We have adapted our search technology to support complex search features required by our clients. Of particular note is our ability to support highly-performing Leading Wildcard and Matrix Queries.
A matrix query is particularly powerful when a user desires a dataset based on 2 or more dimensions. For example, rather than asking for all patents where the assignee is IBM, Intel, or Microsoft, the user might request all datasets where the assignee is IBM, Intel, or Microsoft, by year for the last 20 years. Other use cases include mapping term lists against assignees or
The conventional way of running such queries was to run each permutation – and depending on the number of dimensions and the number of possibilities in each dimension, this approach can quickly become intractable. Our innovative approaches to this challenge and many other complex search problems have given us the most powerful and scalable capabilities on the market.
For one of our clients, the ability to ask questions such as these was proving problematically slow on their resident Oracle search systems. By switching to our search and the Catalyst API, such searches became more than 100x faster – and we continue to extend and optimize performance to support even more complex analysis projects.
We participated in the PE2E (Patent End-to-End) pilot project last winter and provided a fully-realized patent and non-patent literature (NPL) search solution to the U.S. Patent and Trademark Office that we integrated into the non-search architecture developed by the prime contractor. We demonstrated world-class speed and real-time indexing, as well as the following features:
We also demonstrated how quickly and effectively our Catalyst architecture can be integrated/adapted for other implementations:
We believe this project serves as another example of the power and flexibility of our architecture and core solutions – and positions us very well for further integration and the delivery of client solutions.
Our client was looking at integrating patent data and analytics derived from patent data into existing internal systems. We provided an advanced API, including 20+ “patent markers” to help them make decisions about patent investment, acquisition, and divestiture. We integrated the record analysis into standard data operations to ensure that data was processed at index and data was always up-to-date, including all of the analytics fields.
We have provided custom data, data processing, and reports for several major academic institutions and continue to serve this client community by offering high quality solutions to enable research. In addition, we are committed to helping Universities and other institutions improve their management of intellectual property – and unlocking the economic potential of their patents.
We are currently working on an interesting project with Harvard University Business School on historical/geographic trends in patenting activity in the United States.