System and method for intelligent context-based personalized beauty product recommendation and matching

A system and method for intelligent context-based personalized beauty product recommendation and matching, comprising a data extraction engine, individual profiles of intended users stored in one or more user databases, one or more product databases, a skin analysis engine, and a data analysis and recommendation engine, which gathers a plurality of customer responses regarding fact-based input including locational data, concern-based input, preference-based input, and goal-based input, winch uses the customer responses and environmental conditions to create a requirement vector, and which uses the requirement vector as an input into one or more machine and deep learning algorithms to generate as output personalized beauty product recommendations.

BACKGROUND

Field of the Art

The disclosure relates to the field of recommendation systems, and more particularly to the field of personalized beauty product recommendation systems.

Discussion of the State of the Art

Exterior beauty often concerns tine interaction of discrete elements such as hair, skin, complexion and the like. When addressing these elements, individuals can have different needs, concerns, and goals. Examples of different needs include oily and combination skin, hair porosity and scalp condition. Examples of different concerns include acne and breakouts, fine lines, wrinkles, dull and flat hair, sensitive scalp and the like. Examples of goals include clear and decongest, refresh and hydrate skin, condition and repair hair, and tame frizz among others.

When addressing beauty-related products, individuals can have poor experiences, such as the inability to access products that they may need, endless trial and error of unsuitable products, an accumulation of products which do not serve their needs, and the associated waste of money and time.

What is needed is a system and method for intelligent context-based personalized beauty product recommendation and matching which takes advantage of an individual's beauty-related needs, concerns, goals, and environment in order to more efficiently match the individual to beauty-related products.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, a system and method for intelligent context-based personalized beauty product recommendation and matching, comprising a data extraction engine, individual profiles of intended users stored in one or more user databases, one or more product databases, a skin analysis engine, and a data analysis and recommendation engine, which gathers a plurality of customer responses regarding fact-based input including locational data, concern-based input, preference-based input, and goal-based input which uses the customer responses and environmental conditions to create a requirement vector, and which uses the requirement vector as an input into one or more machine and deep learning algorithms to generate as output personalized beauty product recommendations.

According to a preferred embodiment, a system for intelligent context-based personalized beauty product recommendation and matching, is disclosed comprising: a computer system comprising a memory and a processor; a data extraction engine, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, causes the computer system to: scrape product data from a plurality of data sources accessible via the internet; store the scraped product data as a product vector in the memory; receive customer data input responses, the customer data input responses comprising at least a fact-based input, at least a concern-based input, at least a preference-based input, and at least a goal-based input; create an individual profile of the customer using the customer data input responses and store the individual profile in the memory; and map the customer data input responses to product requirements and store the mapped customer data input responses in the individual profile; and a data analysis and recommendation engine, comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, causes the computer system to: retrieve the individual profile from the memory; retrieve the fact-based input responses and extract environmental conditions from a locational input included in the fact-based input; combine the mapped customer data input responses and the extracted environmental conditions into a requirement vector; retrieve one or more product vectors from the memory; compute the similarity between the requirement vector and the one or more product vectors; identify the product associated with the product vector that has the highest computed similarity score; and display the identified products as recommended products to the customer.

According to a second preferred embodiment, a method for intelligent context-based personalized beauty product recommendation and matching, is disclosed comprising the steps of: scraping product data from a plurality of data sources accessible via the internet; storing the scraped product data as a product vector in the memory; receiving customer data input responses, the customer data input responses comprising at least a fact-based input, at least a concern-based input, at least a preference-based input, and at least a goal-based input; creating an individual profile of the customer using the customer data input responses and store the individual profile in the memory; mapping the customer data input responses to product requirements and storing the mapped customer data input responses in the individual profile; retrieving the individual profile from the memory; retrieving the fact-based input responses and extract environmental conditions from a locational input included in the fact-based input; combining the mapped customer data input responses and the extracted environmental conditions into a requirement vector; retrieving one or more product vectors from the memory; computing the similarity between the requirement vector and the one or more product vectors; identifying the product associated with the product vector that has the highest computed similarity score; and displaying the identified product as a recommended product to the customer.

According to various aspects of the invention; the product data includes ingredients, chemical composition, functions of ingredients, allergens, ingredient free data, benefits, use, and product source information; wherein the customer data input responses are received from a questionnaire; the customer data input responses are received from an interactive chat bot; further comprising a skin analysis engine, comprising a third plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, causes the computer system to: receive a photograph of the customer; perform face detection on the photograph; extract a face oval from the photograph; eliminate non-skin areas from the extracted face oval; analyze the extracted face oval with eliminated non-skin areas to generate as outputs a predicted skin-tone and a predicted skin concern; store the outputted skin-tone and skin concern in the individual profile; and forward the outputted skin-tone and skin concern to the data analysis and recommendation engine; the data analysis and recommendation engine receives the outputted skin-tone and skin concern and combines them into the requirement vector.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and method for intelligent context-based personalized beauty product recommendation and matching, comprising a data extraction engine, individual profiles of intended users stored in one or more user databases, one or more product databases, a skin analysis engine, and a data, analysis and recommendation engine, which gathers a plurality of customer responses regarding fact-based input including locational data, concern-based input, and goal-based input, which uses the customer responses and environmental conditions to create a requirement vector, and which uses the requirement vector as an input into one or more machine and deep learning algorithms to generate as output personalized beauty product recommendations.

