AI-driven Patent Categories – Use of explainable AI for Innovation / Ideation
Patents and Publications data is crucial for R&D teams. It gives tremendous insights on the market front, competitor trends and innovation, technology trends, and others. However, patent data in its raw form is quite bulky to manage. In order to get the most out of patent data, it becomes essential to categorize, process, and visualize it.
Not only does patent categorization help in organizing patent data, it also assists in accelerating the analysis process.
Hence patent categorization can aid businesses to gain quick insights from the patent data. However, categorizing such large “patent data sets” manually can be a tedious and extensively time-consuming task. Categorizing patent data sets brings along a few challenges of its own.
The main challenge is to decide the criteria of categorization.
Important parameters for categorization
Classification based on the class codes is one of the widely known methods. These class codes are globally acknowledged and standardized based on the geographies. However, class code based categorization misses out on the other important parameters like semantics and concepts.
The advancement in technology has paved the way for automation in patent categorization methodologies. AI is making its way through the Intellectual property and Innovation space. There are many tools that use technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing (NLP) to expedite the Patent Categorization process.
XLSCOUT is an AI based platform equipped with NLP and Machine learning capabilities that offer modules assisting in innovation and R&D workflows. The advanced BERT technology enables the tool to read through textual data, analyze them and then categorize them into technology and sub-technology domains.
XLSCOUT’s Landscaper module is equipped with capabilities to categorize large portfolios into system-generated or user-generated categories as per the user’s requirements.
The XLSCOUT Landscaper provides 3 ways to get landscape analysis
Instant Landscapes fully made by AI
Within this segment, the machine applies intelligence and categorizes patents into auto-generated categories. This categorization is done based on the expert search logic which is set on XLSCOUT. The taxonomy is assisted by the 3 billion-plus words on the XLSCOUT corpus which is machine trained.
Quick Landscape study
Trained Taxonomy Landscapes
Trained Taxonomy Landscape allows users to use training data in conjunction with the corpus to create the expert taxonomy. This trained data can be unique to the client which helps in generating focused and relevant landscapes.
After an expert taxonomy is fed to the system, the machine then applies NLP to cluster down patents into relevant categories.
Trained data was used to create a taxonomy.
The taxonomy formed is relevant and customized to the client’s needs.
The hybrid model allows the creation of taxonomy using machine and manual categorization. The information comes from a variety of reliable sources. These can be patents, market data, scientific literature, and others.
Interim Updates are provided.
Expert Taxonomy augmented by AI.
Transparency & High quality is maintained.
Need of the hour
Explainable AI: When your job depends upon AI, it better not be a Blackbox, and the use case should be adequately explained/ explainable. XLSCOUT solutions are explainable and do not have black box orientation.
Machine Learning based Corpus: A Corpus that continually learns from users is the need of the hour.
Patent categorization enables quick insight generation and helps in optimizing the decision making process.
Users can visualize the insights through an interactive analysis tab by studying the result-set using visualizations that are sharable.
With capabilities ranging from search assistance to insight generation and visualizations, XLSCOUT is a one-stop solution for patent categorization.
For more information on how XLSCOUT can help in strategizing decision making through its advance algorithms, reach out to us at firstname.lastname@example.org