The technology industry continues to be a hotbed of patent innovation. Activity is driven by the increasing demand for AI-powered applications across industries, the need for more accurate and reliable AI models, and the availability of vast computing resources, as well as growing importance of technologies such as deep learning algorithms, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. AI innovation in neural net architecture aims to improve the performance, scalability, and interpretability of AI models, thereby unlocking new possibilities and driving advancements in various domains. In the last three years alone, there have been over 1.5 million patents filed and granted in the technology industry, according to GlobalData’s report on Artificial intelligence in technology: neural net architecture. Buy the report here.
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However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilizing and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
185+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models built on over 1.6 million patents, there are 185+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, quantum AI, GenAI for coding and emotion AI are disruptive technologies that are in the early stages of application and should be tracked closely. Biological computing models, defect detection models and circuit designing AI are some of the accelerating innovation areas, where adoption has been steadily increasing.
Innovation S-curve for artificial intelligence in the technology industry
Neural net architecture is a key innovation area in artificial intelligence
Neural net architecture refers to a collection of algorithms and methodologies employed for the creation of artificial neural networks. These networks consist of interconnected nodes, referred to as neurons, organised in layers. Each neuron undertakes a mathematical computation to convert inputs into a single output. The output from one neuron can be relayed as input to subsequent neurons in the following layer, enabling further data processing. This iterative process continues until the desired outcome is attained, completing the neural network's computation.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 120+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of neural net architecture.
Key players in neural net architecture – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of applications identified for each patent. It broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of countries each patent is registered in. It reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to neural net architecture
Source: GlobalData Patent Analytics
Apple is one of the leading patent filers in neural net architecture; others include Alphabet and Intel.
Samsung Group is another prominent patent filer in neural net architecture. One of the company’s patents focuses on an electronic apparatus comprising storage for object data and kernel data, as well as a processor consisting of multiple processing elements arranged in a matrix formation. The processor is designed to input specific elements from the object data into processing elements in a row and sequentially input multiple elements from the kernel data into the same row to perform operations between them. It is also configured to determine the depth at which a non-zero value exists in the first and second elements and input them into a calculator in each processing element to perform a convolution operation.
Other prominent patent filers in the space include Cambricon Technologies and Qualcomm.
By geographic reach, Groq leads the pack, followed by Alphabet and Cambricon Technologies. In terms of application diversity, Apple holds the top position, followed by Samsung Group and Alphabet.
Artificial intelligence (AI) innovation in neural net architecture focuses on developing advanced and efficient models inspired by the structure and functioning of the human brain. This involves designing neural network architectures that can effectively process and analyze complex data, enabling tasks such as image recognition, natural language processing, and decision-making.
To further understand how artificial intelligence is disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI).
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
