The R&D business function is one of the most significant and pivotal departments in an organization, as it allows organizations to keep up with ever-changing technological innovations. Imagine a company without an R&D function. Corporate R&D operates in unexplored technology domains that are complex, difficult to understand, and sensitive, making it even more difficult to measure performance parameters. However, an organization needs to measure such KPIs and ensure the sustainable development of the innovation powerhouse.
How Patent Data Helps Corporate R&D?
Patent data is an important enabler for corporate R&D departments, as it allows them to understand the depth of the subject matter and the existing work done in the domain. The process of streamlining corporate R&D involves understanding the existing domain and innovating in the organization’s area of operation.
However, the volume of patent literature available across the globe is enormous, and categorizing the data into relevant buckets might be a challenging task. The ever-increasing amount of data also reduces the chance of finding that insightful information in the clutter.
The patent data is accessed and utilized by two of the most important business functions.
1. Corporate R&D Strategy
Access to the patent data allows teams to plan the innovation strategy effectively and reduce the chances of duplicity. The availability of existing invention disclosures opens avenues for new idea generation and helps align the idea with trending technology and potential product technology.
2. IP Strategy
Access to patent data enables IP development, technology transfers, in-licensing, and more, and helps in taking business-centric decisions.
Hence, it is very important to acknowledge the role of patent literature analysis and insight mining. Manual processes could be a way to go; however, considering the volume of patent literature, this might not be the most efficient way.
Technology has opened new avenues in the data analysis and visualization space as the Natural Language Processing (NLP) equipped algorithms are capable of generating technical concepts around the textual data and allowing users to categorize it as per their areas of interest.
AI and ML Assisting NLP-equipped Searches
Artificial intelligence and machine learning have added another dimension to NLP-equipped searches. AI allows the tools to analyze the textual data in a fraction of a second and create concept-based clustering algorithms. These tools are equipped with custom categorization, where users can feed their custom taxonomies and the tool can learn the broad categorization parameters.
Though AI-based tools cast a broader net on the patent literature and collate data from relevant databases, they can never replace human intelligence, especially in the corporate R&D and IP domains. These tools can assist human intelligence with the knowledge of the subject matter in making informed decisions and streamlining their business strategies.
Here’s a comprehensive but not exhaustive list of features where AI can outsmart human interventions in IP and corporate R&D spaces:
- Accelerate patent data analysis.
- Auto-categorize patent documents for optimized analytics.
- Create data visualizations.
- Enable a first-pass idea validation.
XLSCOUT, an AI-assisted search and analytics tool, offers function-specific modules that make patent searches and other IP workflows more efficient. These can be easily integrated with existing systems to further streamline the processes.