You’ve come up with a brilliant invention and are ready to patent it. The first step in the process is to conduct a prior art search to ensure that there is no existing patent for your idea. The relation between your invention and the prior art will determine whether the Patent Office awards your patent or fights issuance with rejection after rejection.
But you might not have considered using the prior art in an iterative process to refine the invention. Whether you find no prior art, some prior art, or anticipatory prior art, you can use the prior art to improve the invention and increase your chances of obtaining a patent.
Here are three ways inventors can leverage prior art to fine-tune their ideas and improve the quality of their enterprise’s patents.
PQAI stands for Patent Quality Artificial Intelligence. This simple, AI-powered search engine provides your innovation’s ten extremely relevant prior art references.
After you run your innovative idea through the PQAI engine, for inventors and other non-attorneys, the search results fall into one of three categories:
Regardless of which category your results fall into, the results give you valuable information about your next steps. Importantly, this is not a binary decision to either patent or abandon the idea. Instead, you can patent the invention or brainstorm and refine the idea to get onto the path to a patent.
In this article, you will learn a course of action for each category of results.
You face a dilemma if you do not get any relevant prior art references in a patent search. Your invention might genuinely be novel. If this is the case, your prior art search results might help the inventors develop new or related inventions to help your enterprise occupy the new field you have discovered.
Conversely, it could be that some relevant prior art does exist, but your search was flawed and failed to find those results. If you think the search results seem strange or inaccurate, you can refocus your search. In PQAI, you do this by:
For example, suppose you invented a coffee machine with a sensor to turn off the burner when the coffee carafe is empty. Then, as you review your search results, you might find that they go toward coffee machines that turn off the heating element that boils the water for the brewer when the water tank is empty.
Source: PQAI Prior Art Search Engine
These results are not relevant to the subject invention. However, to find out if there are closer prior art references, you can use the tools built into PQAI to refocus the search.
Suppose you run your search and find a prior art reference that precisely describes your invention. You might think these search results spell the end of your invention. But you can leverage these search results to continue innovating in the area and potentially create something new to be patented.
You can look at the later patents that cite your invention’s prior art reference and see how the concept has evolved. These forward citations might not necessarily tie directly to the original concept. They might have cited the references for some ancillary point. But these forward citations can describe one evolutionary path the invention took.
Within PQAI, you can use the save/open results functionality to nudge the system to find other related patents. This chain of patents might not have cited the original prior art reference, but they may illustrate similar concepts. Based on the references from forwarding citations and nudged results, you may find areas without prior art where you can tweak the invention.
Another option is to conduct a Google search to identify any products that ended up using the invention. This option can provide you with concrete applications for the invention that you might improve upon.
In the other direction, you may see that other enterprises have thoroughly covered the invention. So you could drop the idea altogether and move on to a different idea.
For example, suppose you invented a mobile phone with multiple cameras on one side of the phone. You could find results showing the exact concept and ideas built on it with 3D or infrared cameras.
Source: PQAI Prior Art Search Engine
Following the prior art chain that cited the closest reference or described concepts similar to the nearest reference, you can decide whether you have the space to tweak the idea or need to drop the idea.
If your invention appears novel and you think you got good search results, you sit in a solid position to get a broad patent. Patent lawyers often compare patents to plots of land. You want your plot of land to sit directly adjacent to your neighbor’s plot, with no gaps in between.
When you read through the prior art found in your search, you may find gaps between your invention and the patented inventions. You can expand your invention to include other embodiments or applications that surround your invention. This approach gives you broader patent protection and closes the gaps for a possible design-around.
You can nudge the search results in PQAI to ensure no on-point results by using the save/open trick described previously. This step will give you peace of mind that you have found all the relevant references.
Once you feel comfortable about your search results, you can save your results in PQAI and download the search report for review by a patent attorney.
Going back to the coffee maker example, suppose you only find references that cover turning off the boiling element, not the warmer under the carafe. You realize your original idea of sensing the weight to turn off the warmer was too narrow. You can now expand your invention to cover other ways of sensing an empty carafe, such as optical sensors that “see” when the carafe is empty and temperature sensors that sense when the carafe starts to burn.
You should view prior art searches as part of the innovation process. The search can tell you something about your invention no matter what prior art references you find.
As you review search results, you should remember to:
PQAI is a not-for-profit initiative focused on creating an open-source AI-based library of software components to accelerate innovation and improve patent quality. We believe that establishing an open-source forum of IP tools will drive critical changes in the IP ecosystem like what Linux did in computing. The PQAI search engine is one example of how such components can be assembled to create new and useful tools. The empowering of all inventors with advanced IP tools will drive more diversity and inclusion, which will accelerate the pace of innovation. PQAI believes in transparency and user privacy.
To learn more about PQAI and explore opportunities to improve system feedback, contact Sam Zellner, project lead for PQAI.
The Patent Office has many restrictions on the information it can disclose. In fact, through the 1990s, prior art searches could only be conducted in the Patent Office’s library. And the early version of the Patent Office’s database only used patent classification codes and did not allow full-text searching.
