Abstract: Techniques described herein are directed to a sentiment analysis engine for classifying and quantifying customer sentiments between a customer and an agent. The sentiment analysis engine includes a data acquisition engine configured to receive data from a plurality of different communication channels. A preprocessing engine of the sentiment analysis engine is configured to apply rule(s) that are configured to perform formatting and/or filtering operations on the received data and/or isolate customer-provided messages from agent-provided messages. A customization engine of the sentiment analysis engine is configured to perform channel-specific customizations on the received data, such as, but not limited to interpreting the data in a channel-specific manner A batch scoring engine of the sentiment analysis engine is configured to quantify customer sentiments in real-time (e.g.

Abstract: A computer-implemented technique is described herein for rescheduling events within calendars to achieve a target objective with respect to target occurrences of a particular kind in the calendars. For instance, the target occurrences may correspond to instances of free time, and the target objective is to increase the amount of minimum-sized free-time blocks within the calendars, e.g., by favoring the introduction of large free-time blocks of minimum size (e.g., two-hour blocks), and disfavoring the presence of smaller-sized free-time blocks scattered among calendar events. In other words, the technique defragments the calendars with respect to free time expressed in those calendars. At the same time, the technique honors (when possible) participant-related constraints associated with calendar events. Further, the technique penalizes modification of an event as a function of a temporal distance over which the event is moved; this penalization has the effect of reducing disruption to the calendars.

Abstract: Techniques described herein are directed to a sentiment analysis engine for classifying and quantifying customer sentiments between a customer and an agent. The sentiment analysis engine includes a data acquisition engine configured to receive data from a plurality of different communication channels. A preprocessing engine of the sentiment analysis engine is configured to apply rule(s) that are configured to perform formatting and/or filtering operations on the received data and/or isolate customer-provided messages from agent-provided messages. A customization engine of the sentiment analysis engine is configured to perform channel-specific customizations on the received data, such as, but not limited to interpreting the data in a channel-specific manner A batch scoring engine of the sentiment analysis engine is configured to quantify customer sentiments in real-time (e.g.

Abstract: Examples are disclosed herein that relate to correlating health data outcomes to work data of a user and recommending future work activities based upon the correlation. One example provides a computing device configured to monitor health data relating to health behavior of a user over time, determine a baseline value for a health outcome of the user based on the monitored health data, and receive work data relating to a work schedule of the user. Responsive to the health outcome deviating from the baseline value, the computing device is configured to determine that the deviation in the health outcome correlates with the work data, and based at least upon determining that the deviation in the health outcome correlates with the work data, output a notification of the determination to the user.

Abstract: A method for generating productivity insights includes receiving health data for a user of a productivity evaluation service. From the health data, health behaviors and health effects of the user are determined. Productivity data for the user is received, and from the productivity data, productivity behaviors and productivity effects of the user are determined. Associations between changes in the health data and changes in the productivity data are identified. Based on one of the associations, a productivity insight is generated for the user including a prompt to engage in a health behavior that is associated with a desirable productivity effect.

Abstract: Examples are disclosed herein that relate to modifying an analysis of personal behavior based on determining a subset of personal data to be non-routine. One example provides a computing device configured to receive personal data relating to personal behavior of a user, receive contextual data regarding the personal data, determine a subset of the personal data to be non-routine based upon the contextual data, and modify an analysis of personal behavior based upon the subset of the personal data determined to be non-routine.

Abstract: Examples are disclosed herein that relate to integrating health data and calendar data of one or more users and providing insights for a selected user to help the user accomplish an outcome of interest. The insights may be identified based on a group of cohorts determined to be similar to the selected user and/or used to predict a likelihood that the selected user will achieve an outcome of interest. Additional insights may be provided by monitoring an effect that following a recommendation has on the selected user achieving the outcome of interest. Recommendations and/or updates to recommendations may be provided based on the insights.

Abstract: Examples are disclosed herein that relate to integrating health data and work data of one or more users and recommending a time to perform a health activity. One example provides a computing device configured to receive health data relating to health behavior, the health data comprising information regarding a relationship between health activity scheduling and health outcome, receive work data relating to work activities, compare the health data and the work data to determine a time at which to recommend performing a health activity, and output a recommendation regarding the time to perform the health activity.

Abstract: A computer-implemented technique is described herein that assists an end-user in setting up an event that involves a set of participants. The technique identifies a proposed time slot that satisfies two objectives. First, the time slot satisfies participant-related constraints pertaining to the event. Second, the time slot satisfies a target objective that is defined as a function of target calendar occurrences of a particular kind, as measured across all of the calendars of the participants. The computer-implemented technique has the effect, upon repeated scheduling of events, of defragmenting the calendars with respect to target occurrences that appear in the calendars. In one implementation, the target objective is a free-time objective, and the technique operates by favoring the introduction of large free-time blocks of minimum size (e.g., two-hour blocks), and disfavoring the presence of smaller-sized free-time blocks scattered among events.

Abstract: A computer-implemented technique is described herein for rescheduling events within calendars to achieve a target objective with respect to target occurrences of a particular kind in the calendars. For instance, the target occurrences may correspond to instances of free time, and the target objective is to increase the amount of minimum-sized free-time blocks within the calendars, e.g., by favoring the introduction of large free-time blocks of minimum size (e.g., two-hour blocks), and disfavoring the presence of smaller-sized free-time blocks scattered among calendar events. In other words, the technique defragments the calendars with respect to free time expressed in those calendars. At the same time, the technique honors (when possible) participant-related constraints associated with calendar events. Further, the technique penalizes modification of an event as a function of a temporal distance over which the event is moved; this penalization has the effect of reducing disruption to the calendars.