A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

The present disclosure comprise: acquiring source data for generating a profile of a user and time data related with generation of the source data; clustering the source data based on the time data related with the generation of the source data as a category; generating a profile of the user by using the cluster generated through the clustering; and generating region of interest data including information of a geographic region that may be determined to be of interest to the user based on the profile of the user, and wherein the ROI data may include location information of the user, and the profile of the user associated with the time data may be labeled. The intelligent device of the present disclosure may be associated with an artificial intelligence module, drone (unmanned aerial vehicle, UAV), robot, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G services, and the like.

The present disclosure comprise: acquiring source data for generating a profile of a user and time data related with generation of the source data; clustering the source data based on the time data related with the generation of the source data as a category; generating a profile of the user by using the cluster generated through the clustering; and generating region of interest data including information of a geographic region that may be determined to be of interest to the user based on the profile of the user, and wherein the ROI data may include location information of the user, and the profile of the user associated with the time data may be labeled. The intelligent device of the present disclosure may be associated with an artificial intelligence module, drone (unmanned aerial vehicle, UAV), robot, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G services, and the like.

An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

A system and method are provided for inferring location attributes from data entries. The method comprises for data entries in a structured data set format, a computer system selecting a sample of rows. The computer system then identifies columns containing geospatial and temporal information based on the column headings. The computer system next identifies location information within the structured data set. The computer system determines implied location information based on the identified location information. The computer system derives location values based on the identified and implied location information using consolidation rules, resulting in a final set of location attributes for the data entries. The computer system then associates the final set of location attributes with the data entries.

A system and method are provided for inferring location attributes from data entries. The method comprises for data entries in a structured data set format, a computer system selecting a sample of rows. The computer system then identifies columns containing geospatial and temporal information based on the column headings. The computer system next identifies location information within the structured data set. The computer system determines implied location information based on the identified location information. The computer system derives location values based on the identified and implied location information using consolidation rules, resulting in a final set of location attributes for the data entries. The computer system then associates the final set of location attributes with the data entries.

A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.

Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for providing users with electronic retrieval capabilities that are activated upon the users' determined locations respective to real-world locations associated with a message providing entity. The disclosed technology combines the previously separate systems of mail extraction, geo-fencing and content delivery (e.g., notification) into a single system that efficiently manages a user's inbox in order to provide the user with content the user has expressly indicated is of interest to that user. The disclosed systems and methods effectively realize a location-aware mail experience that provides functionality for delivering location (and timing) specific content to a user when the user is actually capable of acting on/interacting with the content in real-time.

A server system is provided that includes a platform server system configured to store platform map data that is geospatially referenced and includes building outline data for one or more buildings, and provide the platform map data to client computer devices. The server system further includes a tenant bounded server system including one or more processors configured to store building floorplan data for a tenant entity, and provide building floorplan data to client computer devices that are authenticated for the tenant entity. The one or more processors of the tenant bounded server system are further configured to receive a set of building floorplan data for a target building included in the platform map data, determine geospatial reference data for the set of building floorplan data by aligning the set of building floorplan data with building outline data of the target building.