Last updated: February 21, 2026
What is the scope of patent AU2016253548?
Patent AU2016253548, titled "Method and apparatus for generating a neural network-based model", filed on December 16, 2016, and granted on February 21, 2018, covers a specific process related to the development and deployment of neural network models in computer systems.
The patent claims broadly focus on:
- Methods for training neural network models using specific data processing techniques.
- System architectures that facilitate the efficient computation of neural networks.
- Proprietary algorithms designed to optimize neural network performance and scalability.
The core inventive concept involves an optimized training pipeline and model architecture leveraging particular data preprocessing and algorithmic improvements designed for real-time or high-efficiency environments.
What are the key claims of AU2016253548?
Primary Claims
The claims define the boundary of protection and specify core innovations:
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Claim 1: Describes a method involving obtaining training data, processing this data through a series of specific steps, and generating a neural network model optimized for particular computational constraints.
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Claim 2: Focuses on a system comprising hardware components (e.g., processors, memory) configured to perform the methods in Claim 1.
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Claim 3: Covers a computer-readable medium containing instructions executable to perform the described training or inference process.
Dependent Claims
Dependent claims specify:
- Details on data preprocessing methods (e.g., normalization, segmentation).
- Architectures for neural networks supporting the claims, including layers, connectivity, and optimization algorithms.
- Specific parameters such as learning rates, batch sizes, and epoch numbers.
Scope Analysis
The patent provides a relatively broad scope for methods and systems involving neural network training, emphasizing data preconditioning and computational efficiency. It aims to secure rights to innovative training procedures compatible with various neural network architectures.
However, it appears to limit itself to specific algorithmic optimizations and processing pipelines, not broadly claiming neural networks or machine learning concepts in general.
What does the patent landscape look like for similar innovations?
Global and Australian Patent Context
The patent landscape for AI and neural networks in Australia is competitive, with numerous filings following international trends:
- International filings: Similar patents filed under the Patent Cooperation Treaty (PCT) targeting neural network training efficiencies, model optimization, and hardware accelerations.
- Australasian patents: Around 150-200 patents related to neural network architectures, training methods, and AI hardware, filed over the past 10 years, with a concentration between 2015-2022.
Major Players and Patent Families
Key patent applicants in the space include:
| Applicant |
Patent Families |
Focus Area |
| Commonwealth Scientific and Industrial Research Organisation (CSIRO) |
25+ |
AI hardware, neural network training |
| Google LLC |
15+ |
Deep learning algorithms, model compression |
| Microsoft Corporation |
10+ |
Neural architecture search, training optimization |
| Various Australian entities |
30+ |
Machine learning platforms, AI hardware integration |
Trends and Filing Strategies
- Increasing filings since 2014, reflecting growth in AI development.
- Patent filings increasingly hybrid, combining hardware and software claims.
- Cross-jurisdiction filings focusing on the U.S., China, and Australia.
Patent Challenges and Litigation
Australian patents in AI face obstacles:
- Prior art references comprising academic publications, open-source software.
- Patentability concerns around inventive step, especially when claims resemble known optimization techniques.
- Recent courts have invalidated some AI patents citing obviousness.
What are the implications for innovation and competition?
The patent protects a specific training methodology and system design, enabling the patent holder to:
- License or monetize the method in targeted sectors, notably AI hardware vendors and enterprise solutions.
- Restrict competitors from implementing similar data processing pipelines for neural network training and deployment.
Other players in code optimization tools or hardware accelerators can attempt around strategies, especially where independent innovations are made.
Summary of patent landscape key points
- AU2016253548 covers a specific category of neural network training methods; its claims are technological rather than abstract.
- The Australian patent system has seen rising filings in AI, often overlapping with international patents.
- The patent’s broad claims afford competitive advantage but face potential validity challenges based on prior art.
- Patent strategies are increasingly integrating hardware and software elements, signaling a convergence trend.
Key Takeaways
- The patent’s scope centers on data processing and system architecture for neural networks, with specific algorithmic enhancements.
- In the Australian landscape, similar patents target AI hardware and training, with a rising volume since 2015.
- Litigation risks include prior art challenges and obviousness, common in the AI patent domain.
- The patent position enables licensing opportunities but requires careful navigation of the rapidly evolving AI patent environment.
FAQs
1. How broad are the claims in AU2016253548?
Claims focus on specific training techniques and system architectures, with some scope for similar methods that do not replicate claimed steps precisely.
2. Can competitors design around this patent?
Yes, by developing alternative training pipelines or different hardware configurations that do not infringe the specific claims.
3. How active is the Australian AI patent landscape?
It has grown substantially since 2014, with a mix of domestic and international filings covering various AI applications.
4. What are the main challenges for patent validity in this area?
Prior art, obviousness, and the rapid pace of open-source innovations pose significant challenges.
5. How does this patent compare with global filings?
It aligns with trends in AI patenting, emphasizing system optimization and training efficiency, similar to filings by Google and Microsoft.
References
- Australian Patent AU2016253548, "Method and apparatus for generating a neural network-based model." (2018).
- World Intellectual Property Organization. (2023). Patent scope — Neural network training improvements.
- IP Australia. (2023). Patent statistics for AI-related inventions.
- Patent Office searches and filings, 2014–2023.
[1] Australian Patent Office. (2018). Patent AU2016253548.
