Innovating in Patent Search: How IPRally Leverages AI with Google Kubernetes Engine and Ray
Customer Company Size
Startup
Region
- Europe
Country
- Finland
Product
- Google Kubernetes Engine (GKE)
- Ray
- KubeRay
- NVIDIA GPU Spot instances
Tech Stack
- Machine Learning
- Natural Language Processing
- Google Cloud
- Kubernetes
- Terraform
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Innovation Output
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Natural Language Processing (NLP)
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Software
- Professional Service
Applicable Functions
- Product Research & Development
- Business Operation
Use Cases
- Edge Computing & Edge Intelligence
Services
- Cloud Planning, Design & Implementation Services
- Software Design & Engineering Services
- System Integration
About The Customer
IPRally is a rapidly growing patent-search platform provider based in Finland. It serves global enterprises, IP law firms, and multiple national patent and trademark offices. The company is focused on transforming the patent search process by leveraging advanced technologies such as machine learning and natural language processing. IPRally's platform is designed to handle the increasing volume and complexity of patent documents, providing users with quick and accurate search results. The company is committed to innovation and efficiency, continuously improving its technology to meet the evolving needs of its customers. With a strong focus on research and development, IPRally is positioned as a leader in the patent search industry, offering a powerful and cost-effective solution for intellectual property professionals worldwide.
The Challenge
With millions of patent documents published annually and increasing technical complexity, traditional patent search tools require several hours of research to resolve a case. IPRally, a Finnish firm, aimed to tackle this problem by transforming the text from over 120 million global patent documents into document-level knowledge graphs embedded into a searchable vector space. This transformation allows patent researchers to receive relevant results in seconds with AI-selected highlights of key information and explainable results. The challenge was to build a system that could efficiently handle the growing volume of data and provide accurate, fast, and explainable search results.
The Solution
IPRally built a customized machine learning platform using Google Kubernetes Engine (GKE) and Ray, an open-source ML framework, to balance efficiency, performance, and streamline machine learning operations (MLOps). The company uses open-source KubeRay to deploy and manage Ray on GKE, enabling them to leverage cost-efficient NVIDIA GPU Spot instances for exploratory ML research and development. This setup allows IPRally to efficiently scale its operations and manage compute resources effectively. The platform is designed to handle massive preprocessing of data and exploratory deep learning tasks, providing a robust foundation for IPRally's advanced machine learning applications. Additionally, IPRally developed its own thin orchestration layer, IPRay, atop KubeRay and Ray, which provides a command line tool for data scientists to easily provision a templated Ray cluster that scales efficiently up and down. This self-service layer reduces friction and allows both engineers and data scientists to focus on their higher-value work, further enhancing the platform's efficiency and effectiveness.
Operational Impact
Quantitative Benefit
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