Abstract: A method, system, and non-transitory computer-readable medium for generating artificial-intelligence makeup look recommendations and presenting them on a facial map. The technology processes data specifying configurations of a cosmetic substance, such as current volume, color, formula ingredients, and usage pattern, and applies an artificial intelligence model to produce one or more makeup look recommendations rendered on the facial map. The model may be a classification model trained on user-specific behavioral history and cosmetic substance usage data from multiple users. Temporal context such as time of day, day of week, or calendar event is processed as additional input so recommendations conform to the occasion. A catalogue of makeup looks links to executable routines characterized by attributes such as time span, placement area, aesthetic vibe, and trending status, and product identifiers for required cosmetic products are accessible to the user.

Abstract: A system for the processing of one or more makeup look recommendations for the recommendation of a respective output makeup look recommendation, wherein the respective output makeup look recommendation is renderable in a particular rendering environment (e.g., physical world, augmented reality world, or a metaverse virtual reality world). The system allows for iterative generation of makeup look recommendations within a single rendering environment (e.g., generating new physical world makeup look recommendations from previously user-selected physical world makeup look recommendations) or interreality feedback loop generation of makeup look recommendations within a plurality of rendering environments (e.g., generating a new physical world makeup look recommendation from previously user-selected augmented reality makeup look recommendations.

Abstract: A method of streamlined selection and application of a makeup look involving personalized makeup look recommendations with respective step-by-step makeup routine instructions for executing a selected makeup look. A predictive model identifies one or more makeup look recommendations from a database of makeup looks to present to a user. Recommendations are generated from a combination of user demographic data, user-input preferences, social networking analytics (e.g., frequently used makeup looks among users such as “trending” makeup looks), and user behavioral history data obtained from a smart device application and a smart cosmetic device system. Each respective makeup look can be executed from a respective detailed tutorial guide comprising step-by-step instructions on how to apply cosmetic substances to achieve the respective makeup look.

Abstract: A system for the recommendation of a makeup look to a user in response to data corresponding to a smart cosmetic device, wherein the recommended makeup look is presented to a user for application using the smart cosmetic device. The smart cosmetic device collects user behavioral history such as cosmetic substance usage (e.g., color and formula of a particular cosmetic substance), design configuration (e.g., spray canister, pen, and so on), and usage pattern (e.g., frequency of use for a particular cosmetic substance at a particular time of the day, a particular day of the week, or a particular event). Data associated with user behavioral history is processed by the system to generate at least one user-personalized makeup look recommendation for presentation to the user.

Abstract: A system for the recommendation of a makeup look to a user in response to data corresponding to a smart cosmetic device, wherein the recommended makeup look is presented to a user for application using the smart cosmetic device. The smart cosmetic device collects user behavioral history such as cosmetic substance usage (e.g., color and formula of a particular cosmetic substance), design configuration (e.g., spray canister, pen, and so on), and usage pattern (e.g., frequency of use for a particular cosmetic substance at a particular time of the day, a particular day of the week, or a particular event). Data associated with user behavioral history is processed by the system to generate at least one user-personalized makeup look recommendation for presentation to the user.

Abstract: A system for the processing of one or more makeup look recommendations for the recommendation of a respective output makeup look recommendation, wherein the respective output makeup look recommendation is renderable in a particular rendering environment (e.g., physical world, augmented reality world, or a metaverse virtual reality world). The system allows for iterative generation of makeup look recommendations within a single rendering environment (e.g., generating new physical world makeup look recommendations from previously user-selected physical world makeup look recommendations) or interreality feedback loop generation of makeup look recommendations within a plurality of rendering environments (e.g., generating a new physical world makeup look recommendation from previously user-selected augmented reality makeup look recommendations.

Abstract: A method of streamlined selection and application of a makeup look involving personalized makeup look recommendations with respective step-by-step makeup routine instructions for executing a selected makeup look. A predictive model identifies one or more makeup look recommendations from a database of makeup looks to present to a user. Recommendations are generated from a combination of user demographic data, user-input preferences, social networking analytics (e.g., frequently used makeup looks among users such as “trending” makeup looks), and user behavioral history data obtained from a smart device application and a smart cosmetic device system. Each respective makeup look can be executed from a respective detailed tutorial guide comprising step-by-step instructions on how to apply cosmetic substances to achieve the respective makeup look.