AI personal fragrance consultation and fragrance selection/recommendation

1 . A method executable by computing circuitry comprising:

collecting sensor data from a sensor array of a user computing device, the sensor array comprising a camera, a microphone, and a volatile organic compound (VOC) sensor, the sensor data comprising attributes of a user in a current environment;

collecting an air sample using the VOC sensor;

evaluating the air sample to identify concentrations of a set of VOCs;

obtaining user data for the user associated with the user computing device;

extracting customer attribute features from the sensor data and the user data;

querying a fragrance database using the concentrations of the set of VOCs to identify a present fragrance;

obtaining fragrance attribute features for the present fragrance from the fragrance database;

adding the fragrance attribute features to the customer attribute features;

establishing a first recommendation pathway to evaluate the customer attribute features using a fragrance recommendation model to calculate a set of customer preference probabilities for fragrances in the fragrance database, wherein the set of customer preference probabilities are calculated as correlation coefficients; and

outputting a fragrance recommendation to the user, via a display of the user computing device, based on the set of customer preference probabilities, wherein the fragrance recommendation is selected from a fragrance database comprising correlations between customer attributes and fragrance attributes.

2 . The method of claim 1 , further comprising:

obtaining fragrance user characteristic training data; and

training the fragrance recommendation model to calculate customer preference probabilities for fragrances based on customer characteristics using the fragrance user characteristic training data.

3 . The method of claim 1 , further comprising:

evaluating the sensor data to determine a current emotional state of the user; and

adding the current emotional state to the customer attribute features.

4 . The method of claim 1 , wherein the user computing device is a mobile computing device, a smartphone, a tablet computing device, a laptop computing device, a desktop computing device, or a kiosk.

5 . The method of claim 1 , further comprising:

in response to presentation of a stimuli to the user via the display, capturing reaction data from the sensor array;

extracting reaction attribute features from the reaction data; and

evaluating the reaction attribute features in conjunction with the customer attribute features to calculate the set of customer preference probabilities.

6 . The method of claim 1 , wherein the user data includes a gender of the user, hobbies of the user, personality traits of the user, fragrance preferences of the user, purchase history of the user, or product feedback submitted by the user.

7 . The method of claim 1 , further comprising:

obtaining an image of the user from the sensor data;

detecting facial features in the image;

evaluating the facial features to determine a customer attribute feature; and

adding the customer attribute feature to the customer attribute features.

8 . The method of claim 1 , further comprising:

comparing the set of customer preference probabilities to a sample fragrance library to select a set of fragrance samples for the user;

generating a notification message for the user with a recommendation notification that includes identification of the set of fragrance samples; and

transmitting the notification message to a device of the user.

9 . The method of claim 1 , further comprising:

obtaining a genetic profile for the user;

obtaining an ingredient list for a fragrance included in the fragrance recommendation;

evaluating the genetic profile to generate a prediction of an allergic reaction to an ingredient in the ingredient list;

preventing recommendation of the fragrance based on the prediction of the allergic reaction; and

selecting an alternate fragrance based on the genetic profile and the set of customer preference probabilities.

10 . The method of claim 1 , further comprising:

obtaining a genetic profile for the user;

evaluating the genetic profile to identify genetic customer attribute features; and

adding the genetic customer attribute features to the customer attribute features.

11 . The method of claim 1 , further comprising:

extracting social media data from the user data;

identifying social media profiles using the social media data;

obtaining characteristic data using the social media profiles;

extracting social media attribute features from the characteristic data; and

adding the social media attribute features to the customer attribute features.

12 . The method of claim 1 , further comprising:

obtaining environmental condition data for the current environment of the user;

extracting environmental attribute features from the environmental condition data; and

adding the environmental attribute features to the customer attribute features.

13 . The method of claim 1 , further comprising:

evaluating the customer attribute features to assign the user to a preference group; and

selecting the fragrance recommendation using the set of customer preference probabilities and the preference group.

14 . The method of claim 1 , further comprising:

obtaining a video feed from the sensor array;

processing the video feed using an artificial intelligence processor to identify an emotional reaction of the user to a stimuli present in the video feed;

generating an emotive reaction attribute feature for the user based on the identified emotional reaction; and

adding the emotive reaction attribute feature to the customer attribute features.

15 . The method of claim 1 , further comprising:

obtaining social proximity data for the user;

evaluating the social proximity data to identify a connection between the user and a connection;

collecting connection data for the connection;

extracting connection attribute features from the connection data; and

adding the connection attribute features to the customer attribute features.

16 . The method of claim 1 , further comprising:

calculating a first correlation coefficient for a first feature of the customer attribute features and a second correlation coefficient for a second feature of the customer attribute features;

determining that the first correlation coefficient is outside a correlation coefficient threshold and the second correlation coefficient is within the correlation coefficient threshold;

ignoring the second feature; and

evaluating the first feature using the fragrance recommendation model.

17 . The method of claim 1 , further comprising:

transmitting, to an output device of the user computing device, a response request prompt in conjunction with the fragrance recommendation;

receiving, via an input device of the user computing device, a response to the response request prompt;

establishing a second recommendation pathway to evaluate the fragrance database using preference parameters determined from the response;

evaluating the fragrance database using the preference parameters to select an alternate fragrance recommendation; and

outputting the alternate fragrance recommendation to the user via a display of the user computing device.

18 . The method of claim 1 , wherein the customer attribute features include at least one human parameter and at least one non-human parameter.

19 . The method of claim 1 , further comprising:

transmitting, to an output device of the user computing device, a feedback request prompt in conjunction with the fragrance recommendation;

receiving, via an input device of the user computing device, feedback regarding the fragrance recommendation from the user; and

refining the fragrance recommendation model using the feedback.