Definitions

“Beauty product” as used herein refers to a class of products including, but not limited to, cosmetics, personal care, skin care, fragrances, hair care, make up, lipstick, and body care. Skin care products may be used to cleanse, exfoliate, protect, and replenish the skin through the use of cleansers, toners, serums, moisturizers, and balms. Beauty products may be constituted from a mixture of chemical compounds derived from either natural sources and/or synthetically created ones.

Conceptual Architecture

FIG. 1is a block diagram illustrating an exemplary system architecture for matching individuals with personalized beauty product recommendation100, according to an embodiment. According to an embodiment, the system100may comprise a data extraction engine110, individual profiles120, one or more user databases140, one or more product databases150, a skin analysis engine130, and a data analysis and recommendation engine160.

According to an embodiment, data extraction engine110may retrieve, receive, or otherwise obtain a plurality of information related to system users and beauty, hygiene, and health related products. User information and product information may be obtained via a variety of mechanisms such as a data connector111, a survey/questionnaire112, a database crawler113, an image portal114, and an interactive chatbot115. Data obtained via these mechanisms may be further processed by one or more data processing pipelines116which may format and process the obtained data for further use by system100such as for training one or more machine and deep learning algorithms or to be used as input into data analysis and recommendation engine160in order to generate as output individualized beauty product recommendations.

A data connector111may be used to expose existing application programming interfaces (APIs) for external services or data sources to facilitate data gathering and extraction. For example, an intended user can choose to link a social media account with their individual profile120and the data connector111can find and interact with APIs that allow for social media data (e.g., likes, dislikes, comments, photographs, etc.) to be retrieved and added to the intended user's individual profile. Web/database crawler113may be configured to search for and download, from the internet, medical information materials related to beauty, hygiene, and health products, including, but not limited to, archives of published medical literature such as MEDLINE and PubMed, archives of clinical trial databases, news reports, conference papers, and individual journals. Additionally, web/database crawler113may be configured to search for and download ingredient lists of a plurality of beauty, hygiene, and health products. As the medical information and ingredient information is downloaded, it is fed to data processing pipeline(s)116which may perform a series of operations to extract data from the medical information materials and ingredient information. For example, data processing pipeline(s)116may first determine a format of each of the materials received (e.g., text, PDFs, images), and perform conversions of materials not in a machine readable or extractable format (e.g., performing optical character recognition (OCR) on PDFs and images to extract any text contained therein).

According to an embodiment, an intended user may complete one or more survey/questionnaire112which may be configured to extract a plurality of user information related to facts, concerns, preferences, and goals in regard to the intended user's beauty, hygiene, and health. In a preferred embodiment, an intended user completes one or more questionnaires during system registration such that the intended user's individual profile120may be populated with useful and relevant user-submitted data which can be used as input into data analysis and recommendation engine160. Questionnaires112may be used to gather user information after the creation of an individual profile120. In some embodiments an interactive chatbot115may be configured to converse with system users in order to extract useful and relevant user data for individual profile120creation or to gather supplemental user information. Chatbot115may also be configured to provide customer service and serve relevant content. According to a preferred embodiment, an image portal114may be configured to receive a user-uploaded photograph of the user. Image portal114may automatically validate the photograph for color accuracy, quality, and other properties. User-uploaded photos may be full body length photos or head shot photos. If a full body length photo is uploaded, image portal114may be configured to crop the photo such that only the user's head is within the frame of the image. Furthermore, image portal may create a copy of the user-uploaded or cropped image so that one or more image processing and analysis tasks may be performed on the copy of the image. Image processing and analysis may be performed by a skin analysis engine130. User-uploaded photograph(s) and cropped images may be added to the user's individual profile120and stored in user database(s)140.

System user information may include, but is not limited to, age, gender, physical address and/or other user location description (e.g., a zip code or geographical region, billing information, etc.), email address, social media handle or username, purchase history, shopping cart inventory data, webpage views, online interactions, user product or service reviews, user recommendation system reviews, social media data (e.g., likes, dislikes, mentions, product and/or company subscriptions, etc.), fact-based input data (e.g., hair type, hair porosity, hair texture, skin type, dark spots, acne, hyperpigmentation, allergies, beauty routine, beauty product preference(s), etc.), a user-uploaded photograph, concern-based input, data (e.g., line lines and wrinkles, loss of skin elasticity, thinning hair, damaged ham, sun damage to skin, etc.), preference-based input, and goal-based input data (e.g., radiant and youthful, thermal protection hair, volumize hair, etc.).

