A computing device is described that includes a camera configured to capture an image of a user of the computing device, a memory configured to store the image of the user, at least one processor, and at least one module. The at least one module is operable by the at least one processor to obtain, from the memory, an indication of the image of the user of the computing device, determine, based on the image, a first emotion classification tag, and identify, based on the first emotion classification tag, at least one graphical image from a database of pre-classified images that has an emotional classification that is associated with the first emotion classification tag. The at least one module is further operable by the at least one processor to output, for display, the at least one graphical image.
Systems and methods are provided for identifying and rendering content relevant to a user’s current mental state and context. In an aspect, a system includes a state component that determines a state of a user during a current session of the user with the media system based on navigation of the media system by the user during the current session, media items provided by the media system that are played for watching by the user during the current session, and a manner via which the user interacts with or reacts to the played media items. In an aspect, the state of the user includes a mood of the user. A selection component then selects a media item provided by the media provider based on the state of the user, and a rendering component effectuates rendering of the media item to the user during the current session.
The technology relates to methods for detecting and classifying emotions in textual communication and using this information to suggest graphical indicia such as emoji, stickers or GIFs to a user. Two main types of models are fully supervised models and few-shot models. In addition to fully supervised and few-shot models, other types of models focusing on the back-end (server) side or client (on-device) side may also be employed. Server-side models are larger-scale models that can enable higher degrees of accuracy, such as for use cases where models can be hosted on cloud servers where computational and storage resources are relatively abundant. On-device models are smaller-scale models, which enable use on resource-constrained devices such as mobile phones, smart watches or other wearables (e.g., head mounted displays), in-home devices, embedded devices, etc.
Methods, systems, and media for ambient background noise modification are provided. In some implementations, the method comprises: identifying at least one noise present in an environment of a user having a user device, an activity the user includes currently engaged in, and a physical or emotional state of the user; determining a target ambient noise to be produced in the environment based at least in part on the identified noise, the activity the user is currently engaged in, and the physical or emotional state of the user; identifying at least one device associated with the user device to be used to produce the target ambient noise; determining sound outputs corresponding to each of the one or more identified devices, wherein a combination of the sound outputs produces an approximation of one or more characteristics of the target ambient noise; and causing the one or more identified devices to produce the determined sound outputs.
Methods, systems, and media for personalizing computerized services based on mood and/or behavior information from multiple data sources are provided. In some implementations, the method comprises: obtaining information associated with an objective of a user of a computing device from multiple data sources; determining that a portion of information from each of the data sources is relevant to the user having the objective, wherein the portion of information is indicative of a physical or emotional state of the user of the computing device; assigning the user of the computing device into a group of users based at least in part on the objective and the portion of information from each of the data sources; determining a target profile associated with the user based at least in part on the objective and the assigned group; generating a current profile for the user of the computing device based on the portion of information from each of the data sources; comparing the current profile with the target profile to determine a recommended action, wherein the recommended action is determined to have a likelihood of impacting the physical or emotional state of the user; determining one or more devices connected to the computing device, wherein each of the one or more devices has one or more device capabilities; and causing the recommended action to be executed on one or more of the computing device and the devices connected to the computing device based on the one or more device capabilities.
A method for social interacting, including using a portable messaging device for designating, from time to time, a plurality of friends, selecting a mood, sending one or more representations of the selected mood to each of the plurality of designated friends, further selecting an updated mood, and further sending one or more representations of the updated mood to each of the plurality of designated friends, to supersede the previously sent one or more representations of the mood. A user interface is also described and claimed.