Canada: Cultivating Innovation In Agribusiness: Drafting AI Patents—Part 2

Determining Patent Eligibility

The core of many AI systems—mathematical algorithms—are generally not patentable across various jurisdictions. The mere implementation of these algorithms via computers does not inherently qualify them as patentable subject matter. This is particularly relevant for machine learning models, which at their essence, are complex mathematical algorithms.

Ensuring Novelty and Non-Obviousness

For an AI invention to be patentable, it must not only be new but also represent a non-obvious advancement over existing technologies. This criterion is critical when considering the application of machine learning models in agriculture, where the novelty often lies in the application rather than the technology itself.

Adequate Disclosure

A patent application must provide sufficient detail about the invention, ensuring that someone skilled in the field could replicate the AI system. Vague descriptions, such as the use of "any machine learning model," are insufficient for meeting the disclosure requirements.

Addressing Real-World Agricultural Problems

Illustrating how the AI technology solves specific, real-world problems in agriculture can help establish its patentability. This approach aligns with the concept of "Applied AI"—demonstrating the application of AI models to address tangible issues such as:

• Crop Yield Optimization: An AI system that analyzes aerial images and soil data to recommend precise planting patterns and crop rotations, significantly increasing yield per acre.
• Pest Detection: A drone-based AI monitoring system that allows for the early identification of pest infestations by analyzing crop images, allowing for targeted pest control measures and, ultimately, reducing crop loss.

Demonstrating Improvements in Computer Functionality

Even if the primary innovation does not lie in its practical application, an AI invention may still be patentable if it enhances computer functionality. This could include, for example, adaptations of AI models that:

• Integrate Low-Power Device Adaptations: An AI model that is designed for soil moisture sensors that operate on minimal power, enabling long-term, remote monitoring without frequent battery replacements.
• Process Speed Optimizations: An AI algorithm that rapidly processes data from field sensors that provide real-time analytics on soil health and subsequent adjustment recommendations, allowing for immediate improvements to farming practices.

Incorporating the Hardware Environment

AI models in agricultural settings often operate within specific hardware environments. Describing this hardware context can lend a physical aspect to the invention, aiding in overcoming patent eligibility hurdles. Relevant examples include:

• Soil Moisture Detection Sensors: Integration of AI with soil moisture sensors that help to predict irrigation needs, adjusting water delivery in real-time to optimize water usage.
• Drones for Aerial Imaging: AI-enhanced drones that process images on-the-fly to map out crop health and growth patterns, enabling precision agriculture practices.

Focusing on Post-Processing of AI Outputs

How the outputs of an AI model are utilized, especially if they effect a physical change or control in the agricultural domain, can further support the case for patentability. This might include using AI to:

• Automate Irrigation Systems: An AI system that analyzes output from various sensors to control irrigation systems, ensuring optimal water distribution based on the crop's stage of growth and soil moisture levels.
• Adjust Livestock Feed Schedules: AI applications that monitor livestock health and growth rates, adjusting feed schedules and compositions to maximize growth efficiency and health.

Detailing the AI Model

It is ideal to describe at least one implementation of the AI model in detail, rather than suggesting that any model could be used. This includes the architecture of the model, any unique configurations and how it is adapted for specific agricultural applications. Relevant examples include:

• Architecture for Disease Prediction: An AI model with a unique convolutional neural network architecture that is tailored to identify plant diseases from leaf images and other environmental signals, including layer configurations optimized for early detection.
• Configuration for Soil Analysis: An AI system that employs a specific arrangement of recurrent neural networks (RNNs) to predict soil nutrient deficiencies, detailing how each layer contributes to the accuracy of predictions.

Explaining Model Training

Detailing the training process of the AI model, including the type of data used and any unique pre-processing methods, can address disclosure requirements and highlight the novelty of the approach. Relevant examples include:

• Supervised Learning for Crop Prediction: Detailing the use of a supervised learning model trained on historical crop yield data and current climate conditions to predict future yields, including the selection and pre-processing of data to improve prediction accuracy.
• Training Data Selection for Pest Detection: An explanation of how unique datasets of pest images were curated and labeled, and the special pre-processing steps taken to enhance the model's ability to distinguish between pest species accurately.