Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair density value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair density model operable to output a hair density value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair density model, the image to determine a user-specific hair density value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair growth direction value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair growth direction model operable to output a hair growth direction value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair growth direction model, the image to determine a user-specific hair growth direction value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific hair removal efficiency value after removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies after removing hair; training, using the pixel data of the training images, a hair removal efficiency model to determine hair removal efficiency values associated with a degree of hair removal efficiencies ranging from a least efficient to a most efficient; receiving an image of a user's body after removing hair; analyzing by the user image using the hair removal efficiency model to determine a user-specific hair removal efficiency value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific hair removal efficiency value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin irritation value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin irritation model may be trained using the training images to output skin irritation values associated with a degree of skin irritation from least to most irritation. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin irritation model to determine a user-specific skin irritation value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Sensor-based shaving systems and methods of analyzing a user's shave event are described for determining a unique threshold value of the user. A grooming device comprises a handle having a connecting structure connected to a hair cutting implement. A shave event sensor associated with the grooming device measures a user behavior, which includes collecting a first dataset comprising shave data defining a shave event. The first dataset is transmitted via a communication device and is analyzed to determine baseline behavior data of the user, and a unique threshold value of the user is determined from the baseline behavior data. One or more subsequent datasets, each comprising shave data of one or more corresponding shave events, is compared to the unique threshold value to determine comparison data. An indication is provided, based on the comparison data, to indicate a deviation from the threshold value and to influence the user behavior.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific trapped hair value before removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies before removing hair; training, using the pixel data of the training images, a trapped hair model to determine trapped hair values associated with a trapped hair scale ranging from least trapped hair to most trapped hair; receiving an image of a user's body before removing hair; analyzing the user image using the trapped hair model to determine a user-specific trapped hair value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific trapped hair value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin redness value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin redness model may be trained using the training images to output skin redness values associated with a degree of skin redness from least to most red. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin redness model to determine a user-specific skin redness value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific trapped hair value before removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies before removing hair; training, using the pixel data of the training images, a trapped hair model to determine trapped hair values associated with a trapped hair scale ranging from least trapped hair to most trapped hair; receiving an image of a user's body before removing hair; analyzing the user image using the trapped hair model to determine a user-specific trapped hair value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific trapped hair value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin redness value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin redness model may be trained using the training images to output skin redness values associated with a degree of skin redness from least to most red. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin redness model to determine a user-specific skin redness value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair growth direction value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair growth direction model operable to output a hair growth direction value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair growth direction model, the image to determine a user-specific hair growth direction value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's body for determining a user-specific hair removal efficiency value after removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies after removing hair; training, using the pixel data of the training images, a hair removal efficiency model to determine hair removal efficiency values associated with a degree of hair removal efficiencies ranging from a least efficient to a most efficient; receiving an image of a user's body after removing hair; analyzing by the user image using the hair removal efficiency model to determine a user-specific hair removal efficiency value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific hair removal efficiency value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a shaving stroke for determining pressure being applied to a user's skin. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective individual when shaving with a shaving razor. A pressure model, trained with the pixel data, is operable to determine pressure being applied to the respective individual's skin. An image of a user when shaving with the shaving razor is received and analyzed, by the pressure model, to determine a user-specific pressure being applied to the user's skin by the shaving razor when a user shaving stroke is taken. A user-specific electronic recommendation to address at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: A digital imaging method of analyzing pixel data of an image of a user's skin for determining a user-specific nick and cut value after removing hair is disclosed. The digital imaging method includes: aggregating training images of respective individuals' bodies after removing hair; training, using the pixel data of the training images, a nick and cut model to determine skin nick and cut values associated with a nick and cut scale ranging from least nicks and cuts to most nicks and cuts; receiving an image of a user's body after removing hair; analyzing the user image using the nick and cut model to determine a user-specific nick and cut value; generating a user-specific electronic recommendation designed to address a feature identifiable within the pixel data of the user's body based on the user-specific nick and cut value; and rendering the recommendation on a display screen of a user computing device.

Abstract: Sensor-based shaving systems and methods of analyzing a user's shave event are described for determining a unique threshold value of the user. A grooming device comprises a handle having a connecting structure connected to a hair cutting implement. A shave event sensor associated with the grooming device measures a user behavior, which includes collecting a first dataset comprising shave data defining a shave event. The first dataset is transmitted via a communication device and is analyzed to determine baseline behavior data of the user, and a unique threshold value of the user is determined from the baseline behavior data. One or more subsequent datasets, each comprising shave data of one or more corresponding shave events, is compared to the unique threshold value to determine comparison data. An indication is provided, based on the comparison data, to indicate a deviation from the threshold value and to influence the user behavior.

Abstract: Digital imaging systems and methods are described for determining a user-specific skin irritation value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin irritation model may be trained using the training images to output skin irritation values associated with a degree of skin irritation from least to most irritation. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin irritation model to determine a user-specific skin irritation value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair density value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair density model operable to output a hair density value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair density model, the image to determine a user-specific hair density value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: A consumer product trial kit is provided comprising a razor handle, a first razor cartridge configured to couple with the razor handle and comprising a first skin engaging feature, and a second razor cartridge configured to couple with the razor handle and comprising a second skin engaging feature. A first identifying feature is associated with the first razor cartridge and a second identifying feature is associated with the second razor cartridge. The second skin engaging feature and the associated second identifying feature may be different than the first skin engaging feature and the associated first identifying feature, respectively. A selection recorder is configured to allow selection of either the first razor cartridge or the second razor cartridge.