Abstract: A computer-implemented method includes extracting, by one or more processors of one or more computing devices, a product family name from each of a plurality of unstructured product titles associated with a plurality of products. The method further includes determining, by the one or more processors, a degree of similarity between model numbers of the plurality of products. The method further includes determining, by the one or more processors, that at least two of the plurality of products are variants of one another by determining that the at least two of the plurality of products have a same extracted product family name and determining that the degree of similarity between the model numbers of the plurality of products is above a predetermined threshold.

Abstract: A method for providing document category recommendations may include training a machine learning algorithm; receiving, from a user, a selection of an anchor document; retrieving a user co-viewing sequence; generating, via the trained machine learning algorithm, a sequence embeddings set based on the anchor document and the user co-viewing sequence; comparing the generated embeddings set to respective embeddings for a plurality of candidate sets; determining the candidate set closest to the generated embeddings; and presenting at least one category from the closest candidate set.

Abstract: A method for predicting a next user selection in an electronic user interface includes receiving a sequence of user selections through the electronic user interface, determining a context embedding vector according to the sequence of user selections, querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embeddings vector, to obtain a knowledge-enhanced representation of the sequence, determining, with a graph neural network respective of the knowledge graph, based on the knowledge-enhanced representation, a respective representation of each selection in the sequence of user selections, and determining a predicted next user selection according to the respective representations of the selections in the sequence of user selections.

Abstract: A computer implemented method for determining object compatibility includes obtaining a first data set, a second data set, and a first compatibility model. The method also includes determining, by the compatibility system, error instances in the second data set by applying the first compatibility model to the second data set. The method further includes determining, by the compatibility system, labeling rules based on the error instances, and determining a third data set by applying the labeling rules to the first data set. The method also includes determining, by the compatibility system, a second compatibility model based on the third data set and determining an ensemble compatibility model based on the first compatibility model and the second compatibility model. The method further includes determining, by the compatibility system, a product recommendation based on the ensemble compatibility model and a user selection of a first product.

Abstract: A method for predicting a next user selection in an electronic user interface includes receiving, from a user, a sequence of selections of documents and generating, for each document in the sequence, a respective attribute vector. The attribute vector includes a numerical attribute vector portion representative of numerical attributes of the document, a category attribute vector portion representative of category information of the document, a text content vector portion representative of text content of the document, and an image content vector portion representative of an image in the document. The method further includes inputting the attribute vectors of the sequence into a machine learning model, and outputting, to the user, in response to the sequence of selections, a predicted next document selection according to an output of the machine learning model.

Abstract: A method of determining item features and organizing an interface according to the features includes determining a plurality of features of a plurality of items, the plurality of items accessible through an electronic interface, applying a plurality of machine learning models to the determined features, wherein each of the machine learning models calculates a correlation of each feature to characteristic determinative of user selection on the electronic interface, calculating respective Shapley values of each correlation determined by each of the plurality of machine learning models, determining one or more of the item features that are most strongly correlated with the determinative characteristic according to the respective Shapley values, and causing the electronic interface to be organized according to the determined most strongly correlated item features.

Abstract: A method for determining a user experience for an electronic user interface includes defining a test period for testing two or more versions of an electronic user interface, receiving, from each of a plurality of users during the test period, a respective request for the electronic user interface, determining, for each of the plurality of users, a respective version of the two or more versions of the electronic user interface by maximizing test power during the test period while maintaining higher in-test rewards than an A/B test or maximizing the rewards during the test period while maintaining a test power no worse than an A/B test, and causing, for each of the plurality of users, the determined version of the electronic user interface to be delivered to the user.

Abstract: A method for predicting a next user selection in an electronic user interface, such as a website, includes training a machine learning model according to a training data set, the training data set including a plurality of token sets, each token set representative of a respective document accessible through the interface, each token set including a plurality of words, each word describing a characteristic of the document, to create a trained model. The method further includes receiving, from a user, a sequence of selections of documents, inputting the sequence of selections to the trained model, and outputting to the user, in response to the sequence of selections, a predicted next document selection according to an output of the trained model.

Abstract: A computer-implemented method includes extracting, by one or more processors of one or more computing devices, a product family name from each of a plurality of unstructured product titles associated with a plurality of products. The method further includes determining, by the one or more processors, a degree of similarity between model numbers of the plurality of products. The method further includes determining, by the one or more processors, that at least two of the plurality of products are variants of one another by determining that the at least two of the plurality of products have a same extracted product family name and determining that the degree of similarity between the model numbers of the plurality of products is above a predetermined threshold.

Abstract: This disclosure includes systems and methods for providing accessory product recommendations to a user that may include items related to an anchor product selected by the user. The determination of accessory product recommendations may be made based on compatibility between anchor and accessory products, based on manually entered anchor-accessory product relationships, using an algorithm trained to determine anchor-accessory product relationships, or using other methods. The accessory recommendations may be provided along with the anchor product on an electronic user interface, such as a website, in response to the user's selection of the product through the electronic user interface. In this way, the accessory and anchor products may be viewed and/or selected by the user with a minimal number of clicks or user interactions through the electronic user interface.