According to an embodiment, user data may be obtained using both explicit and implicit forms of data collection, Examples of explicit data collection include, but are not limited to, asking an intended user to rate an item on a sliding scale, asking an intended user to search, asking an intended user to rank a collection of items from favorite to least favorite, presenting two items to an intended user and asking her/him to choose the better one of them, asking an intended user to create a list of items that she/he likes. This type of explicit data may be gathered during intended user profile creation and registration, it may be gathered via a series of questionnaires112that the intended user can submit, it may be gathered from an interactive chat-bot115that can converse with an intended user to extract user preference data, and it may be gathered from external sources such as a user-linked social media account or geographic and mapping services such as GOOGLE MAPS®. These data gathering techniques are used as examples and do not represent all possible data gathering techniques which may be used in various embodiments of the disclosed system. Examples of implicit data collection include, but are not limited to, observing the items that an intended user views in an online store, analyzing item/user viewing times, keeping a record of the items that a user purchases online, obtaining a list of items that a user has searched for or purchased on her/his computer or mobile computing device (e.g., smart phone, tablet, laptop, smart wearable, etc.), analyzing the user's social network and discovering similar likes and dislikes.

The gathered user information can be used to create an individual profile120of the user and can be stored in user database(s)140. The gathered product information may be stored in product databases)150. Both the user databases)140and product database(s)150may be implemented on any suitable storage device including, but not limited to, external or internal hard drives and solid state drives, flash memory, cloud storage, ROM, RAM, and other non-transitory storage mediums. Both user database(s)140and product database(s)150may be configured to store data in a plurality of ways such as in a relational database, graph database, object-oriented database, NoSQL, database, centralized database, cloud database, operational database and the like.

The one or more product databases150may be configured to include relevant information concerning beauty products, such as to facilitate matching of products with individuals. Product database(s)150may include detailed product information, such as the non limiting examples of ingredients, functions of the ingredients, allergens, sourcing information (e.g., fair trade, ethically sourced, etc.), benefits, use, ingredient free data and the like. According to an embodiment, product information may be vectorized such that each product in the product database(s)150has a unique vector representation. The vectorized products may be stored in product database(s)150.

According to an embodiment, data analysis and recommendation engine160may receive as inputs an individual profile, one or more skin analysis outputs, and product data in order to generate as output one or more personalized beauty product recommendations to an end user device170. Recommendation engine160may use the input response by the user from the individual profile120to automatically create a mapping161of user information to product requirements. Environmental conditions may be extracted for personalized recommendations by using the locational input provided by the user in the individual profile120. Environmental context is important for making beauty product recommendations. Environmental factors such as air quality, UV index, water quality (e.g., soft or hard water), temperate zone, and climate. For example, water quality is an important factor when recommending beauty products because if the product utilizes a surfactant cleansing function there will not be much lather in hard water areas and as a result those types of products with be less effective. Using all user information and the environmental factors a requirement vector is created162. All available products may be represented by a vector and a similarity score of products may be calculated between all product vectors and the requirement vector using similarity calculator164. The similarity score may be calculated by a variety of methods such as cosine similarity and/or Euclidian distance and/or other similarity metrics known to those skilled in the art. The 3-4 top products with the highest similarity score in different product categories can be presented to the customer on the end user device170as personalized recommendations. In this way, system100can match customer requirements to products. For example, a customer may have indicated that he is concerned about clogged pores and prefers organic products, and the recommendation engine160can differentiate between products that have chemical ingredients versus organic ingredients and would recommend to the customer a product with organic ingredients that can alleviate clogged pores.

Recommendation engine160utilizes the information, gathered, organized, and stored by data extraction engine110to train machine and deep learning algorithms at a training stage and conduct analyses in response to user activity and return recommendations based on the analyses at an analysis stage. According to an embodiment, recommendation engine160may utilize one or more neural networks163in order to predict recommended products for the system user. Examples of neural networks that may be used include, but are not limited to, recurrent neural networks (RNNs), long shot-term memory (LSTM) networks, and graph based neural networks (GNNs). The neural network163may be trained on a subset of the product database(s)150data and a subset of the user database(s)140data. Product vectors and requirement vectors may be created from the subsets) of data and fed into the input layer of the neural network163. The vector representations are processed by the hidden layers for feature extraction. The output layer produces the result to recommend a product. During the training process neural network163may be optimized by tuning the hyperparameters for each layer of the network, as well as using backpropagation and gradient descent techniques to minimize the error between layers and improve network predictions. At the output, the trained model should produce one or more recommended products for a one or more product categories. The outputted recommended products may be in vector form. A similarity score may be calculated between the outputted product vectors and the requirement vector. The product(s) with the highest similarity score may be displayed to the customer as recommended products.

