members of your team, and even with users outside your company as long as they also have an AcclaimIP account.

    Research Folders are more than just a place to store the results of your searches.  AcclaimIP's advanced syntax gives you the ability to find unions, intersections and differences between folders and are in themselves analysis tools.

    The contents of Folders can be charted together giving you the ability to chart two portfolios across the same dimensions for comparative charts.

  • Updated on: Jul 07, 2017

    Charting in AcclaimIP

  • Updated on: Jul 06, 2017

    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: Jul 06, 2017


    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.