The Internet of Things (IoT) market has grown quickly over the past half-decade and shows no indication it will slow down. With more devices being connected to the Internet all the time, the IoT has made an impact on everyone’s lives, whether they are aware of it or not.  Everyday items, such as refrigerators, thermostats and wearable devices are connected, as well as industrial machines, energy meters and automobiles.
The pervasiveness of the IoT across industries is illustrated by GrowthEnabler & MarketsandMarkets prediction that the global IoT market will grow from $157B in 2016 to $457B by 2020. The research suggests the top three sub-sectors will be smart cities, industrial IoT and connected health.

Why Obtaining IoT Patents is Important

IoT innovation has become a fierce battle ground, and as demand for these types of connected solutions grows, so too do the number companies joining the market. IoT solutions for B2B applications make up the majority of the market growth, despite the greater visibility of connected consumer products.
In a highly competitive space like this, differentiating your products or solutions from competitors can be challenging. So, when your company discovers a competitive advantage, it is essential to protect it from copycats and potential lawsuits. IoT patents can protect your company’s technology and secure the interests of employees, stakeholders and customers for years to come.
Obtaining IoT patents can increase the value of a company, which is especially important for startups that may not yet be profitable. In fact, obtaining patents increases the valuation of a startup between 23 and 29 percent and boosts the likelihood of first and second round funding by more than 50 percent. Also, many of these startups are being acquired by larger organizations, and data shows that companies with patents are 83 percent more likely to be acquired than those without patents.

Challenges to Patenting IoT Devices

The decision whether to patent an IoT device, or a system comprised of an array of devices, is not always straightforward. However, there are three typical IoT models we will discuss, where we see a lot of R&D and inventing, particularly where learning is involved. By ‘learning’ we mean the edge devices (102, 104, 106 etc.) that adapt to their environment based upon experience.
Model 100 is a “hub-and-spoke” IoT system. Every edge device communicates directly with the hub (108) over a network, usually the Internet. For many reasons this model remains commercially popular. For one thing, IP cores for Internet protocols and short- and mid- range wireless communication (e.g., Bluetooth LTE) have become quite inexpensive. If the environment in which the edge devices will function has reliable network connectivity throughout, and the data coming from the IoT devices doesn’t overwhelm the onramp to the network, the basic hub-spoke model 100 can often work just fine.

Model 200 is a hybrid model in which one of the IoT edge devices (104 in this case) serves as the onramp to the network and interface to the hub (108). The other devices send their data to this onramp device (104) and updates based on learning are propagated through the onramp device.
There are many examples of this model, such as a camera network in which some data reduction by the onramp device (104) takes place before communicating with the hub (108), or when the network connectivity is uneven in the deployment so that only one or a select group of the edge devices can reliably reach the network.

Model 300 is a “full mesh” architecture with some centralized functionality. The edge devices function as peers, largely independently of control from the hub (108).
Mesh architectures are popular for the decentralization and redundancy they provide compared to more centralized models.

Learning in any of these models can take three general forms:

  1. The edge devices can each independently learn, and share their learning through the hub (108)
  2. The edge devices send raw data to the hub (108), which learns from the data and sends behavioral updates to the edge devices (the hub 108 does the learning)
  3. Some combination of learning takes place on the edge devices and the hub (108). As an example, each edge device could perform some level of unsupervised learning based exclusively on its own inputs, while the hub (108) performs supervised learning across the inputs from all the edge devices and shares this learning globally.

The odds are good that your IoT invention utilizes some form of unsupervised, semi-supervised, or supervised learning, or a combination of these. Obtaining patent protection, or even if patent protection should be sought, will depend to some extent on the approach you take to IoT learning.
For example, if your approach is of the second kind (learning is centralized in the hub), your invention might be a candidate for trade secret protection. This is because it might be difficult for a competitor to independently develop your learning algorithms, or to discover them from the IoT interface to your hub. In that case, you have to weigh the tradeoffs of trade secret protection against the costs and benefits of patent protection.
If your invention uses learning of type (1) or (3), your algorithms might be easily discovered through reverse engineering. If they are valuable to your competitive advantage, then patent protection is probably your best bet for preventing competitors from taking your algorithms and using them in their own products.
Most hybrid mesh (200) or full mesh (300) IoT models will use some variant of type (1) or (3) learning, so they are strong candidates for patent protection. That’s not to say patent protection doesn’t make sense for other IoT models – it certainly does, but the option for trade secret protection is also there when the primary learning takes place at the hub and isn’t easily discovered.

Why TurboPatent is the Best Choice for Patenting IoT

TurboPatent has domain expertise in IoT devices, having drafted patents for several IoT clients. The patents have been for companies that produce IoT devices, as well as those whose technology facilitates IoT development. Our patent engineers possess the technical knowledge to understand complex IoT devices, allowing them to accurately capture the inventions. Additionally, TurboPatent employs repeatable processes, an engineer’s mindset and cutting-edge technology to draft patents in a way similar to how IoT designers work.

*Content Provided by FSP LLC, a patent law firm.