Djuripedia AI Framework & Patent – Decision Optimization in Diagnostics

From Human Diagnostics to Veterinary Intelligence

1. Introduction – Intelligence with Purpose

Before “AI” became a global buzzword, BraineHealth and founder Roger Svensson were already building an intelligent reasoning system that could think like a doctor.
The result: Diagnosio, a patented logic engine (U.S. Patent 11,908,579 B2) that doesn’t guess diagnoses — it determines the next most informative action.

Today, that same principle guides the Djuripedia AI Framework, extending the technology from human healthcare into veterinary medicine through research at Djuriverse Veterinary Clinic in Norrtälje.

2. The Core Idea – Diagnostic Inversion

Traditional AI lists probable conditions from symptoms.
The Diagnosio patent introduces inversion technology — an algorithmic method that evaluates every possible next step (question, test, observation) and recommends the one that will maximize diagnostic certainty.

This process mathematically “inverts” the diagnostic chain: instead of Symptom → Diagnosis, it computes the Derivative → Optimal Next Measurement.
The result is a reasoning engine that learns how to search the diagnostic space efficiently, not just classify data.

3. Scientific Roots – From KTH to Real World

The concept was validated at the KTH Royal Institute of Technology in 2018.
A neural-network model based on simulated patient data achieved up to 99 % accuracy in classification across 20 000+ cases, 6 000 diagnoses, and 200 000 variables .

BraineHealth later secured full U.S. patent protection in 2024, laying the foundation for a new generation of diagnostic reasoning tools.

4. Applying It to Veterinary Medicine

At Djuriverse, the patent framework is being adapted to animal health, where communication barriers and diverse species create unique diagnostic challenges .

Pilot Phases

Code Adaptation (3–6 months) – Re-implement MATLAB models with KTH developers.
Proof-of-Principle (100–200 cases) – Run pilot within Djuriverse Veterinary Clinic.
Scale Up (> 2026) – Expand to multi-clinic data integration and human-care crossover.

Goal: 99 % precision diagnostics that scale across species and conditions.

5. The AI Framework Today – LLM Orchestration

While the inversion engine represents Djuripedia’s long-term foundation, today’s system focuses on multi-LLM reasoning.
By calling trusted models like GPT 5, Gemini, and Grok through secure APIs, Djuripedia builds structured anamneses and bias-minimized case reasoning.

This approach forms the “AI Framework” — a layer that unites:

Conversational reasoning (AIVET PRO & Djuripedia bots)
Logical decision optimization (patent foundation)
Future data integration (DjuriLab & partner datasets)

Together, these elements evolve toward a complete decision-optimization engine.

6. Market Readiness and Partnership Potential

The Diagnosio patent is actively available for licensing or co-development through Vibrant IP, with applications extending beyond healthcare — into finance, engineering, and cybersecurity .

However, Djuripedia’s focus remains on veterinary care and preventive medicine — fields where faster, smarter reasoning can directly improve welfare outcomes.

For organizations exploring partnership, the signal is clear:

The underlying logic is validated, patented, and now being translated into real-world veterinary workflows.

7. Vision Forward – From Text to Context

The next generation of diagnostic AI won’t stop at language.
It will integrate visual, sensor, and environmental data — much like self-driving systems learn from multiple input streams.
By connecting inversion logic with contextual AI, Djuripedia aims to build a platform that can eventually reason from: