> For the complete documentation index, see [llms.txt](https://digimorph.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://digimorph.gitbook.io/docs/open-innovation/editor.md).

# Open-source Patent

### ***"A Computing Task Allocation System and Method for Distributed Inference in Large Language Models."***

This patent focuses on optimizing the inference process of large language models (LLMs) on **mobile devices** by utilizing **distributed task allocation, model segmentation, and linear optimization techniques**. The goal is to enable efficient, low-latency, and cost-effective inference across multiple edge devices.

This patent is based on the **February 2024 prototype and experimental results** from the **DigiMorph Lab team**, demonstrating real-world feasibility and performance improvements in decentralized AI inference. The **core content** of the patent is open-sourced in this repository, while the **full patent has been filed in Australia, the United States, and China in 2024.**

## The Value of This Patent for DigiMorph

As one of the foundational technologies for **DigiMorph**, this patent plays a critical role in shaping the platform’s approach to **AI-driven digital embodiment and decentralized intelligence**.

🔹 **Empowering Personal Digital Embodiments**

* Enables **low-latency, personalized AI agents** to operate on distributed devices without cloud dependency.

🔹 **Decentralized AI Infrastructure**

* Aligns with DigiMorph’s **Web3 and decentralized AI vision**, reducing costs and improving scalability.

🔹 **Integration with Web2 & Web3 Ecosystems**

* Supports AI agents functioning across **social media, gaming, blockchain-based identities, and dApps**.

🔹 **AI Computation Monetization**

* With DigiMorph’s **Proof of Interaction** mechanism, distributed AI inference could be monetized via **decentralized staking, AI compute credits, or token rewards**.

This patent serves as a cornerstone for DigiMorph’s **AI Agent evolution**, offering a **scalable, decentralized, and intelligent infrastructure** to support **multi-agent collaboration, real-world AI autonomy, and next-gen digital intelligence**.

## Key Innovations

**✅ Distributed Task Allocation for LLMs**

* Instead of executing LLMs on a single device, this method **distributes inference tasks** across multiple mobile devices, improving efficiency and reducing latency.

**✅ Advanced Network-Aware Task Scheduling**

* Implements **Mixed-Integer Linear Programming (MILP)** for optimal model partitioning, considering:
  * Model FLOPs, memory constraints, device processing capabilities
  * Network conditions such as **bandwidth, jitter, and packet loss**
  * **Binomial distribution modeling** for packet loss rate

**✅ Enhanced ONNX-Based Adaptive Model Deployment**

* Provides an **end-to-end process** for **model segmentation, conversion, and execution** on Android devices without relying on high-performance GPUs.

**✅ Robustness in Weak Network Conditions**

* Unlike traditional approaches that assume **high-bandwidth, low-latency environments**, this method incorporates **packet loss modeling and jitter adaptation**, ensuring reliable performance even in constrained network settings.

**✅ Optimized Communication Overhead**

* Introduces a **network link quality penalty term** to balance computation and communication trade-offs.
* Implements **effective payload calculations for communication protocols** to enhance transmission efficiency.

More Information: <https://github.com/DigiMorphLab/distributed-llm-inference>


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