Invented by Dev; Bodhayan, Smith; Aaron, Juneja; Girish, Swaroop; Prem

Understanding new technology can be tough, but learning about patents can give you a peek at the future. Today, we’re going to break down a patent application for a system that lets you point your phone at a part—like a screw, washer, or any other component—and instantly get its identity using artificial intelligence (AI) trained on 3D models. If you’ve ever struggled to figure out which part you’re looking at, this invention is for you.

Let’s walk through how the world looks before this technology, why this invention is special, and what makes it tick. We’ll keep things simple and easy to understand, so you can see the power and potential of this idea.

Background and Market Context

Buying, replacing, or repairing things often means finding the right part. If you work in construction, manufacturing, maintenance, or even just fixing things at home, you know this can be a challenge. There are so many parts—big, small, similar, or confusingly different. Sometimes, all you have is the part in your hand, and no idea what it’s called, where to get it, or if there’s a better version.

Traditionally, identifying a part meant searching through huge catalogs, asking experts, or comparing photos online. Mistakes are common. Picking the wrong part can lead to delays, wasted money, or even safety problems. Companies spend a lot of time and money helping customers figure out what they need.

Smartphones have changed many things. Almost everyone has a phone with a camera. People use phones to scan barcodes, take pictures, and search for products. But what if you could just snap a photo of a part and instantly know what it is—no barcode needed? That’s where this patent comes in.

The market for this is huge. Factories, repair shops, warehouses, and even hobbyists all need to identify parts quickly. As machines and products become more complex, there are more unique parts than ever. With global supply chains and online shopping, the right part might be anywhere in the world. A system that makes part identification fast and easy can save time, reduce errors, and help businesses serve customers better.

But to make this work, the system needs to be reliable. Lighting, angles, and backgrounds can make parts look different in photos. Parts might be dirty, painted, or worn. The system must handle all of this, and it needs to work on a regular phone, not just in a lab. This is a big challenge, and it’s what this new invention tries to solve.

Scientific Rationale and Prior Art

To understand why this patent is a big deal, we need to look at what’s been tried before and what problems still exist.

In the past, some apps let you take a photo of a part and search for matches online. These usually rely on comparing your photo to a database of other photos. But photos taken in the real world are messy. Lighting changes, backgrounds distract, and small changes in angle or wear can throw off the results. Making a big enough database of photos is also hard—every part needs to be photographed from many angles, in many conditions.

Some companies use barcodes or QR codes to identify parts, but not all parts have these codes. Small parts, old parts, or parts from different suppliers may have no markings at all. This leaves people stuck.

Another approach is using 3D scanning. Special scanners can create digital models of parts, but scanners are expensive and not always practical in the field. Plus, most people don’t have access to 3D scanners.

In recent years, AI has made huge leaps, especially in recognizing objects in pictures. AI models like neural networks can be trained to spot cats, cars, and faces with high accuracy. But training these models needs lots of labeled pictures. Getting good training data for thousands of different parts is a huge task.

Some researchers have tried training AI using computer-generated images—making digital pictures of parts from 3D models. This gives more control and variety, but it’s still tricky. The images need to look real enough for the AI to learn useful patterns. The system also needs to know exactly where the part is in each picture, which is easier with digital models but can get complicated.

Another area of progress is large language models (LLMs). These are powerful AI systems that can answer questions, summarize documents, and even help with product support. When combined with visual recognition, they can help not just identify a part, but also provide information, instructions, or even tutorials about it.

Despite all this progress, no one has put all these pieces together in a way that works smoothly for identifying mechanical and electrical parts using just a phone. That’s what makes this invention stand out. It uses 3D models to create a huge, varied set of training images, builds a smart AI to recognize parts in real-world photos, and links it all together with language models and friendly interfaces.

Invention Description and Key Innovations

Now let’s dive into how this invention works, and what makes it special.

The heart of this invention is a method and system that lets you use your phone’s camera to snap a picture of a part. The system then uses a trained AI model to figure out what the part is. But the magic happens in how the AI is trained and how the system uses 3D models to make everything work better.

