Chapter 2 -- The 50-Day Plan For Building A Personal Assistant Agentic System (PAAS)

PHASE 2: API INTEGRATIONS (Days 11-25)

In this phase, you'll build the data collection foundation of your PAAS by implementing integrations with all your target information sources. Each integration will follow a similar pattern: first understanding the API and data structure, then implementing core functionality, and finally optimizing and extending the integration. You'll apply the foundational patterns established in Phase 1 while adapting to the unique characteristics of each source. By the end of this phase, your system will be able to collect data from all major research, code, patent, and financial news sources.

Day 17-19: Patent Database Integration

These three days will focus on integrating with patent databases to monitor intellectual property developments in AI and related fields. You'll learn how to navigate the complex world of patent systems across different jurisdictions, understanding the unique structures and classification systems used for organizing patent information. You'll develop expertise in extracting meaningful signals from patent filings, separating routine applications from truly innovative technology disclosures. You'll build systems to monitor patent activity from key companies and research institutions, tracking how theoretical research translates into protected intellectual property. You'll also create methods for identifying emerging technology trends through patent analysis before they become widely known.

  • Morning (3h): Research patent database APIs

    • USPTO, EPO, WIPO APIs: Study the APIs of major patent offices including the United States Patent and Trademark Office (USPTO), European Patent Office (EPO), and World Intellectual Property Organization (WIPO), understanding their different data models and access mechanisms. Create a unified interface for querying across multiple patent systems while respecting their different rate limits and authentication requirements.
    • Patent classification systems: Learn international patent classification (IPC) and cooperative patent classification (CPC) systems that organize patents by technology domain, developing a mapping of classifications relevant to AI, machine learning, neural networks, and related technologies. Build translation layers between different classification systems to enable consistent monitoring across jurisdictions.
    • Patent document structure: Understand the standard components of patent documents including abstract, claims, specifications, and drawings, and develop parsers for extracting relevant information from each section. Create specialized text processing for patent language, which uses unique terminology and sentence structures that require different approaches than scientific papers.

  • Afternoon (3h): Build patent monitoring system

    • Query recent patent filings: Implement systems that regularly query patent databases for new filings related to AI technologies, focusing on applications from major technology companies, research institutions, and emerging startups. Create scheduling systems that account for the typical 18-month delay between filing and publication while still identifying the most recent available patents.
    • Extract key information (claims, inventors, assignees): Build parsers that extract and structure information about claimed inventions, inventor networks, and corporate ownership of intellectual property. Develop entity resolution techniques to track patents across different inventor names and company subsidiaries.
    • Classify patents by technology domain: Create classification systems that categorize patents based on their technical focus, application domain, and relationship to current research trends. Implement techniques for identifying patents that represent significant innovations versus incremental improvements, using factors like claim breadth, citation patterns, and technical terminology.