Abstract: A method of providing purchase recommendations to a user may include tracking user comparisons of products on an electronic commerce system, receiving a user selection of an anchor product from the products through an electronic user interface of the electronic commerce system, designating a recommended product from the products for recommendation to the user through the electronic commerce system according to a frequency with which the recommended product is compared with the anchor product based on the tracking, and presenting the designated recommended product to the user responsive to the user's selection of the anchor product.

Abstract: This disclosure provides systems, methods and apparatus for touch and gesture recognition, using a field sequential color display. The display includes a processor, a lighting system, and an arrangement for spatial light modulation that includes an array of light modulators. Each light modulator is switchable between an open position that permits transmittance of light from the lighting system through a respective aperture and a shut position that blocks light transmission through the respective aperture. The processor switches the light modulators in accordance with a first modulation scheme to render an image and in accordance with a second modulation scheme to selectively pass object illuminating light through at least one of the respective apertures. A light sensor receives light resulting from interaction of the object illuminating with an object and outputs a signal to the processor. The processor recognizes, from the output of the light sensor, a characteristic of the object.

Abstract: Methods, systems, computer-readable media, and apparatuses for determining a location fix for optically-based active stylus input recognition technology are presented. The method includes, from an active light source capable of being handled by a user, emitting an electromagnetic wave signal in an initial direction toward a planar layer. The method also includes directing the electromagnetic wave signal in one or more lateral directions different from the initial direction, toward a plurality of detectors positioned at peripheral locations relative to the planar layer. The method further includes receiving the directed electromagnetic wave signal at the plurality of detectors. The method additionally includes, based on the directed electromagnetic wave signal received at the plurality of detectors, estimating a position of the user-controlled light source.

Abstract: Systems, methods, and apparatus for recognizing user interactions with an electronic device are provided. Implementations of the systems, methods, and apparatus include surface and air gesture recognition and identification of fingertips or other objects. In some implementations, a device including a plurality of detectors configured to receive signals indicating interaction of an object with the device at or above a detection area, such that a low resolution image can be generated from the signals, is provided. The device is configured to obtain low resolution image data from the signals and obtain a first reconstructed depth map from the low resolution image data. The first reconstructed depth map may have a higher resolution than the low resolution image. The device is further configured to obtain a second reconstructed depth map from the first reconstructed depth map. The second reconstructed depth map may provide improved boundaries and less noise within the object.

Abstract: Methods, systems, computer-readable media, and apparatuses for determining a location fix for optically-based active stylus input recognition technology are presented. The method includes, from an active light source capable of being handled by a user, emitting an electromagnetic wave signal in an initial direction toward a planar layer. The method also includes directing the electromagnetic wave signal in one or more lateral directions different from the initial direction, toward a plurality of detectors positioned at peripheral locations relative to the planar layer. The method further includes receiving the directed electromagnetic wave signal at the plurality of detectors. The method additionally includes, based on the directed electromagnetic wave signal received at the plurality of detectors, estimating a position of the user-controlled light source.

Abstract: This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for sensing touch and/or gestures in the near-field area overlying a light-guiding layer within a device or other optical sensing system. In one aspect, modulated infrared light is emitted into the overlying area, and the light reflected by objects within the overlying area is redirected through the light-guiding layer to infrared sensors. In one aspect, a masking structure can be located between the light-guiding layer and the infrared sensors. In some aspects, probability mapping or backtracing can be used to estimate the locations of objects within the overlying area.

Abstract: A microscopy method and configuration provides the ability to achieve wide field view and high resolution simultaneously. A beam array generator generates an M×N light beam array that illuminates a sample and an M×N sensor array. The sensor array obtains a whole microscopy image of the sample based on the light beam array. Each light beam of the array corresponds to one unique pixel sensor in the sensor array. A scanning of all light beams of the light beam array covers a whole area of the sample.

Abstract: This disclosure relates to a channel waveguide, at least a portion of which is disposed within a viewing area of an interactive display, proximate to and substantially parallel to a user interface surface of the interactive display. A source of a first electromagnetic or acoustic signal is input to the channel waveguide. At least one detector is configured to receive, from the channel waveguide, a second signal corresponding to the first signal and to output a third signal indicative of a position of an object proximate to the channel waveguide.

Abstract: A surface wave assisted system having an aperture layer with a surface and an aperture, and a plurality of grooves around the aperture. The plurality of grooves is configured to generate an optical transfer function at the aperture by inducing a surface wave for interfering with transmission of light of a range of spatial frequency.

Abstract: A method for noninvasive analysis of a retina includes exposing the retina to one or more first sets of at least three illumination light signals. The at least three illumination light signals each have a different wavelength. The method also includes optically collecting a reflected light signal for each of the at least three illumination light signals of the one or more first sets. Each of the reflected light signals is a portion of the respective illumination light signal reflected by the retina. The method further includes detecting the reflected light signals of the one or more first sets as a function of intensity. The method still further includes determining a first opsin density using the detected intensity of each of the reflected light signals of the one or more first sets.