AI-driven Patent Categories – Use of explainable AI for Innovation / Ideation
Patent and publication 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 in gaining 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 for categorization.
Important Parameters for Categorization
Classification based on the class codes is one of the more widely known methods. These class codes are globally recognized and standardized based on geography. However, class code-based categorization misses out on other important parameters like semantics and concepts.
The advancement of technology has paved the way for automation in patent categorization methodologies. AI is making its way through the intellectual property and innovation spaces. There are many tools that use technologies like artificial intelligence, machine learning, and natural language processing (NLP) to expedite the patent categorization process.
XLSCOUT offers modules that assist in innovation and R&D workflows, equipped with NLP and machine learning capabilities as an AI-based platform. The advanced BERT technology enables the tool to read through textual data, analyze it, and then categorize it into technology and sub-technology domains.
XLSCOUT’s Techscaper has capabilities that allow users to categorize large portfolios into system-generated or user-generated categories based on their requirements.
The XLSCOUT Techscaper Provides 3 Ways to Get Landscape Analysis
1. 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 that is set on XLSCOUT. The XLSCOUT corpus, which is machine-trained, assists the taxonomy with 3 billion-plus words.
Takeaways:
- Quick Landscape study
- Automated reports
2. 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.
Takeaways:
- We used trained data to create a taxonomy
- The taxonomy formed is relevant and customized to the client’s needs
3. Hybrid Landscapes
The hybrid model allows the creation of taxonomies using both machine and manual categorization. The information comes from a variety of reliable sources. These can be patents, market data, scientific literature, and others.
Takeaways:
- Interim Updates are provided
- Expert Taxonomy augmented by AI
- Maintain Transparency and high-quality
Need of the hour
Explainable AI: When your job depends on AI, it better not be a black box, and the use case should be adequately explained and explainable. XLSCOUT solutions are explainable and do not have a 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 optimize the decision-making process.
Users can visualize the insights through an interactive analysis tab by studying the result set using visualizations that are shareable.
With capabilities ranging from search assistance to insight generation and visualizations, XLSCOUT is a one-stop solution for patent categorization.
