Abstract: Methods are provided for improving computer resource utilization based on computer-implemented analysis of secure workspace data retrieved over a network connection. In one aspect, a plurality of encrypted workspace data is retrieved from a plurality of secure workspace data repositories associated with a plurality of human resources service workspaces. The plurality of encrypted workspace data is decrypted to produce a plurality of decrypted workspace data. A plurality of customer engagement metrics are calculated based on the plurality of decrypted workspace data, wherein each of the customer engagement metrics is associated with at least one of the plurality of human resources service workspaces. A composite customer engagement score is calculated based on the plurality of customer engagement metrics. Systems and machine-readable media are also provided.

Abstract: Computer systems, devices, and methods are provided for improving the technology related to asset condition monitoring. For instance, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and using a predictive model that is configured to output an indication of whether at least one failure type from the group of possible failure types is likely to occur at an asset within the given period of time in the future.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.

Abstract: Disclosed herein are systems, devices, and methods related to the interaction between assets and remote analytics systems, which may involve an asset compressing time-series values captured for a given operating data variable by producing an approximated version of the time-series values that comprises a set of approximation function-base region pairs.

Abstract: Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.

Abstract: Disclosed herein are systems, devices, and methods related for generating a recommendation to repair an asset based on operating data. A computing system may be configured maintain a hierarchy that comprises two or more distinct levels of conditions that operating data may be checked against in order to determine which repair recommendation (if any) should be output. The hierarchy may include at least (1) a first condition that corresponds to a first repair recommendation having a first level of precision, and (2) a second condition that corresponds to a second repair recommendation having a second level of precision. Once repair recommendations are identified for satisfied conditions, the computer system may select the recommendation having the highest level of precision and then cause that recommendation to be output.

Abstract: Examples of systems, devices, and methods related to assets and asset operating conditions involve determining health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.

Abstract: Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring. In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.

Abstract: Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to patient response to outreach options of healthcare facilities. Examples involve determining patient response metrics for patients based on patient data and/or other data. A patient response metric indicates a probability that a patient will perform a medically-related patient action in response to an outreach option. Example output data can indicate various outreach options, patient response metrics for outreach options, expected value of implementing outreach options, and/or recommendations of outreach options.

Abstract: Implementations generally relate to forecasting a support requirement for a health care unit to use in preparing patient support at a target time. In some implementations, a method includes accessing external conditions data for a plurality of different external conditions projected for the location at the target time. The target time and the external conditions data may be provided into a prediction model, which identifies one or more reference times prior to the target time that are predictive of the target time, accesses historical data for the reference times, and outputs the data indicating a support requirement based on the historical data and external conditions data.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.