According to an embodiment, recommendation engine160may use content-based filtering, collaborative filtering, session-based filtering, or a hybrid combination of these. Initially, recommendation engine160may implement content-based filtering which is based on a description of the item (e.g., product vector) and an individual profile of the user's preferences.

The data contained in the individual profile120can be matched with suitable products from product database(s)150by recommendation engine160. In making recommendations, recommendation engine160considers the inputs from the fact-based information, the concern-based information, the preference-based information, and the goal-based information. For example, in the event, the individual profile120indicates the presence of oily skin, acne, scarring, homes in a tropical climate and prefers gluten free products, the recommendation engine160via the neural network163match the individual with appropriate cleansers with salicylic acid to address acne, toner with aloe for calming and soothing properties, lightweight moisturizer with sunscreen to reflect the tropical climate and includes salicylic acid for acne, with all product recommendations being gluten free. In this example, it should be appreciated that recommendation engine160is configured to match the individual with complimentary products for specific conditions, such as the acne condition.

According to an embodiment, recommendation engine160may have, the form of a rule-based recommendation engine, with supervised learning. Recommendation engine160considers the individual's conditions, concerns, goals, and environmental conditions, as well as past purchases. Additionally, recommendation engine160may be configured to also consider the recommendations made to individuals having similar profiles. In other embodiments, recommendation engine160may have other forms and can consider other decision-making criteria.

As a user develops experience with the products listed in the individual beauty product recommendations, feedback information can be entered into the individual profile120via the end user device170. The feedback information can include, but is not limited to, experiences with certain products and/or ingredients, likes and dislikes. The feedback information may be used to adapt time individual profile120to those products which better suit the individual's needs. In this way, system100advantageously learns and adjusts to the specific needs, concerns, and goals of an individual based on sentiment (e.g., reviews), lookalike audiences, wish-listed items, searches and the like.

FIG. 2is a block diagram illustrating an exemplary architecture for an aspect of the personalized beauty product recommendation system100, a skin analysis engine200. According to an embodiment, the skin analysis engine200may comprise a face detection module201, a face oval extraction module202, a non-skin area removal module203, a secondary feature module205, one or more classification networks206, and one or more image segmentation networks204. It should be appreciated that various embodiments may include more or less modules without deviating from the scope and function of the disclosed system. The various modules and networks operating within skin analysis engine200may comprise one or more machine and deep learning algorithms. These algorithms may be trained using a subset of the information located in user database(s)140, product database(s)150, and external data that may be obtained. The training of these algorithms may be conducted via supervised learning, unsupervised learning, or some combination of the two.

According to an embodiment, when a system100user uploads a photograph it may trigger skin analysis engine200to receive, retrieve, or otherwise obtain the uploaded image data210in order to perform skin analysis. The face detection module201analyzes the image data and detects the face of the user. Additionally, face detection module201can detect ethnic facial types. Upon detection of the user's face, the image is cropped and fed into a face oval extraction module202that extracts the face oval of user in the uploaded image. The face oval can be used to determine the face shape of the user. The non-skin area removal module203may be used to remove non-skin areas from the face (e.g., eyebrows, eyes, lips, etc.). After the image data210has been processed through these modules, what remains is a processed image of the user's face without its facial features. The processed image may then be sent to either of or both of a classification network206to determine the skin-tone of the user and an image segmentation network204that detects and classifies the skin concerns of the user. Both skin-tone analysis230and skin concern analysis220outputs may be used to match cosmetic products to a user, such as matching foundation to skin tone or recommending products that may alleviate a user's skin concern.

Image segmentation is the process of dividing an image into multiple segments. In this process, every pixel in the image is associated with an object type. There are two major types of image segmentation: semantic segmentation and instance segmentation. In semantic segmentation, all object of the same type are marked using one class label while in instance segmentation similar object get their own separate labels. The basic architecture in image segmentation consists of an encoder and decoder. Then encoder extracts features from the image through filters. The decoder is responsible for generating the final output which is usually a segmentation mask containing the outline of the object.

