# Efficiently Combining Data from Different Sensors > This patent describes a system for combining information from many different types of sensors more efficiently by carefully selecting and processing the data, reducing the computer power needed. - **Patent:** US 12737436 - **Original title:** Systems and methods of sensor data fusion - **Granted:** 2026 - **Status:** Active - **Times cited:** 0 - **Field:** consumer_electronics, automotive, telecommunications, ai_ml, software, robotics ## What it does The system outlined in this patent collects data from multiple sensors, then carefully organizes and filters it, a process called curation. It links related pieces of data and combines them, which is known as fusion. A key part of this process is calculating 'conditional entropy' to decide which data is most important, which helps reduce the amount of computing power and time needed. The system then uses this combined data to make educated guesses or decisions (inference) and checks if the combined data is reliable enough by comparing it against a set mathematical standard (validation). For example, a robot could use this to combine camera images, lidar distances, and microphone sounds to understand its surroundings without slowing down. ## What it does NOT cover - Does not cover sensor data fusion systems that do not curate or filter the raw data before combining it. - Does not cover methods of data fusion that do not calculate conditional entropy to reduce computational load. - Does not cover systems that combine data from only a single type of sensor. - Does not cover systems that fuse data without validating the output against a mathematical threshold. - Does not cover data fusion techniques that increase, rather than reduce, computational demand and processing time. ## The clever bit The clever part is reducing the computational effort required for data fusion. It achieves this by curating the data and calculating conditional entropy, which helps the system focus only on the most relevant information from multiple sensors, making the process much more efficient. ## Real-world examples 1. Autonomous vehicle navigation systems 2. Robotics for industrial automation 3. Smart home environmental monitoring 4. Wearable health tracking devices 5. Industrial IoT sensor networks ## Why it matters Combining data from various sensors is critical for technologies like self-driving cars, smart homes, and advanced robotics. This patent focuses on making that process faster and less demanding on computer resources. By reducing computational demands, it allows for more complex real-time decision-making in devices with limited power or processing ability, which can lead to more responsive and reliable systems. ## Frequently asked questions ### What does Efficiently Combining Data from Different Sensors cover? This patent describes a system for combining information from many different types of sensors more efficiently by carefully selecting and processing the data, reducing the computer power needed. ### When does this patent expire? This patent is expected to expire on September 15, 2046, when the invention enters the public domain. ### What problem does this patent solve? Combining data from various sensors is critical for technologies like self-driving cars, smart homes, and advanced robotics. This patent focuses on making that process faster and less demanding on computer resources. By reducing computational demands, it allows for more complex real-time decision-making in devices with limited power or processing ability, which can lead to more responsive and reliable systems. ### What does this patent NOT cover? Does not cover sensor data fusion systems that do not curate or filter the raw data before combining it. **Full plain-English explainer:** https://patentbrief.org/patent/us/12737436/systems-and-methods-of-sensor-data-fusion **Original patent:** https://patents.google.com/patent/US12737436 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Autonomous Cars Process Sensor Data for Driving](https://patentbrief.org/patent/us/20220161815/autonomous-vehicle-system) — Intel's 2020 patent describes a system for autonomous vehicles that cleans and standardizes data from various sensors before using it to perceive the environment and make driving decisions. - [How to Combine Wide-Angle LiDAR Depth with Standard Camera Images](https://patentbrief.org/patent/us/11543533/systems-and-methods-for-wide-angle-lidar-using-non-uniform-magnification-optics) — This patent describes a method for combining a wide-angle depth map from a LiDAR sensor, which has unevenly spaced pixels, with a standard camera image by first correcting the depth map's pixel distribution and then merging the two for a complete view. - [How Vehicles and Drones Find Their Way Using Many Sensors](https://patentbrief.org/patent/us/10107627/adaptive-navigation-for-airborne-ground-and-dismount-applications-anagda) — This patent describes a layered computer system that combines data from many different sensors and an Inertial Measurement Unit to precisely determine a mobile platform's position, velocity, and orientation, even in challenging environments. - [How Assistant Systems Combine Information About One Thing from Many Places](https://patentbrief.org/patent/us/11704899/resolving-entities-from-multiple-data-sources-for-assistant-systems) — This patent describes a system that gathers all known information about a single person, place, or thing from various sources and combines it into one complete profile for an assistant system. - [How Computers Match and Join Messy Data from Different Sources](https://patentbrief.org/patent/us/9607103/amazon-athena) — A method for merging datasets by identifying related but non-identical items using flexible matching rules rather than strict equality.