Things started to change in the early 2000s. The U.S. began publishing pending applications in 2001, opening the door to a wealth of information for inventors, litigators, and patent prosecutors. Gone were the days of hiring agents near the Patent Office to conduct your patent searches and pull your file wrappers. Instead, anyone with some training and patience could use the Patent Office’s database to obtain patent and patent application data.
We have now reached the next stage in the evolution of patent data tools. Developers have identified the strengths and weaknesses in the Patent Office’s interface. They have created proprietary and open-source tools to obtain, clean, and visualize patent data.
Open-source tools have several advantages over proprietary approaches, including:
These attributes encourage developers to adopt a standard instead of creating separate approaches. It also speeds development by allowing developers to “stand on the shoulders” of their predecessors.
Formerly, patent data was located in silos in the Patent Office. However, for the past 20 years, the Patent Office has made this information available. Some of the open-source tools designed to obtain patent data include:
PQAI stands for Patent Quality through Artificial Intelligence. This library of patent-related tools provides a next-generation prior art search engine. This search engine evaluates the search results and returns the top ten prior art references. In addition, the search engine trains itself to determine which results to return based on historical patent examination records.
PQAI promises to transform the IP landscape for inventors/enterprises, patent attorneys, and even patent examiners by delivering higher-quality search results. Conducting a search and reviewing mountains of search results takes time. Since PQAI only provides the most relevant prior art references, it provides more accurate, faster, and cheaper patent searches.
PQAI was initiated by the Georgia Intellectual Property Alliance (GIPA) and AT&T. GreyB contributed the algorithm, and InspireIP manages the application. As an open-source application, developers continue to improve PQAI. To review PQAI or contribute, you can access the files on PQAI’s GitHub.
The initiators of PatZilla call it “a modular patent information research platform and data integration toolkit.” Its primary feature is a search engine that pulls prior art references from the European Patent Office’s database. It also pulls from DEPATISnet, CLAIMS Direct, and depa.tech. In addition, PatZilla provides pdf, image, bibliographic data, and full test acquisition from these services.
PatZilla’s contributions to the evolving IP landscape include:
Andreas Motl authored PatZilla. But many developers have contributed to PatZilla since its initial release in 2014. To view the files for PatZilla or to contribute, go to PatZilla’s GitHub.
phpIP manages and dockets patents and other IP rights. The software was designed for inventors, enterprises, and IP law firms.
The system’s initiators sought to develop a software package that was flexible and easy to use. Unfortunately, most alternative packages were complicated and provided more features than necessary. As a result, most users paid for features they did not need and could not use the features they wanted.
phpIP was built on open-source software. It is changing the IP landscape by providing intuitive docketing and patent management tool. Notably, users can adapt the system to their specific needs. As they do, they can contribute to the overall improvement of the system.
You can view the documentation and source code files at phpIP’s Github.
Not every user who works with patent data will need to clean it. But occasionally, you will have a large file of patent or patent application data that does not have the correct format for your use.
In the past, users have relied on Excel or Open Office to clean data. But this often requires the user to manually fix each cell or have the programming knowledge to write a macro to fix the cells automatically.
Open Refine is a tool that automates patent data cleaning. It is an open-source tool that Metaweb Technologies, Inc, developed. It was acquired by Google and released for open use in October 2012.
Open Refine provides automated data cleaning functions that can be applied to large patent data files. Some of the features that apply to patent data cleaning include:
This tool can improve the speed and accuracy of the review, analysis, and storing of patent data. To contribute to Open Refine, visit the GitHub page.
Visualizations can help identify trends or patterns in the massive amount of patent data that may relate to your project. For example, you might benefit from a visualization of when patent applications were filed or which countries they were filed in.
Until recently, you would need to comb through a spreadsheet to spot patterns in the data. Now, there are tools to turn patent data into visualizations, including:
Gephi is a network visualization platform that can create graphs showing relationships between patents or patent applications. It is an open-source application that is free to use. Association Gephi authored the software, but many developers have contributed to it.
Gephi can convert CSV or Excel files into data visualizations. This means you can import a file from The Lens or a cleaned file from Open Refine (both discussed above). Gephi will then create a visualization of the data.
This will change the IP landscape by revealing obscure or hidden patterns in the data. For example, you can visualize the number of pending applications in the data file that belong to each assignee.
To view the source code for Gephi or participate in its development, visit the Gephi GitHub page.
Plotly Chart Studio is an open-source platform that can be used to create interactive graphics. The open-source version of Plotly is cloud-based. This version is free to use. Plotly also offers enterprise versions for a fee.
Plotly creates graphs from data files generated through The Lens or Open Refine. Like Gephi, Plotly can help spot trends or patterns in the data. But unlike Gephi, the graphs in Plotly were designed to be interactive and shareable. This makes Plotly a valuable collaborative tool that will alter the IP landscape.
Plotly was developed by Plotly Technologies, Inc. You can help develop Plotly by reviewing the source code and documentation on Plotly’s GitHub.
The open-source nature of these tools almost guarantees that they will continue to develop and improve. To be a part of these opportunities, you can either use the software and provide feedback or you can collaborate with the developers to identify and create new features for these applications.
Get in touch with Sam Zellner, project lead for PQAI, to explore collaboration opportunities with PQAI.
©2024 Project PQAI | All Rights Reserved