According to an embodiment, image segmentation network204may comprise a convolutional neural network (CNN) with a trained encoder and decoder. There are a variety of CNN architectures known in the art that may be used for image segmentation such as, for example, U-Net, Fast Fully-connected network (FastFCN), Gated-Stream CNN, DeepLab, and Mask R-CNN to name a few. An encoder may be trained to extract features corresponding to a pre-determined set of skin concern objects. For example, skin concern objects may include, but are not limited to, acne, hyperpigmentation, scarring, ultraviolet (UV) damage, melasma, itchy scalp, etc. The encoder can be trained to extract features and detect one or more skin concern objects that may be present in the processed image in order to classify user skin concerns such that the system100may make personalized recommendations. For example, image segmentation network204could detect whether a user has hyperpigmentation or melasma, which are different skin conditions and thus require different products to treat the conditions and which would be taken into account when analyzing the image and making product recommendations. The encoder may be trained on a subset of the uploaded images to system100. Additionally, the encoder may be trained using supervised learning and a large corpus of labeled images which show a skin condition. Such labeled images may be gathered from publicly available databases, datasets, and medical literature. The encoder may extract features from the image through one or more filters. The decoder is responsible for generating the final output which can be a segmentation mask containing the outline of the skin concern object(s). The outlined skin concern objects may then be used to classify the user skin concerns. The classified skin concerns220may then be used as an input to data analysis and recommendation engine160for generating personalized beauty product recommendations.

According to an embodiment, classification network206may comprise a convolutional neural network with a trained encoder and decoder. A CNN is able to successfully capture the spatial and temporal dependencies in an image though the application of relevant filters. A CNN performs a better fitting to the image dataset due to the reduction in the number of parameters involved and reusability of weights. In other words, the CNN can be trained to understand the sophistication of the image better. The role of the CNN is to reduce the images into a form which is easier to process, without losing features which are critical for generating good predictions. Classification network206may comprise a plurality of convolutional layers/filters which taken together form the encoder part of the network. For example, a first filter is called the Kernel which moves across the input image with a certain stride value (the number of pixels the Kernel filter moves at a time) until the entire image is traversed. In the case of images with multiple channels (e.g., RGB), the Kernel has the same depth as that of the input image. Matrix multiplication may be performed between the Kernel filter matrix and the input channel stack and all the results can be summed with the bias to generate a compact one-depth channel convoluted feature output. The purpose of the convolution operation is to extract the high-level features such as edges, from the input image210. Generally, the first convolution layer is responsible for capturing the low-level features such as edges, color, gradient orientation, etc. With additional layers, the CNN adapts to high-level features as well, giving classification network206a complete understanding of the images in the dataset.

Similar to the convolutional layer, the pooling layer is responsible for reducing the spatial size of the convolved feature. This is done to decrease the computational power required to process the data through dimensionality reduction. Furthermore, it is useful for extracting dominant features which are rotational and positional invariant, thus maintaining the process of effectively training the classification model. Pooling layers may be implemented between convolution layers of the CNN with the goal of this type of architecture being a spatially reduced, feature-rich representation of the input image210. There are two main types of pooling known in the art: max pooling and average pooling. Depending on the embodiment, either max pooling or average pooling may be implemented in classification network206. Max pooling also performs as a noise suppressant; it discards the noisy activations altogether and also performs de-noising along with dimensionality reduction. As a result, in a preferred embodiment of the system100max pooling may be implemented as it leads to improved model performance compared to average pooling.

The convolutional layer and the pooling layer together form the i-th layer of the CNN. Depending on the complexities in the images, the number of such layers may be increased for capturing low-level details even further. After going through the above process, the classification network206is enabled to understand the features and results in a fully trained encoder portion of the CNN. Classification network206may then flatten the final output of the encoder and feed it to a regular neural network for classification purposes. Adding a fully connected layer to the output of the CNN can allow for learning of non-linear combinations of the high-level features as represented by the output of the convolutional layer(s). The fully-connected layer is learning a possible non-linear function in that space. After passing through the CNN the input image is converted into a suitable form for input into a multi-level perceptron neural network, the classification network206may flatten the image into a column vector. The flattened output may be fed to a feed forward neural network and backpropagation may be applied to every iteration of training. Over a series of epochs, the model is able to distinguish between dominating and certain low-level features in images and classify them using a soft-max classification technique. In other embodiments, different classification techniques known in the art may be used. As a result of the above process, one or more classification networks206may be trained and configured to classify input images210such as for skin-tone analysis, skin concern analysis and various other skin related tasks. The outputted skin-tone230may be sent to data analysis and recommendation engine160and used as an input to generate personalized beauty product recommendations.

FIG. 10is a block diagram illustrating an exemplary architecture for the training of a beauty product recurrent neural network1000, according to an embodiment. According to an embodiment, a recurrent neural network may be trained and utilized to generate personalized beauty product recommendations. The training of the beauty product RNN1000may be implemented using supervised learning techniques. The beauty product RNN1000may be trained using a subset of the obtained and pre-processed user responses1001and product information1002. User responses1001may include fact-based inputs, concern-based inputs, preference-based inputs, and goal-based inputs as well as locational data extracted from the fact-based inputs. The subset of user responses1001may be fed into an encoder1010which may have one or more recurrent layers1011for embedding the input subset of user responses1001such that after the input data has been processed through the one or more recurrent layers the encoder1010may extract the input data's defining features and assign weights1013to the neurons existing within the hidden recurrent layers1011. After passing the subset of user responses1001through encoder1010, what is output is a user requirement vector1021existing within the encoded feature space1020. The user requirement vector1021may comprise all of a given user's responses1001and environmental context extracted from locational data encoded into the requirement vector1021after passing through the one or more recurrent layers1011. According to some embodiments, beauty product RNN1000may be configured with one or more attention1012mechanisms. Attention1012is a mechanism that may be combined with the beauty product RNN1000allowing it to focus on certain parts of the input when predicting a certain part of the output sequence, enabling easier learning and of higher quality.

