CV

Paul Hanchett – Research CV

AI-Human Collaboration Research | Generic User Framework

Portland, OR | paul@paulhanchett.com | @paulha1951 | Research Collaboration Welcome

Research Focus

Foundational research in AI-human collaboration

Developing foundational research in AI-human collaboration through three complementary papers. While the Generic User Framework has working infrastructure (research in process!), the Memory Agent and Persistent Cognitive Architectures papers explore broader theoretical implications with potentially greater impact on the field. This work bridges practical engineering with cognitive architecture research.

Current Research Results

AI Browser Extension Interface (AIBE) – Working infrastructure that captures “what users see and do” to train AI to operate as a human user would

• Starting infrastructure built and tested with comprehensive validation of core functionality:

  • Observer channel: Real-time capture of human browsing interactions

  • Actor channel: Foundation for AI systems to interact with web interfaces

  • Session-based architecture supporting multiple concurrent research scenarios

This tool enables an AI to watch a human at work on real websites, doing real work, learning the patterns of their actions. Later, the tool also enables the AI to emulate what the human did, to drive the browser in the same way and see the result.

Linguistic Structure Observation – Breakthrough insight that browser interactions naturally form hierarchical language:

• Individual actions (clicks, keystrokes) form “words” with screen updates serving as breaks between words

• Interactions on a single page combine into “sentences,” terminated by navigation to another page

• Site workflows become “paragraphs” – sequences of sentences within a single site

• Complete task sequences become “stories” – from start of task to task completion

This understanding helps an observer to assign meaning to web actions without regard to semantics, and to form hypotheses about how they are joined together to accomplish tasks.

Function Model Observation – Framework for AI systems to learn website “functions” and compose them for complex tasks. Pages accept inputs, transform them, and produce outputs, just like functions.

• Cross-site pattern recognition and transfer learning

• Hypothesis-driven exploration of unknown information sources

• Path planning through function composition

Functions, inputs and outputs become a toolkit that can possibly be assembled to get desired outputs from the available inputs.

Research Collaboration Opportunities

Seeking active partnerships to advance from foundational infrastructure to working AI systems:

Academic Collaborations

• Paper co-authorship, endorsement, and peer review partnerships

• Conference presentations and academic venue submissions (remote/virtual participation preferred)

• Cross-institutional research validation and expansion

Technical Development Partners

• AI integration layer implementation

• Advanced cognitive architecture development

• Cross-platform expansion and validation

Industry Research Partnerships

• Practical AI-browser interaction applications

• Enterprise-scale validation and deployment

• Real-world use case development and testing

Funding and Institutional Support

• Research continuation and team building

• Advanced AI infrastructure and compute resources

• Long-term cognitive architecture research programs

Professional Engineering Background

Industry Experience Overview

25+ years of solving complex technical problems in aerospace, telecom, security, and open-source technology sectors. Patent holder with track record of delivering high-impact solutions that save organizations significant time and resources.

Design Philosophy: Build from desired result backward toward available starting point, while tackling highest-risk elements first.

Key Professional Achievements

Blue Origin (2020–2022) – DevOps Consultant/Senior Software Engineer

• Develop and maintain test and support software for rocket engine development

• Integrate Helix QAC for code quality improvements across development teams

• Migrate Python Flask applications to Kubernetes, reducing maintenance overhead significantly

Ericsson (2019) – System Integration Engineer IV

• Develop Python-based diagnostic tools and automation scripts

• Reduce new system setup time from weeks to one day

• Facilitate on-time delivery of telecom network management solutions

Intel SSG OTC (2016–2018) – Web Applications Architect IV

• Build Python automation tools for open-source technology initiatives

• Save hundreds of administrative hours through Jira and JAMA data analysis automation

• Streamline project workflows for large-scale technology development

McAfee (1999–2004) – Product Architect/Technical Lead/Senior Software Engineer

• Architect security products saving estimated $1M in re-engineering costs

• Lead technical teams through complex system design and optimization projects

• Granted two patents as inventor (6,834,301 and 6,983,486) for large-scale network instrumentation

Tektronix, Inc. (1974–1985) – Project Manager and Senior Hardware/Software Engineer

• Engineer and manage development of many custom signal processing systems (up to $8M value)

• Analog and digital electronic design, including PDP-11 software and debuggers

• 100% on-time and on-budget delivery record over 11 years

• Lead teams of 1-30 people on complex hardware/software integration projects

Technical Expertise

• System Architecture: More than 25 years designing and optimizing complex systems across multiple industries

• Programming: Python, Java, C++, JavaScript – focus on automation and system integration

• DevOps & Infrastructure: Docker, Kubernetes, Jenkins, CI/CD pipelines, cloud deployment

• Electronics Engineering: Analog/digital design, signal processing, hardware/software integration

• Research Methodologies: Cross-domain problem-solving, engineering approach to AI systems

• Technical Leadership: Mentoring and leading technical teams in high-stakes environments

Research Methodology and Approach

“Component’s Eye View” Engineering Philosophy

Understanding complex systems, from each component’s perspective, by asking “How would I accomplish what’s being asked of me?” This electronics engineering approach has proven applicable to AI cognitive architecture design.

Cross-Domain Pattern Recognition

Apply proven methodologies from electronics engineering to AI system design, bringing a different perspective to cognitive architecture research.

“Outsider Advantage” in AI Research

First encounter with AI systems in April 2025 – no preconceived academic notions, enabling fresh perspectives on fundamental problems in AI-human interaction.

Extensive Use of AI to produce this collaboration

Publications and Research Output

Papers Ready for Submission

• “The Generic User Approach: Training AI to Navigate Browsers as Humans Do” (with Claude/Anthropic) – Foundational paper establishing dual-AI architecture with specialized components, concrete data structures for browser interaction capture, and hybrid training methodology

• “The Memory Agent: Enhancing Browser Automation with Contextual Awareness” (with Claude/Anthropic) – Extension to GUA framework addressing temporal dimension of web interaction through dedicated contextual awareness system and cross-session learning

• “Beyond Sequential Processing: Toward Persistent Cognitive Architectures for AI Systems” (with Claude/Anthropic and Grok/xAI) – Framework for persistent cognitive processing that enables background insight generation and accumulated expertise development in AI systems

These papers are now available here: http://paulhanchett.com/research

Technical Documentation

• AIBE System Architecture – Complete technical specification available for research collaboration

• Observer/Actor API Design – Interface specifications for AI-browser communication

• Test Suite Validation – Comprehensive testing framework demonstrating system reliability

Note: All research materials are available for collaboration and peer review

Education and Professional Development

MBA – Marketing & HR Management, University of Phoenix
BS – Business Management, University of Phoenix

Patents and Intellectual Property

Inventor of two McAfee patents for large-scale network instrumentation management:

• Patent 6,834,301: Network management system architecture

• Patent 6,983,486: Advanced network instrumentation methods

Professional Interests

Amateur Radio Operator (AI7JR) with deep electronics background providing unique perspective on AI system limitations and capabilities.

Contact and Collaboration

Research Collaboration: paul@paulhanchett.com Social: @paulha1951 (X/Twitter) Research Papers: Available at paulhanchett.com/research Technical Documentation: AI Browser Extension Manual

Research Statement Summary

“Always willing to share what I think I know with others. I’ve developed infrastructure ideas for AI-human web interaction research, applying constraint-based engineering thinking to cognitive architecture problems. The work represents a starting point rather than finished solutions – collaboration with AI researchers could help explore what these approaches might offer the field.”