Here’s a step-by-step look at the key ideas:

1. Training with 3D Models: Instead of relying on real-world photos, the system starts with 3D models of parts. These can be CAD files or other digital representations. For each part, the system generates many images by digitally “photographing” the part from different angles, with different backgrounds and textures. This creates a rich set of training images that cover many real-world situations.

2. Smart Image Processing: The system knows exactly where each part is in every generated image, since it controls the digital photo shoot. It can draw boxes around the part, making it easy for the AI to learn what to look for. It can even use tricks like converting images to grayscale, finding edges, or cropping out backgrounds, all to make the training data cleaner and more useful.

3. Avoiding Duplicate Training Data: Sometimes, parts look almost the same from different angles, especially simple parts like washers or screws. The system checks for images that are too similar and removes duplicates. This keeps the AI from being confused or biased by seeing the same view too many times.

4. Training the AI Model: With all these smartly generated images, the system trains an AI model to find and identify parts in photos. This model learns to handle different orientations, backgrounds, and lighting—making it robust for real-world use.

5. Using the AI to Identify Parts: When a user takes a photo of a part with their phone, the image is sent to the AI, which looks for known parts and suggests possible matches. Instead of just giving one answer, the system can show several likely identities, each with a score or description.

6. Checking for Similar Parts: Sometimes, two parts might look almost the same but belong to different products or assemblies. The system checks the “family tree” for each part, using product hierarchies stored in the 3D files. If two parts are nearly identical but used in different places, the system can show just one to avoid confusion.

7. Adding Language Intelligence: The system can use a large language model trained on product descriptions, manuals, and other documents. This lets it do more than just name the part—it can describe what the part does, suggest compatible products, or even answer user questions. For example, if you ask, “What does this washer do?” the system can give a plain-English answer.

8. Making It Interactive and Visual: Results aren’t just shown as a list. The system can present a visual “family tree” showing where the part fits in a product. It can even use augmented reality (AR) or virtual reality (VR) to let users explore a digital model of the part or the whole assembly. Imagine seeing a 3D model of the product on your screen, with the identified part highlighted.

9. Tutorials and Support: If you want to know how to install, remove, or use the part, the system can provide step-by-step tutorials or guidance, all generated or summarized by the language model. This helps users not only find the right part, but also use it correctly.

10. Flexible and Scalable: Because the system is based on digital models, it can be updated easily. New parts can be added by uploading new 3D files. The heavy processing can be done in the cloud, so even simple phones can use the system without needing lots of power.

This invention stands out because it connects visual AI, 3D modeling, language intelligence, and user-friendly interfaces into a single workflow. It’s not just about recognizing a part—it’s about making the whole process of finding, learning about, and using parts as quick and simple as possible.

The patent also covers the technical details, such as how images are created from different angles, how bounding boxes are calculated using contours or coordinates, how similar images are filtered, and how language models are trained using product hierarchies and descriptions. It even includes ways to convert different types of 3D files, handle curved surfaces, and merge data from multiple sources.

For the user, the experience is seamless. Open an app, point your camera at a part, and get instant answers—what it is, where it belongs, and what to do with it. For businesses, it means fewer mistakes, faster service, and happier customers.

Conclusion

This patent application brings together the latest in artificial intelligence, computer vision, and interactive technology to solve a very real problem: identifying parts quickly and accurately using just a mobile phone. By using 3D models to generate training data, the system overcomes many of the weaknesses of older methods. It adapts to real-world challenges, gives clear and useful answers, and can even teach users about the parts they’re working with.

As technology continues to move forward, inventions like this will make repair, maintenance, and purchasing easier for everyone. The blend of smart AI, clear visuals, and helpful explanations means no more guessing or searching through endless catalogs. Instead, you get the right part, right when you need it, with all the information at your fingertips.

If you’re involved in any business that handles parts, or if you’re just a curious tinkerer, this is a glimpse of the future—a future where finding and understanding any component is as simple as taking a photo.

Click here https://ppubs.uspto.gov/pubwebapp/ and search 20250218151.