The subset of product information1002may be fed into an encoder1010which may have one or more recurrent layers1015for embedding the input subset of product information1002such that after the input data has been processed through the one or more recurrent layers the encoder1010may extract the input data's defining features and assign weights1014to the neurons existing within the hidden recurrent layers1015. After passing the subset of product information1001through encoder1010, what is output is a product vector1022existing within the encoded feature space1020. According to some embodiments, beauty product RNN1000may be configured with one or more attention1012mechanisms. Attention1012is a mechanism that may be combined with the beauty product RNN1000allowing it to focus on certain parts of the input when predicting a certain part of the output sequence, enabling easier learning and of higher quality.

Both the user requirement vector1021and product vector1022may be sent to the decoder1030where the vectors may be processed through one or more fully connected layers1031which may be configured to extract one or more products from the feature space1020which are similar or exist within proximity to the user requirement vector1021within the feature space1020. A SoftMax layer1032may be configured to assign probabilities to the extracted products which measure the appropriateness of the extracted beauty products to satisfy the user requirement vector1021. The SoftMax layer1032may connect to an output layer which generates as outputs one or more personalized beauty product recommendations1040. At this point in the training process the beauty product RNN can be evaluated for model performance and feedback training using a parametric optimizer1050. Parametric optimizer1050may be configured to allow for hyperparameter tuning and neuron weight adjustment. Hyperparameters that may be configured and adjusted between training sessions include, but are not limited to, the number of hidden layers and units, dropout (e.g., to avoid overfitting the model thus increasing the generalization power), network weight in initialization, the activation function (e.g., SoftMax, sigmoid, rectifier activation function, etc.), the learning rate (i.e., how quickly the network updates its parameters), momentum, number of epochs, batch size and the like. Parametric optimizer1050may utilize various methods to discover and tune hyperparameters such as manual search, grid search, random search, and Bayesian optimization. Beauty product RNN1000optimization and training may be conducted numerous times until model performance and accuracy have met a predetermined criteria for model performance.

After beauty product RNN1000has been fully trained, it may be implemented by system100in order to generate personalized beauty product recommendations using user response data as input. After deployment and as new users and products are introduced to system100, beauty product RNN1000may continuously optimize and update model parameters using user feedback to adjust hyperparameters and network weights.

According to an embodiment, the recurrent layers1011,1015may be replaced by long short term memory (LSTM) cells which are a type of recurrent neural network with built in memory which may increase the model performance and improve beauty product recommendations.

Detailed Description of Exemplary Aspects

FIG. 3is a block diagram illustrating an exemplary questionnaire300that may be used for gathering user information, preferences, concerns, and goals for the creation of a user's individual profile for matching individuals with beauty product recommendations, according to an embodiment. According to an embodiment, a questionnaire300may be configured for a plurality of functions. As a first function, the questionnaire300may be configured to develop a physical profile of the intended user. The physical profile of the intended user may be developed through the use of fact-based inputs310. For example, fact-based inputs310may include, but are not limited to, hair type, hair porosity, hair density, skin type, skin complexion, allergies and the like. The fact-based inputs310can also include environmental inputs such as the non-limiting examples of climate and altitude. The fact-based inputs310may also include lifestyle factors such as the non-limiting examples of exercise and water intake. In this way, questionnaire300can be used to gather and collate user information in order to produce a physical profile of an intended user that can be used as one form of input into a data analysis and recommendation engine160to produce as output individualized beauty product recommendations.

The questionnaire300may also be configured to develop a profile of concerns that the intended user may have. The concern profile of the intended user can be developed through the use of concern-based inputs320. For example, concern-based inputs320may include, but are not limited to, acne, acne breakouts, scarring, pigmentation, itchy scalp, dandruff, thinning hair and the like.

The questionnaire300may also be configured to develop a profile of preferences that the intended user may have regarding health and beauty products. The preference profile of the intended user can be developed through the use of preference-based inputs. For example, preference-based inputs may include, but are not limited to: prefers ethically sourced products (e.g., fair trade, organic, small batch, animal cruelty free, etc.); ingredient preferences (e.g., prefers gluten free products despite not having a gluten allergy), shipping and delivery preferences, health and beauty product preferences (e.g., prefers a face mask product over a face cream product, and various other types of preferences that cannot be captured via the fact-based inputs310, the concern-based inputs320, and the goal-based inputs330.

