Abstract:
Aspects of the technology described herein provide a personalized computing experience for a user based on a user-visit-characterized venue profile. In particular, user visits to a venue are determined. For those visits, user characteristics and/or visit characteristics are determined. User similarities and visit features similarities may be determined and associated with the venue to form the user-visit-characterized venue profile. The user-visit-characterized venue profile may be provided to an application or service such as a personal assistant service associated with the user, or may be provided as an API to facilitate consumption of the user-visit-characterized venue profile by an application or service.
Abstract:
Systems and methods are provided for associating project entities with projects. In various implementations, user activity data is monitored based on sensor data from at least one user device associated with a user. From the monitored user activity data, entity features are determined of project entities corresponding to the user activity data. Time slots are extracted from the project entities. The time slots are clustered based the entity features of ones of the project entities corresponding to the time slots. The project entities are grouped into projects based on the clustered time slots. Project tags corresponding to the projects are applied to the project entities based on the grouping of the project entities. Content is personalized to the user based on the project tags applied to the project entities.
Abstract:
Aspects of the technology described herein provide a personalized computing experience for a user based on a predicted future semantic location of the user. In particular, a likely future location (or sequences of future locations) for a user may be determined, including contextual information about the future location. Using information from the current context of the user's current location with historical observations about the user and expected user events, out-of-routine events, or other lasting or ephemeral information, a prediction of one or more future semantic locations and corresponding confidences may be determined and used for providing personalized computing services to the user. The prediction may be provided to an application or service such as a personal assistant service associated with the user, or may be provided as an API to facilitate consumption of the prediction information by an application or service.
Abstract:
Methods, computer systems, and computer storage media are provided for inferring sleep-related aspects for a user based, in part, on sensor data reflecting user activity detected by one or more sensors. In an embodiment, a user sleep model is trained using a dataset that includes previously-sensed data, descriptive information associated with the previously-sensed data, and/or interpretive data extracted from the previously-sensed data describing circumstances surrounding users when the data was acquired. In an embodiment, services providing time-sensitive recommendations personalized for a user's sleeping pattern using the inferred sleep-related aspects.
Abstract:
Aspects of the technology described herein improve the clarity of information provided in automatically generated notifications, such as reminders, tasks, alerts or other messages or communications provided to a user. The clarity may be improved through augmentations that provide additional information or specificity to the user. For example, instead of providing a notification reminding the user, "remember to send the slides before the meeting," the user may be provided with a notification reminding the user "remember to send the updated sales presentation before the executive committee meeting on Tuesday. The augmentation may take several forms including substituting one word in the notification with another more specific word, adding additional content such as a word or phrase to the notification without altering the existing content, and/or by rephrasing the content for grammatical correctness and/or clarity.
Abstract:
Systems, methods, and computer-readable storage media are provided for automating personalized out-of-the-box and ongoing in-application settings. A triggering event is detected for an exchange of information between an information service and one or more application or service. A trust level and domain of information of the one or more application or service is determined. Based on the trust level and domain of information, information to be shared with the one or more application or service is identified and the identified information is shared. The information to be shared can be all of the requested information, some of the requested information, or none of the requested information.
Abstract:
In some implementations, a method includes extracting completion criteria of an action item and parameters of the completion criteria from a message portion of a user message between users. In response to determining, from sensor data provided by one or more sensors associated with a user, the user practices a routine, a routine-related aspect is generated from a user routine model of the user for the routine. It is inferred that at least one of the extracted completion criteria of the action item is unsatisfied based on the extracted parameters and the identified routine-related aspect. Based on the inferring, a notification is provided to at least one user associated with the action item.
Abstract:
A logical location of a user may be inferred using semantics of the user's computing device(s). The logical locations may correspond to venues visited by the user, such as frequently visited venues, referred to as hubs. Logical hubs, representing logical locations, may be tagged to the computing device by classifying the device as being associated with a logical hub. Classification may be based on signals or features of the device, such as device usage or device characteristics. The device may be monitored to detect user activity. Based on analysis of the user activity and associated logical hub, the user's logical location may be inferred. A computer user-experience may be personalized to the user based on the user's inferred logical location or logical hub(s) associated with the device. In some cases, geographical location information is not used to determine the logical hubs or infer a user's logical location.
Abstract:
An enhanced computing experience is provided to a user based on an optimal schedule of user activity or activity types. The optimal schedule may be generated based on learned user activity patterns and determined or inferred future activity likely to be performed by the user. A user's current context and user preferences also may be considered when generating an optimal schedule. The optimal schedule may be utilized by a computerized personal assistant application to provide an enhanced computing experience for the user. For example, a calendar management program manages a user's calendar events to be more in accordance with an optimal schedule; a personal performance application generates a recommended daily schedule for the user; and a notifications service manages electronic notifications to the user or to other people in accordance with an optimal schedule.
Abstract:
In some implementations, a user schedule is constructed comprising planned events of a user. It is determined that a planned event of the planned events corresponds to a divergence from a pattern of detected instances of a routine of the user based on user activity data from a set of sensors. An occurrence of an event of the user is determined from the user activity data. It is determined the divergence failed to occur based on the occurrence of the event and the user schedule. Based on the determining the divergence failed to occur, content associated with the routine is caused to be presented on a user device.