Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: Automatically determining, with reduced (or no) input from the users of a group, a set of activity instances that the group of users has interest in performing. A representation of the set of activity instances can be rendered for consideration by a group, and the set of activity instances can be determined even when only limited criteria are specified. Optionally, in response to affirmative user interface input(s) directed to a rendered representation of the set of activity instances, one or more of the activity instances of the set can be confirmed through limited input(s) of one or more users of the group. Further, the automatic determination of the set of activity instances is optionally performed using one or more trained machine learning models that are trained to optimize a likelihood that the users of the group will find the set satisfactory.

Abstract: A first handwriting input is received comprising strokes corresponding to a set of first characters comprising one or more first characters forming a first language model unit. A set of candidate first characters and a set of candidate first language model units with corresponding probability scores are determined based on an analysis of the one or more sets of candidate first characters using the first language model and a corresponding first character recognition model. When no first probability score satisfies a threshold, one or more sets of candidate second characters and a set of candidate second language model units are determined based on an analysis of the first handwriting input using a second language model and a corresponding second character recognition model. A first candidate list is then output comprising at least one of the set of candidate second language model units.

Abstract: The present disclosure provides systems and methods that leverage machine learning to refine and/or predict sensor outputs for multiple sensors. In particular, systems and methods of the present disclosure can include and use a machine-learned virtual sensor model that has been trained to receive sensor data from multiple sensors that is indicative of one or more measured parameters in each sensor's physical environment, recognize correlations among sensor outputs of the multiple sensors, and in response to receipt of the sensor data from multiple sensors, output one or more virtual sensor output values. The one or more virtual sensor output values can include one or more of refined sensor output values and one or more predicted future sensor output value.

Abstract: A first handwriting input is received comprising strokes corresponding to a set of first characters comprising one or more first characters forming a first language model unit. A set of candidate first characters and a set of candidate first language model units with corresponding probability scores are determined based on an analysis of the one or more sets of candidate first characters using the first language model and a corresponding first character recognition model. When no first probability score satisfies a threshold, one or more sets of candidate second characters and a set of candidate second language model units are determined based on an analysis of the first handwriting input using a second language model and a corresponding second character recognition model. A first candidate list is then output comprising at least one of the set of candidate second language model units.