The questionnaire300may be further configured to develop a goal profile of the intended user, that is, what goals and expectations does the user have with regard to beauty products. The goal profile of the intended user may be developed through the use of goal-based inputs330. For example, goal-based inputs may include, but are not limited to, volumizing hair, curl definition, cleansing and balancing skin, softening and smoothing skin and the like.

While the questionnaire300illustrated inFIG. 3includes fact-based inputs310, concern-based inputs320, and goal-based inputs330, it should be appreciated that in other embodiments questionnaire300may include more or less inputs. For example, other inputs may include user preferences such as the non-limiting examples of paraben free products, sodium lauryl sulfate free products and the like. Other non-limiting examples of other inputs can include medical-related conditions such as, for example, pregnancy and menopause. Taken together, the fact-based input310responses, the concern-based input320responses, the preference-based inputs responses, and the goal-based input330responses may be used to create and populate an individual profile of an intended user. The user-submitted responses may be used to generate a requirement vector that can be processed by recommendation engine160to generate as output personalized beauty product recommendations.

FIG. 4is a diagram illustrating an exemplary product database400, according to an embodiment. According to an embodiment, product database400may be configured to include relevant information concerning beauty, hygiene, and health products, such as to facilitate matching products with individuals. Product database400can include detailed product information, such as the non-limiting examples of ingredients, functions of the ingredients, allergens, ingredient free data and the like. Additionally, each product may have a vector representation of the product details stored in the product database400. In this figure, product A401is shown, product B402is shown, and product C403is shown. However, in other embodiments, the product database400can include more or less products, more or less detailed product information and the product information can be presented in other formats.

FIG. 5is a diagram illustrating an exemplary recommended product list, according to an embodiment. According to an embodiment, the individual beauty product recommendations500can include information concerning hair-related products510and skin-related products520. While not illustrated, individual beauty product recommendations500can also include recommended makeup-related products, lipstick-related products, eye-related products, products to remove make-up, scent-related products and the like. Individual beauty product recommendations500may be displayed to a customer on an end-user device170such as a desktop or laptop computer, smart phone, tablet, smart wearable and other compute devices. A customer may be able to view the product recommendations500and then choose to add the items to an online shopping cart, or purchase the recommended products500.

FIG. 6is a flow diagram illustrating an exemplary method600for generating individualized beauty product recommendations utilizing a quiz and skin-tone analysis, according to an embodiment. According to an embodiment, the process begins by identifying the user601using, for example, a username and password that provides access to the system100. In the case that the user has not previously used the system, the user can proceed to registration to set up a new account602. The user's information and details (e,g., name, address, etc.) can then be stored in user database140. Once the user is identified by the system (e.g., logged in), data can be collected603to determine the user's needs (e.g., allow the system to store all quiz/questionnaire responses, allow the user to store images, etc.). The data collection step can be performed by users themselves by taking the quiz (and/or answering the questionnaire and/or conversing with a chatbot) or photographs on a consumer device (e,g., a laptop, desktop, mobile phone, tablet computer, smart wearables, etc.)604,605which will then automatically validate the photograph for color accuracy, quality, and other properties606. Information captured by the quiz (or questionnaire or chatbot) or the device can be stored in the user database(s)140for later reference and reuse.

If a user uploads a photograph of themselves, the skin-tone analysis algorithm(s) are triggered607. For more detailed information about the skin-tone analysis607process, please refer toFIG. 7. The output of the skin-tone analysis may be taken as input in creating personalized recommendations for foundation shade608. If a user does not upload a photograph of themselves, then the skin-tone analysis607step is bypassed and the process proceeds to step609. The data analysis and recommendation engine160may use the input response (e.g., quiz/questionnaire responses) by the user to create a mapping of the requirements609. The environmental conditions may be extracted for personalized recommendation by using the locational input provided by the user610. Using all user responses and the environmental factors a requirement vector may be created611. All available products are represented as vectors and a similarity score of all products may be calculated612using the requirement vector. The top product or products with the highest similarity score in different product categories may then be presented (e.g., displayed on a webpage) to the customer system user) as a product recommendation613. A customer may then proceed to purchase the product and have it delivered614. The delivered product and any customer feedback may be stored in the user database(s)140and used for recalibration615of the one or more machine and deep learning algorithms and/or models.

FIG. 7is a flow diagram illustrating an exemplary method700for conducting skin-tone analysis on a user-submitted photograph, according to an embodiment. According to an embodiment, skin-tone analysis is triggered when a user uploads a photograph701and may be implemented by skin analysis engine130. Skin analysis engine130detects the face702of the user. In a next step, the image may be cropped and analyzed to extract the face oval703of the user. At the next step, non-skin areas from the face are removed704(e.g., eyebrows, eyes, lips, etc.). At the next step, the image with the eliminated non-skin areas may be used as input into a classification network that detects the skin tone705. Then secondary features such as a beard, bindi, and jewelry are removed if detected706. As a last step, the dominant skin tones are extracted and output as skin-tone color707. The output skin-tone may then be used as an input to data processing and recommendation engine160for creating personalized beauty product recommendations.

