SYSTEMS AND METHODS FOR BABY BOTTLE MANUFACTURING

Smart overview of the Invention

A novel method has been developed for creating custom baby bottle nipples tailored to individual users. The process begins with scanning the user's nipple using a computing device, which generates a scan image. A machine learning engine analyzes this image to identify specific features of the nipple, ensuring accurate representation. The output from this analysis is an enhanced scan image that highlights these features.

3D Image Generation and Transmission

Following the initial scanning and feature identification, a genetic algorithm is employed to create a three-dimensional (3D) image of the user's nipple. This 3D model serves as a detailed profile for the baby bottle nipple. Once generated, the 3D image is transmitted to another computing device, enabling the 3D printing of a custom nipple that replicates the user's unique anatomy.

Utilization of Machine Learning and AI

The method incorporates advanced machine learning techniques to refine the identification process. By training the machine learning engine on a diverse set of nipple images, it can accurately detect and analyze various nipple shapes. This training also allows for dynamic adjustments during scanning, prompting users to rescan if necessary and ensuring high-quality results.

Enhanced Scanning Techniques

To facilitate effective scanning, the method leverages technology commonly found in modern mobile devices, such as structural light sensors and facial recognition systems. These tools help capture multiple angles of the nipple, which are then stitched together to form a comprehensive 3D point cloud. This technique not only aids in accuracy but also enhances user experience by allowing scans to be conducted conveniently at home.

Addressing Common Feeding Challenges

The creation of custom nipples aims to alleviate issues like nipple confusion, where infants struggle to transition between breastfeeding and bottle feeding. By replicating the mother's nipple shape closely, these custom products can improve feeding experiences for both mothers and infants. The integration of AI further optimizes the scanning process, ensuring that each custom nipple meets the specific needs of users based on their unique anatomical features.