Identifying Emergent Companies and Patents
The focus of business intelligence is on the acquisition and storage of large quantities of data and leveraging that data into actionable insights. One of the most abundant sources of valuable data is unstructured text, but it has proven difficult to mine effectively. Automated identification of emergent technologies, patents, and companies from unstructured text is challenging due to the inability of modern systems to identify semantic similarity and grammatical structure.
The emergence of deep learning AI algorithms as a driving force in text analytics is one of the driving forces behind the creation of Synapse. Synapse is connected to pre-modeled master sources such as PubMed and Patents, and is also designed to securely ingest and analyze user text 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. This minimizes the need for manual search and data entry, and also reduces the need for subject matter expertise. Search through constantly updated databases of patents technologies, identify synonymous technologies, and expand your search to include contextually similar terms.
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.
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.
Manual research is time consuming and modern search tools are limited in scope. Synapse utilizes its understanding of semantics to drive efficient discovery and allows the focus to return to the current case.