FIG. 8is a flow diagram illustrating an exemplary method800for generating individualized beauty product recommendations utilizing a quiz and skin concern analysis, according to an embodiment. According to an embodiment, the process begins by identifying the user801using, for example, a username and password that provides access to the system100. In the case that the user has not previously used the system, the user can proceed to registration to set up a new account802. The user's information and details (e.g., name, address, etc.) can then be stored in user database140. Once the user is identified by the system (e.g., logged in), data can be collected803to determine the user's needs (e.g., allow the system to store all quiz/questionnaire responses, allow the user to store images, etc.). The data collection step can be performed by users themselves by taking the quiz (and/or answering the questionnaire and/or conversing with a chatbot) or photographs on a consumer device (e.g., a laptop, desktop, mobile phone, tablet computer, smart wearables, etc.)804,805which will then automatically validate the photograph for color accuracy, quality, and other properties806. Information captured by the quiz (or questionnaire or chatbot) or the device can be stored in the user database(s)140for later reference and reuse.

If a user uploads a photograph of themselves, the skin-concern analysis algorithm(s) are triggered807. For more detailed information about the skin-concern analysis807process, please refer toFIG. 9. The output of the shin-concern analysis may be taken as input in creating personalized recommendations. The data analysis and recommendation engine160may use the input response (e.g., quiz/questionnaire responses) by the user to create a mapping of the requirements808. The environmental conditions may be extracted for personalized recommendations by using the locational input provided by the user809. Using all user responses and the environmental factors, a requirement vector may be created810. All available products are represented as vectors and a similarity score of all products may be calculated811using the requirement vector. The top product or products with the highest similarity score in different product categories may then be presented (e.g., displayed on a webpage) to the customer (i.e., system user) as a product recommendation812. A customer may then proceed to purchase the product and have it delivered813. The delivered product and any customer feedback may be stored in the user database(s)140and used for recalibration814of the one or more machine and deep learning algorithms and/or models.

FIG. 9is a flow diagram illustrating an exemplary method900for conducting skin concern analysis on a user-submitted photograph, according to an embodiment. According to an embodiment, skin concern analysis may be triggered when a user-uploads a photograph901and may be implemented by skin analysis engine130. Skin analysis engine130detects the face902of the user. In a next step, the image may be cropped and analyzed to extract the face oval903of the user. At the next step, non-skin areas from the face are removed904(e.g., eyebrows, eyes, lips, etc.). At the next step, the image with the eliminated non-skin areas may be used as input into an image segmentation network that detects and classifies the skin concerns905. The output skin concerns may then be used as an input to data processing and recommendation engine160for creating personalized beauty product recommendations.

FIG. 11is a flow diagram illustrating an exemplary method1100for training a beauty product recurrent neural network for making personalized beauty product recommendations, according to an embodiment. According to an embodiment, the training process begins by creating training datasets using retrieved subsets of user response data and product information1101. The subsets of data may be pre-processed subsets ready to be input into a neural network. The training datasets may then be fed into a recurrent neural network1102comprising one or more hidden recurrent layers which may embed the input training datasets and extract weighted features which define the datasets as it passes through each of the one or more hidden recurrent layers. The hidden recurrent layers constitute an encoder. The next step is to train the encoder to learn the features of the inputted subset of user response data and the subset of product information such that they may be encoded into a user requirement vector and a product vector, respectively, existing within an encoded feature space1103. As a next step, product vectors are extracted from the feature space and fed into a decoder to determine one or more beauty products to recommend1104. The decoder may comprise one or more fully connected layers. At this point in the training cycle the recommended beauty products may be checked for usefulness as well as validating model performance using a pre-determined criteria for success1105. If the recommend products are not useful, or model performance does not meet the pre-determined criteria for success, then the networks parameters and weights can be updated to adjust and improve beauty product RNN performance1106and then repeat steps1103-1105until the pre-determined criteria of success is met. Once the model performance is at a satisfactory level and useful product recommendations are being generated as output, the beauty product RNN may be deployed in system100and used to make personalized beauty product recommendations using non-training data1107.

Hardware Architecture

In addition, in some aspects, servers32may call external services37when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services37may take place, for example, via one or more networks31. In various aspects, external services37may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications24are implemented on a smartphone or other electronic device, client applications24may obtain information stored in a server system32in the cloud or on an external service37deployed on one or more of a particular enterprise's or user's premises. In addition to local storage on servers32, remote storage38may be accessible through the network(s)31.