Currently, analytics tools for legal professionals focus on time management, billing, and keyword search. Most conventional legal databases span centuries across multiple jurisdictions, yet there is a notable lack of tools that leverage text analytics to aid in search and discovery. Manual research is time consuming and modern search tools are limited in scope and by an inability to consider content semantically similar to the search parameters.
The emergence of language understanding deep learning AI algorithms, and their ability to identify semantically similar content, is one of the driving forces behind the creation of Synapse. Synapse is connected to master document repositories such as PubMed and Patents, and is also designed to securely ingest and analyze user sources. Once a Synapse “Smart Table” is connected to these sources, deep learning agents can help explore the data and populate the table with their findings. Rather than relying on key word searches like a traditional search engine, Synapse drives discovery of semantically similar content. Start with terms describing the current case and quickly drill down on supporting cases, populating Synapse smart tables with dates, details and decisions. Synapse allows the users focus to return to the current case, deploying its deep learning agents to help automate legal research.
Read About Other Use Cases
Rare diseases are those that affect less than 200,000 people and, as can be expected, studying these elusive diseases can be difficult. Synapse allows efficient and deep exploration of massive amounts of text data that might otherwise remain hidden.
Automated identification of emergent technologies, patents, and companies from unstructured text can be challenging. Synapse can uncover even the most obscured similarities between technologies hidden deep in documents, making discovery efficient and effective.
Business intelligence focuses on the acquisition of huge quantities of data and leveraging that data into actionable insights. Synapse Radar and Trendlines enable deep, efficient research to uncover information hidden in text.