Efficiently Manage Your Patent Portfolios Using Next-Gen AI Solutions
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:
- Scalability: As the volume of data increases, manual classification becomes an even bigger challenge.
- High Error Rate: Errors can occur due to a number of reasons such as fatigue, distractions, and inconsistent application of the classification criteria.
- Subjectivity and Bias: Individual prejudices, perspectives, and viewpoints may adversely affect manual patent classification.
- Standardization: Manual classification doesn’t have any standardized guidelines or criteria.
- Training and Expertise: To classify patent data effectively, you need trained classifiers with requisite domain knowledge and expertise, which is mostly lacking.
- Time and Cost: The manual patent classification process is costly and takes up a good amount of time, especially when performing complex classification tasks or dealing with huge data sets.
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). |
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.
