A guidance system includes: a station congestion estimating unit to estimate congestion situations in respective areas in a station and to output station congestion information indicating the estimated congestion situations in the respective areas in the station; a guidance determining unit to determine a mode of guiding station users on the basis of the station congestion information output by the station congestion estimating unit and to output guidance information indicating the mode that has been determined; and an output generation unit to generate an output signal for guiding the station users on the basis of the guidance information output by the guidance determining unit, to output the output signal that has been generated to an output device, and to cause the output device to provide output corresponding to the output signal.

Disclosed are systems, servers and methods for a multi-tenant framework that manages and controls operations of software as a service (SaaS) applications and services, and the data and metadata (e.g., files) created, updated and interacted with therefrom. The disclosed framework provides a centralized approach to managing the entitlement and provisioning of SaaS applications on client devices across a variety of channels on a network. The disclosed SaaS framework is configured for management, control, deployment and synchronization between devices, applications, systems and platforms both on-premises (on-prem or local devices/storage) and/or hosted on a network (e.g., a cloud platform, service or platform).

A supervisory control system is disclosed that provides an operator situational awareness interface use with monitoring a plurality of automated vehicles (AVs). The system is configured to: generate a map of a geographical area of interest; obtain location data and perceived risk data for a plurality of AVs in the geographical area; generate a vehicle icon corresponding to each AV; position the vehicle icon for each AV on the map based on the location data for a corresponding AV; apply a color coding to each vehicle icon based on a perceived risk level for a corresponding AV; and signal a display device to display an AV fleet map graphic that includes the color coded vehicle icons positioned on the map. The controller may be further configured to: generate an AV servicing queue graphic that displays vehicle icons in an order based on a determined servicing priority.

A network storage volume stores first entries in a first-mode storage bucket and a second entries in a second-mode storage bucket. The first-mode storage bucket has first bucket metadata, and the second-mode storage bucket has second bucket metadata. A computer-implemented method includes comparing a utilized capacity of the network storage volume to a target capacity information of the network storage volume to obtain a comparison result. Based on the comparison result, at least one bucket is selected to be purged from the buckets of the network storage volume based at least in part on bucket metadata of the buckets. The method further includes causing a purge of the at least one selected bucket from the network storage volume.

A network storage volume stores first entries in a first-mode storage bucket and a second entries in a second-mode storage bucket. The first-mode storage bucket has first bucket metadata, and the second-mode storage bucket has second bucket metadata. A computer-implemented method includes comparing a utilized capacity of the network storage volume to a target capacity information of the network storage volume to obtain a comparison result. Based on the comparison result, at least one bucket is selected to be purged from the buckets of the network storage volume based at least in part on bucket metadata of the buckets. The method further includes causing a purge of the at least one selected bucket from the network storage volume.

Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

A technique is described herein for automatically logging journeys taken by a user, and then automatically classifying the purposes of the journeys. In one implementation, the technique obtains journey data from one or more movement-sensing devices as a user travels from a starting location to an ending location in a vehicle. The technique generates a set of features based on the journey data, and then uses a machine-trainable model (such as a neural network) to make its classification based on the features. The machine-trainable model accepts at least one feature that is based on statistical information regarding at least one aspect of prior journeys that the user has taken. Overall, the technique provides a resource-efficient solution that rapidly provides personalized results to individual respective users. In some implementations, the technique performs its personalization without sharing journey data with a remote server.

A method for generating a report is provided. The method may include acquiring a key word related to an industry field, and acquiring one or more condition values related to the report to be generated. The method may also include determining a report template having one or more data query sections and one or more conclusion sections based on the industry field and the one or more condition values, and acquiring report data based on the one or more data query sections of the report template. The method may further include determining one or more conclusions based on the report data, and generating the report based the data acquired based on the report data, the one or more conclusions, and the template.

A method for generating a report is provided. The method may include acquiring a key word related to an industry field, and acquiring one or more condition values related to the report to be generated. The method may also include determining a report template having one or more data query sections and one or more conclusion sections based on the industry field and the one or more condition values, and acquiring report data based on the one or more data query sections of the report template. The method may further include determining one or more conclusions based on the report data, and generating the report based the data acquired based on the report data, the one or more conclusions, and the template.