MODULATION OF COLORING FORMULATION COMPONENTS FOR HAIR COLORING DEVICE

Smart overview of the Invention

The system introduces a computer-based method for personalizing hair coloring by capturing digital images of a user's hair. This data is processed by a style recommendation engine that identifies the hair color and uses a machine learning model to create a style profile. This profile includes parameters to control a hair coloring device's formulation dispensing system, ensuring precise application tailored to the user's hair characteristics.

Style Profile Generation

The style profile encompasses not only color but also patterns such as highlights or balayage, and may include custom components like specific dyes or developers. The machine learning model can incorporate additional inputs like hair length, texture, and environmental conditions, enhancing the accuracy of the style recommendations. Sensor feedback from the coloring device further refines application by adjusting discharge based on real-time data.

Device Configuration

A hair coloring device equipped with computation circuitry receives the style profile to modulate formulation dispensing. The device adjusts parameters like flow rates to achieve effects such as gradient coloring. A user interface, potentially on a smartphone, aids users by providing visual guidance for optimal application techniques, ensuring professional-quality results at home.

Additional Factors and Recommendations

The system considers various factors when generating style recommendations, including current trends, geographic influences, and user preferences. It may suggest additional treatments or provide diagnostic tests to predict hair response to proposed styles. Alerts and updates keep users informed about maintaining their chosen styles or adapting to environmental conditions.

Integration and Feedback

The software allows for wireless transmission of style profiles between devices, enhancing user convenience. Onboard sensors in the coloring device gather data to optimize operation and provide user guidance. Feedback mechanisms enable users to influence future recommendations, ensuring continuous improvement and personalization of the hair coloring experience.