Whether running a patent search tool or have a database of patents to use, we have seen that certain data enrichments that will make all the difference to your results. Data enrichments can significantly enhance the capabilities and effectiveness of an AI patent search tool. Some of the data enrichments that can make a difference to your AI patent search tool include:
- Patent Family Data: Patent family data provides information about related patents, including continuation and divisional applications, allowing for a more comprehensive search.
- Inventor and Assignee Data: Information about inventors and assignees can provide insight into the ownership and inventorship of patents, allowing for more targeted searches.
- Legal Status Data: Legal status data, including information about the expiration, renewal, and assignment of patents, can provide valuable information about the enforceability and availability of patents.
- Citation Data: Citation data provides information about the references cited in a patent, allowing for a more in-depth analysis of the state of the art and the technological landscape.
- Classification Data: Classification data provides information about the technical field of a patent, allowing for more accurate and focused searches.
- Keyword and NLP Data: Keyword and NLP data provide additional information about the content and context of a patent, allowing for more advanced and sophisticated searches.
- Historical Data: Historical data, including information about the examination process and prosecution history, can provide valuable insights into the patentability and validity of a patent.
In my view, data enrichments can significantly enhance the capabilities of an AI patent search tool, allowing for more accurate, focused, and comprehensive searches. By incorporating these data enrichments, businesses and patent professionals can make more informed decisions about their IP strategy and the patent landscape.
Search tools are only as good as their data
Having a data-rich patent search tool is a necessity for any patent slinger. However, acquiring all the pertinent details can be costly. The good news is that many commercial providers have figured out a way to make their data available for purchase. While it’s not the cheapest thing to do, the resulting database is both flexible and up to date. Moreover, the best providers can be contacted directly and will answer your query within minutes. Keeping in mind that a database is a living thing, you may want to keep a close eye on maintenance and updates.
It is also important to note that a data-rich patent search tool is no longer just a pipe dream. There are now more sophisticated solutions to keep your IP department running smoothly. Some of these solutions can be found on the open market, and others are designed specifically to meet the needs of your specific industry. Whether you are a patent slinger or an IP professional, the smartest way to stay on top is to keep up with the latest technologies. The best way to do that is to rely on vendors with a proven track record and a commitment to quality. Keeping this in mind, you can be sure that your patent search tool will be able to keep up with the competition.
Standardization and cleansing of the input data set
Whether you’re a patent attorney, a patent administrator or an IP aficionado, it’s important to take the time to properly prepare your input data set for your patent search tool of choice. This will ensure that you’ll receive a high quality set of results for your query. Additionally, you can expect your search results to be more relevant to your end users, as opposed to the search results you’ll get from a generic database.
Curating and maintaining multiple cleaned versions of your input data set is physically and financially prohibitive. That said, the most efficient way to cleanse your patent input data set is to create a standardized data set. This will not only help you to perform more accurate searches, but it will also make your patent search tool of choice more efficient in the long run. You should also consider storing your standardized patent input data in a cloud based database, as this will allow you to access your data from anywhere, at any time. This will also reduce your risk of having an outdated database, which can have a negative impact on the productivity of your team.
Lastly, you should also consider performing a comprehensive data validation and quality control process on your input data set. This will reduce the time it takes to get your patent search tool of choice up and running, while also helping you to avoid a major data leak that could have a catastrophic impact on your bottom line.
Matching of non-duplicate entries of the input data set to a business compendium
Whether you are a small startup or a global company, a robust, effective sanitation program can help prevent contamination and maintain product quality. However, poor personal hygiene can be a major factor in contamination. Ensure your employees are properly hygienic by setting and enforcing strict health and personal hygiene guidelines.
In addition, cleaning equipment and machinery before processing is essential. A sanitary work area must be free of clutter, unwanted materials, and unwanted documents. This should include a clean, safe, and hard surface that is not susceptible to sharp corners. In addition, equipment labels should be placed on the equipment, indicating that the equipment is clean and ready for use.
Raw materials must be accurately measured and weighed before they are dispensed. A qualified person must verify that yields are within acceptable limits. They must also record all checks, and ensure that all raw materials are stored under appropriate storage conditions. They should regularly check the accuracy of measuring devices and record all results.
Records should be written down, and retrievable in a timely manner. They should include initials and dates of operation. A change history should also be maintained. These records should be traceable by audit trails or secure user identification. These records should be retrieved and re-verified if any changes are made to production documents.
In addition, electronic signature systems must be validated to demonstrate that they are reliable and secure. The system must also have an audit trail to show that it is unique.
In addition, the FDA requires that all procedures be documented in writing. This is important for documentation purposes, as well as for tracing, because records must be available for inspection and review.
Commercial data providers are making their patent data available for purchase in BigQuery
Several commercial data providers have recently made their patent data available for purchase in BigQuery. This enables users to analyze and customize their data.
This is important for a variety of purposes. Whether researchers are looking at new patents or examining an existing application, access to patent information is necessary. Using this data, researchers can perform a wide range of analytics. For example, they can study the number of inventors on a specific patent or investigate the number of patents in an industry category. These data can also be used to personalize marketing campaigns.
The USPTO maintains a Patent Examination Data System, which allows researchers to view a large database of patents and other patent information. However, there are limitations to the system, including a limited number of querying options and a fixed set of data sources. In order to get the most from the system, researchers must understand their data sources and learn how to use them effectively.
Fortunately, researchers can access and analyze the data in BigQuery, which provides an easy-to-use interface and programmatic access. This can reduce the time it takes to find and analyze the data.
In addition to the web-based interface, BigQuery also offers REST APIs for Java and Python. These APIs allow users to perform machine learning-based models on structured data. The data can then be shared with other researchers, encouraging reproducibility. This can also allow others to build on the work done by a researcher.
For example, a researcher can upload data manually or in a batch file. This can be in CSV, JSON, or ORC formats. Then, they can write scripts and execute advanced queries. The results can be easily synchronized with other data sources, such as Google Analytics.
