--

Days

--

Hours

--

Mins

--

Secs

Ends On

If you haven’t dabbled in artificial intelligence yet, you may seem a little behind the times. There's a big difference between announcing plans to use artificial intelligence and actually using it, but according to some media reports, a recently published patent application from Shimano describes the use of trainable machine learning programs to automate control electric mountain bike shocks and dropper posts.

Shimano shift system

Of course, automatic control of shock absorbers is nothing new thing. Fox Live Valve, RockShox Flight Attendant, and most recently SR Suntour’s TACT suspension products have been automatically adjusting shock damping for years, with varying degrees of success. However, the programming behind the functionality of these products is relatively fixed, and users cannot provide feedback to the system about its performance. It cannot "learn" the user's preferences.

Shimano describes in U.S. Patent 11866114 B2 is a system that can automatically adjust suspension behavior and seat tube and saddle position on the fly, and can also be "trained" by the user to perform at a given race. Maximum performance on the road.

The device includes a comprehensive data acquisition system with many sensors that measure many parameters related to the way the bike is ridden and the type of terrain. Speed, cadence, torque, acceleration, tire pressure and brake usage, as well as the ebike's yaw, roll and pitch. There are also accelerometers on the shock absorbers, which record information about how well the suspension absorbs wheel forces. There is also a camera on the front.

All of this information is fed into a control unit, which itself is connected to various electric motors or solenoids that are able to adjust spring rate, damper position, stroke length, lockout, seatpost height, and saddle Location etc.

What's unique about Shimano's patent is that the program can learn and change itself accordingly as it collects more and more data, which is considered "training data" in artificial intelligence and machine learning. Moreover, some of this data comes in the form of direct user feedback.

While Shimano makes Koryak dropper posts under the PRO Components brand, it does not currently make shocks. So where do they patent machine learning methods for automatically controlling suspension adjustments? It might just be a development tool that Shimano uses to support its racing teams. As for whether it can be seen on the market in the future, it cannot be determined at present.

 

Leave a comment

Please note, comments need to be approved before they are published.

This site is protected by hCaptcha and the hCaptcha Privacy Policy and Terms of Service apply.

Latest Stories

View all

V-Adapt™: Adaptive Pedal Assistance Technology

V-Adapt™: Adaptive Pedal Assistance Technology

V-Adapt™ is VTUVIA’s intelligent pedal assistance system, combining torque and cadence sensing to deliver smooth, adaptive, and natural riding performance across varied terrain.

Read more

Top Winter E-Biking Gear: Ride Safe and Comfortably in Cold Conditions

Top Winter E-Biking Gear: Ride Safe and Comfortably in Cold Conditions

Prepare for your winter e-bike rides with the right gear. From merino layers to studded tires, here’s everything you need to stay safe, warm, and visible during cold weather cycling.

Read more

Connecticut Electric Bike Laws Explained (2026 Guide)

Connecticut Electric Bike Laws Explained (2026 Guide)

Learn how electric bike laws work in Connecticut, including e-bike classifications, helmet requirements, registration rules, and what riders need to know to stay legal in 2025.

Read more

VTUVIA SN100 Long-Term Review: A Fat Tire Electric Bike Built to Last

VTUVIA SN100 Long-Term Review: A Fat Tire Electric Bike Built to Last

A long-term VTUVIA SN100 electric bike review based on years of real-world riding. Learn how this fat tire electric bike holds up over time — and how the latest SN100 improves with V-Adapt dual-sensor assist and a 48V 14Ah LG battery.

Read more