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Efficiently Manage Your Patent Portfolios Using Next-Gen AI Solutions

Jul 24, 2023
By Mamata Saha

Having massive patent data sets necessitates data management, which is essential to know the variety of technologies your patent repository holds and their areas of application, among other details. However, patent data management can pose challenges when done manually, especially if the portfolios run into thousands, perhaps even more. In such situations, advanced AI tools can be of immense help in classifying these data sets into specific categories. A tool that saves time, resources, and money is the need of the hour for all organizations.

Also, key challenges encountered by analysts while performing manual classification of patents include the following:

This is where Relecura’s AI Classifier steps in and helps to automatically sort massive data sets into defined buckets, using Machine Learning and Deep Learning technologies. 

The tool classifies large data sets in hours with higher accuracy levels, thereby reducing the total cost of ownership (TCO).
Fig 1: This image outlines the AI Classification workflow followed by the Classifier, along with some relevant use cases.

As evident in the above image, patent documents concerning any of the categories mentioned under ‘Input’ such as Business, Private, Legal, Technical, or Scientific, serve as input for the tool. Using Machine Learning, Deep Learning, and Generative AI, the output is generated in the form of classified patent data sets. Additionally, Patent Portfolio Management, Competitive Intelligence, and Technology Landscape Analysis are other critical tasks the tool can perform.

Model creation, which is the starting point of the classification process, requires the user to input a few training documents and train the engine. Once that’s done, the created model can be used to classify a larger set of patents. The core steps used to train ML models are outlined below:

Key steps in training ML Models:
Model Architectures: Selecting and designing appropriate models.
Data Augmentation
Feature Engineering: Selecting, transforming, or creating new features from the available data.
Hyperparameter Tuning: ML models often have hyperparameters that control their behavior, such as learning rate, regularization strength, or tree depth.
Periodic Model Evaluation and Refinement

In summary, it’s clear that for organizations owning patent data sets that are challenging to keep a track of due to their sheer volume, the AI Classifier is the right way to go.

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