Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Abstract: Implementations for include providing one or more product designs using an intelligent design platform receiving a product indicator that indicates a product that is to be designed, transmitting a request to a contextual requirements system, the request including the product identifier and requesting one or more contextual requirements, determining a set of context models based on the product identifier, each context model in the set of context models being generated based on one or more scenes represented in a digital video, each scene depicting a contextual use of the product, providing a set of contextual requirements to the design generation system based on one or more context models, and inputting a set of aggregate requirements to a generative design tool that generates the one or more product designs based on the set of aggregate requirements, the set of aggregate requirements including at least one contextual requirement.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Abstract: This document describes multi-modal platforms that receive multi-modal inputs and generates interactive experiences based on the inputs. In one aspect, a method includes receiving input data comprising one or more images of an area of a sports venue. A person is detected in the one or more images. Apparel information including at least one of a team identifier or player identifier on athletic apparel of the person is identifier in the one or more images. An interactive experience that includes a particular sports-related character is selected based at least on the apparel information. The interactive experience is initiated on an interactive display. The interactive experience includes video of the particular sports-related character delivering a message to the person.

Abstract: This document describes a platform that processes multi-modal inputs received from multiple sensors and initiates actions that cause the user to transition to a target state. In one aspect, a method includes detecting, based on data received from sensors, a current state of a user. A set of candidate states to which the user can transition from the current state is identified based on the current state. A target state for the user is selected based on the data received from the sensors and/or the current state of the user. For each of multiple candidate states, a probability at which the user will transition from the current state to the target state through the candidate state is determined. A next state for the user is selected based on the probabilities. One or more actions are determined and initiated to transition the user from the current state to the next state.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Abstract: Implementations for include providing one or more product designs using an intelligent design platform receiving a product indicator that indicates a product that is to be designed, transmitting a request to a contextual requirements system, the request including the product identifier and requesting one or more contextual requirements, determining a set of context models based on the product identifier, each context model in the set of context models being generated based on one or more scenes represented in a digital video, each scene depicting a contextual use of the product, providing a set of contextual requirements to the design generation system based on one or more context models, and inputting a set of aggregate requirements to a generative design tool that generates the one or more product designs based on the set of aggregate requirements, the set of aggregate requirements including at least one contextual requirement.

Abstract: This document describes multi-modal platforms that receive multi-modal inputs and generates interactive experiences based on the inputs. In one aspect, a method includes receiving input data comprising one or more images of an area of a sports venue. A person is detected in the one or more images. Apparel information including at least one of a team identifier or player identifier on athletic apparel of the person is identifier in the one or more images. An interactive experience that includes a particular sports-related character is selected based at least on the apparel information. The interactive experience is initiated on an interactive display. The interactive experience includes video of the particular sports-related character delivering a message to the person.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.