A computer-implemented method for performing data difference evaluation is provided. Aspects include obtaining a first data set and a second data set, creating a first plurality of feature vectors by inputting the first data set into each of a plurality of models, and creating a second plurality of feature vectors by inputting the second data set into each of the plurality of models. Aspects also include identifying a mapping between elements of the first plurality of vectors and elements the second plurality of feature vectors created by a same model of the plurality of models, calculating, for each of the plurality of models based at least in part on the mapping, a model distance between the first data set and the second data set, and calculating, based at least in part on the model distances, an ensemble distance between first data set and the second data set.

A physical space includes obstacles with different characteristics. Different sensors may detect some obstacles well, while other obstacles are poorly detected. An autonomous mobile device (AMD) uses data from various sensors to determine information about the physical space that is expressed in layers. Each layer represents specified areas within at least a portion of the physical space, with each area having a value representative of whether an obstacle is present. Some layers may represent specific volumes, or height ranges. For example, different layers may represent a low height, a medium height, and a high height. Aggregated data may be determined using the values from multiple layers. The aggregated data may be calculated using an aggregation profile to specify a relative weighting for the values in a particular layer. The aggregated data may then be processed to determine maps, such as a navigation map for autonomous movement, floorplan, and so forth.

A physical space includes obstacles with different characteristics. Different sensors may detect some obstacles well, while other obstacles are poorly detected. An autonomous mobile device (AMD) uses data from various sensors to determine information about the physical space that is expressed in layers. Each layer represents specified areas within at least a portion of the physical space, with each area having a value representative of whether an obstacle is present. Some layers may represent specific volumes, or height ranges. For example, different layers may represent a low height, a medium height, and a high height. Aggregated data may be determined using the values from multiple layers. The aggregated data may be calculated using an aggregation profile to specify a relative weighting for the values in a particular layer. The aggregated data may then be processed to determine maps, such as a navigation map for autonomous movement, floorplan, and so forth.

A system and method for identifying a request of a service. The method includes generating vector embeddings for raw data of events using a machine learning model, wherein the machine learning model is trained to indicate semantic meaning of at least one event of the raw data of events; clustering, based on the vector embeddings, the at least one event of the raw data of events into a plurality of clusters, wherein a subset of the plurality of clusters includes relevant events in the data of events; and identifying a request as a sequence of events from the subset of the plurality of clusters.

A system and method for identifying a request of a service. The method includes generating vector embeddings for raw data of events using a machine learning model, wherein the machine learning model is trained to indicate semantic meaning of at least one event of the raw data of events; clustering, based on the vector embeddings, the at least one event of the raw data of events into a plurality of clusters, wherein a subset of the plurality of clusters includes relevant events in the data of events; and identifying a request as a sequence of events from the subset of the plurality of clusters.

In some implementations, a device may obtain first data associated with one or more accounts. The device may process the first data to obtain clustering information associated with the first data. The device may cluster the first data into one or more clusters based on the clustering information. The device may identify, via a second machine learning model, one or more deviation patterns associated with a portion of the first data that is included in a cluster of the one or more clusters. The device may determine, for the cluster, one or more operations to be performed to mitigate deviations based on the one or more deviation patterns. The device may perform an action, that is based on the one or more operations, associated with second data grouped into the cluster.

In some implementations, a device may obtain first data associated with one or more accounts. The device may process the first data to obtain clustering information associated with the first data. The device may cluster the first data into one or more clusters based on the clustering information. The device may identify, via a second machine learning model, one or more deviation patterns associated with a portion of the first data that is included in a cluster of the one or more clusters. The device may determine, for the cluster, one or more operations to be performed to mitigate deviations based on the one or more deviation patterns. The device may perform an action, that is based on the one or more operations, associated with second data grouped into the cluster.