All Technologies Used
Motivation
The project aimed to process massive volumes of unstructured patent and biological sequence data while ensuring high-quality metadata, scalable processing, efficient retrieval, compliance with global IP standards, and automation of labor-intensive workflows. It focused on enabling actionable insights, faster discovery, and improved efficiency for researchers and IP analysts.
Main Challenges
Patent documents and biological sequences were in multiple formats (PDF, XML, FASTA, GenBank) and updated continuously. Processing terabyte- to petabyte-scale datasets required large-scale ingestion, normalization, cleaning, and indexing pipelines capable of handling diverse and unstructured data.
Many records lacked standardized metadata, hindering search, classification, and contextual understanding. Relationships between sequences, annotations, and patent claims were often lost during manual processing, reducing analytical value and complicating compliance and reproducibility.
Annotation, summarization, and monitoring were labor-intensive, error-prone, and difficult to scale. Researchers spent significant time curating data, tracking updates, and maintaining quality control, limiting overall operational efficiency.
The client required a modular AI system capable of automating metadata enrichment, semantic search, intelligent summarization, and workflow automation. The solution had to integrate with existing pipelines, support cloud and on-premises deployment, and be flexible for future AI-driven capabilities.
Key Features
- Automated AI Workflows: Comprehensive AI-driven workflows including patent interpretation, sequence annotation, summarization, and metadata enrichment. Modules like AI Assistant and AI Summary reduce manual curation and accelerate research.
- AI Dataset Analysis & Enhancement: Automated cleaning, clustering, deduplication, and enrichment of large biological datasets using machine learning, NLP, and vector similarity search. Ensures structured, high-quality, and actionable data for researchers.
- Semantic Search & Discovery: Advanced search capabilities combining Elasticsearch and vector databases, enabling semantic search, similarity-based retrieval, and context-aware exploration of patents and sequences.
- Error Detection & Quality Assurance: Continuous anomaly detection, validation of metadata consistency, template recognition, and quality reporting to maintain high data reliability and compliance with IP standards.
- Flexible Deployment: Modular platform supports both cloud (AWS S3, RDS, Step Functions) and on-premises (MinIO/PostgreSQL) deployment, allowing customization for different research environments and scalability for large datasets.
- Performance Monitoring: Real-time dashboards, workload monitoring, dynamic resource scaling, and operational analytics ensure system stability and transparency for administrators managing large-scale data pipelines.
- Integration with Existing Pipelines: Seamless connection with BLAST, sequence alignment tools, patent databases, and existing IP analysis pipelines for end-to-end workflow automation.
- AI-Powered Summaries and Insights: Automatic generation of domain-specific summaries for patents and sequence alignments, highlighting key entities, relationships, and potential IP implications, significantly reducing researcher workload.
Our Approach
Project Impact
Massive Data Processing: The platform successfully processed over 50 million patent documents and biological sequences, enabling scalable analysis of terabyte- to petabyte-scale datasets.
Reduction in Manual Work: Automated annotation, summarization, and metadata enrichment reduced manual effort by 72%, freeing researchers and IP analysts to focus on higher-value tasks.
Enhanced Search Accuracy: AI-driven semantic search and enriched metadata improved search accuracy and result relevance to 91%, enabling faster discovery of patents and sequence similarities.
Accelerated Research Workflows: AI-generated summaries and automated insights significantly reduced the time required for patent analysis and sequence interpretation, accelerating scientific research and IP evaluation.
Operational Transparency: Real-time dashboards and performance monitoring provided administrators with complete visibility into data volumes, workflow progress, and system performance, ensuring stability during large-scale processing.
Actionable Insights: Researchers and IP analysts gained structured, interpretable data with AI-enriched annotations and contextual relationships, enabling faster decision-making and more accurate IP analysis.