Abstract: Disclosed herein are system, method, and computer program product embodiments for determining a user-preferred attribute type. An embodiment operates by maintaining user-presented attributes associated with user-presented records, wherein the user-presented attributes comprise one or more user-presented attribute types. After receiving a user-desired attribute of the user-presented attributes, a user-preferred attribute type of the user-presented attribute types is determined based on the user-presented attributes and the user-desired attribute. Thereafter, a new record and associated attribute are to be presented with the new attribute being of the user-preferred type.

Abstract: Techniques and technologies for using an intelligent meeting classifier to assess and enhance a work-related productivity are described. In at least some embodiments, a system includes a processing component operatively coupled to a memory; a productivity analyzer configured to perform operations including classifying one or more calendar events based at least partially on calendar data associated with one or more users; assessing one or more productivity metrics based at least partially on one or more calendar event classifications; determining one or more situation-enhancing operations based on the assessed one or more productivity metrics; and performing the one or more situation-enhancing operations.

Abstract: Techniques and technologies for using an intelligent meeting classifier to assess and enhance a work-related productivity are described. In at least some embodiments, a system includes a processing component operatively coupled to a memory; a productivity analyzer configured to perform operations including classifying one or more calendar events based at least partially on calendar data associated with one or more users; assessing one or more productivity metrics based at least partially on one or more calendar event classifications; determining one or more situation-enhancing operations based on the assessed one or more productivity metrics; and performing the one or more situation-enhancing operations.

Abstract: A system and method is provided for generating a dynamic comprehensive domain review. A domain review engine obtains authoritative literature associated with a domain to extract insights using entity recognition and relationship extraction, and ranks the extracted results to generate a dynamic domain review.

Abstract: Techniques and technologies for using an intelligent meeting classifier to assess and enhance a work-related productivity are described. In at least some embodiments, a system includes a processing component operatively coupled to a memory; a productivity analyzer configured to perform operations including classifying one or more calendar events based at least partially on calendar data associated with one or more users; assessing one or more productivity metrics based at least partially on one or more calendar event classifications; determining one or more situation-enhancing operations based on the assessed one or more productivity metrics; and performing the one or more situation-enhancing operations.

Abstract: A system and method is provided for generating a dynamic comprehensive domain review. A domain review engine obtains authoritative literature associated with a domain to extract insights using entity recognition and relationship extraction, and ranks the extracted results to generate a dynamic domain review.

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: Determining stress and productivity insights based on computerized data is described. Productivity data and stress-related behavior data associated with a user may be determined. The stress-related behavior data may be based on sensor data received from sensor(s) associated with a device corresponding to the user. The stress-related behavior data may be utilized to determine a first value indicative of stress associated with the user and the productivity data may be utilized to determine a second value indicative of productivity of the user. The productivity data, the stress-related behavior data, the first value, and/or the second value may be utilized to determine a recommendation. The recommendation may be intended to modify the first value and/or the second value. A user interface configured to communicate the recommendation to the user may be presented via the device.

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 method of estimating at least one mechanical property value of a rock formation in the geologic past. In one embodiment, a correlation model is generated which correlates at least one mechanical property with a porosity value and a composition value relative to a first rock type. An estimation of a past porosity value and a past composition value is then generated for the rock formation at a point of time in the past. A mechanical property value at the point of time in the past is identified in the correlation model based on the estimated past porosity and past composition values.

Abstract: A method of estimating at least one mechanical property value of a rock formation in the geologic past. In one embodiment, a correlation model is generated which correlates at least one mechanical property with a porosity value and a composition value relative to a first rock type. An estimation of a past porosity value and a past composition value is then generated for the rock formation at a point of time in the past. A mechanical property value at the point of time in the past is identified in the correlation model based on the estimated past porosity and past composition values.