As we saw in our previous article which featured the ins and outs of Patentcloud’s Advanced Search, having a set of more powerful search options can greatly improve even the savviest patent searcher’s chances of finding what they are looking for. In this article, we are going to explore an aspect, one that is often overlooked in terms of importance, but that can mean the difference between a successful patent search and a complete waste of time: advanced syntax operators.
Even the most powerful of search capabilities will fall short should the basics be missing. Pretty much like a brick wall, if the mortar between the bricks hasn’t been prepared properly, the resulting structure will be weak and at risk of collapsing.
In the patent search world, the role of mortar is played by syntax operators: it is through these operators that search queries are crafted.
Before combining the most basic building blocks into advanced queries, however, it is important to check whether the former ones are:
The two advanced operators that we are going to analyze in this article—WITH and NEAR—are both instrumental to the above and enable patent searchers to define more specifically the scope of their patent searches.
Before discovering how these advanced operators can help us to work more efficiently, let’s quickly recap the usage of the basic ones:
|*||The * operator is used as a multi-character variable. The query in the example below will search for any words that begin with “app-”, no matter how long.
|?||The ? operator is used as a single-character variable. The query in the example below will search for any words that begin with “app-“ and terminate with a single additional character.
|“”||The “” operator will search for the exact words/characters contained in the quotation marks. The query in the example below will search for the exact word “artificial”.
|()||The () operator denotes the order of precedence when performing a search. Operations within parentheses will be performed first.
Example: SPEC/(art OR paint) AND canvas
|AND||The AND operator will search for both keywords on either side of the operator. Results will be shown only if both keywords are found.
Example: SPEC/(art AND paint)
|OR||The OR operator will search for both keywords on either side of the operator. Results will be shown if one of the two keywords, or both keywords, are found.
Example: SPEC/(art OR paint)
|NOT||The NOT operator will provide results that do not contain the keyword after the operator. The query in the example below will search for the word “art” in the patent description, but it will ignore any results including both the keywords “art” and “paint”.
Example: SPEC/(art AND NOT paint)
Now let’s take a closer look at how WITH and NEAR can improve our patent searches.
This advanced operator comes in handy when performing patent searches that involve assignment data. In particular, the data fields associated with it are:
- Assignee & Assignor (AAN, AAR)
- Licensee & Licensor (LNE, LNO)
- Pledgee & Pledgor (PGE, PGO)
Some patent searches may require us to identify patents that have been transferred directly from one company to another.
As an example, which patents have Nokia transferred to Microsoft?  The most intuitive way to find the answer is by relying on the AND operator:
The search retrieves 1,300 patents: 
A quick look at the documents, however, reveals that some of them aren’t in line with the expectations: below is the assignment data for patent US9058501B2 (Method, apparatus, and computer program product for determining media item privacy settings.) This is found under the History tab in Patent Search.
It is clear that no direct transfer happened between the two companies. The reason behind this is that the query lists correctly all of the patent documents that have Nokia as assignor and Microsoft as assignee, but not necessarily in the same assignment.
In this case, the patent at issue was first transferred to a number of licensing entities before being assigned to Microsoft.
The solution is to rely on the WITH advanced operator, which searches for data fields in a single transaction record. The query then becomes:
Keying in this new query in Patent Search results in the retrieval of 1,211 documents: 
The results filtered out are those in which Nokia and Microsoft are not parties in the same transaction. To confirm this, we can create a folder to collect all of the search results and check whether patent US9058501B2 is included or not. 
Once inside the new folder, we can click on “Search within Results” on the toolbar:
And search for patent no. US9058501B2 by simply keying in the patent number in the search box:
Since the patent hasn’t been directly transferred between the two companies, the search should not bring back any results:
As expected, no data is found, meaning that all of the patents in this folder have been directly transferred from Nokia to Microsoft.
The NEAR advanced operator is useful when we need to narrow down the scope of a patent search that focuses on a particular technology (or set of technologies) applied to a product.
We might be looking, for example, at patents that cover wireless communication technologies in mobile devices.
The first data field we could think of is TAC, which searches for keywords found together in either the title, the abstract, or the claims. The search query would be:
The search results retrieved 1,467,636 documents: 
A quick look at the first pages reveals that several documents are related to methods and systems that are not directly applied to a specific device.
Oftentimes, the device-related keywords appear isolated (mainly as examples of application). Moreover, their frequency within the text is much lower compared to the wireless-related ones.
We can take a look, for example, at patent US9496744B2 (Wireless charging optimization utilizing an NFC module that detects induced current and provides an indication of induced current): by using the Highlighter feature available in Patent Search, we can check how many times our keywords appear in the patent:
The disproportion is obvious. What’s more, by browsing the claims, we can spot where the device-related keywords are located:
Claim 14 cites some application examples of the technology, referring to an image of the patent:
Examples of such portable devices include (but are not limited to) an ultrabook, a mobile phone, a cellular phone, a smartphone, a personal digital assistant, a tablet computer, a personal computer (PC), a netbook, a notebook computer, a laptop computer, a multimedia playback device, a digital music player, a digital video player, a navigational device, a digital camera, and the like.
Utilizing the NEAR advanced operator can help us in obtaining more pertinent results. This advanced operator, in fact, allows us to specify the range of words within which the given keywords must appear.
The range is specified through the use of “?”, which we saw above. In this example, we chose a range of three words:
This time we have 299,534 results, which is a much more manageable workload than the original 1,467,636:
We can conservatively say that the patent documents obtained through this query are much more relevant to what we are looking for.
To confirm this, we can once again check whether the unwanted patent is included in the results or not by simply tweaking the query as follows:
As expected, the search does not being back any results:
It should be clear by now that these two advanced operators are powerful tools that we can leverage to reduce the “noise” in our patent searches, enabling us to not only to save time but also mitigate the risk of relevant patent documents ending up unnoticed due to being buried in the not-so-relevant results.
The way a patent search is carried out varies greatly according to several factors such as:
- The level of detail required;
- Time and budget constraints;
- The technology field involved.
More often than not, the same result can be obtained through different approaches: take your time to explore the data field and the syntax operators available.
Refining your search queries is one of the first steps towards optimizing patent searches. Other metrics—such as the Patent Quality and Value Rankings—can be used to further reduce the pool of documents to analyze, especially in State-of-the-Art or Clearance Searches. To discover how, download our free white paper: “Patent Quality and Value: Debunking the “All Patents Are Created Equal” Myth.
 In 2015, Microsoft acquired Nokia’s mobile business for $7.6 billion. The deal also included the related patent portfolio.
 For the sake of this example we won’t take into consideration the legal status of patents. This number, therefore, includes also expired patents or patent applications.
 Like before, we won’t take into consideration the legal status of patents.
 This extra step is needed because queries containing the WITH advanced operator can’t be enclosed within additional parentheses, or they won’t perform as intended. Another way to obtain the same result is to rely on the Combination feature in Patent Search.
 Once again, we won’t take into consideration the legal status of the documents listed.