Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: Zero-touch monitoring devices and systems are disclosed that are configured with hardware to perform a process-mining on an event-to-entry process associated with an organization to collect data information associated with the process, determine an input-related zero-touch quotient for the event-to-entry process based on the data information, determine a rule-related zero-touch quotient for the event-to-entry process based on the data information, determine a zero-touch potential predictive accounting factor (ZTP PAF) value for the event-to-entry process, and generate a zero-touch processing quotient for the event-to-entry process based on the input-related zero-touch quotient, the rule-related zero-touch quotient, and the ZTP PAF value. The ZTP PAF value quantifies an incremental zero-touch potential for the event-to-entry process and corresponding attributes.

Abstract: Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Abstract: Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Abstract: Systems and methods for detecting and verifying closed places (e.g., claims no longer in business) from trip data are provided. A networked system accesses trip data associated with the POI. The networked system processes the trip data to generate at least two time buckets based on timestamps from the trip data associated with the POI, and calculates trip counts associated with the POI for each of the time buckets. Using a machine learning algorithm and based on the at least two time buckets, the networked system determines that the trip counts show a decline over time that indicates that the POI is likely closed. In response to the determining, the networked system updates a database to indicate the POI is closed.

Abstract: A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

Abstract: Systems and methods for improving attribute data for a point of interest (POI) are provided. A networked system accesses trip data associated with the POI. The networked system generates, using a processor-implemented clustering algorithm, a first spatial cluster and a second spatial cluster using coordinates corresponding to the POI indicated in the trip data. A centroid for the first spatial cluster and a centroid for the second spatial cluster are identified by the networked system. The networked system determines that a difference in distance between the centroid for the first spatial cluster and the centroid for the second spatial cluster meets or transgresses a centroid distance threshold. In response to the determining, a database is updated to indicate a new attribute for the POI, the new attribute corresponds to an attribute associated with either the first spatial cluster or the second spatial cluster.

Abstract: Systems and methods for maintaining a current database of point of interest (POI) by automatically detecting a change associated with a POI based on cohort analysis are provided. A networked system accesses trip data associated with the POI. Using the analyzed trip data, the networked system generates a cohort associated with the POI. The networked system monitors for, and detects a change in, trip behavior in the cohort associated with the POI. In response to detecting the change in trip behavior, the networked system updates a database in a data store to indicate a status update.

Abstract: Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Abstract: A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

Abstract: Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Abstract: Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Abstract: Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.

Abstract: A network system receives geographic information from a device. The geographic information is representative of a candidate GPS route used by a vehicle to complete a trip. The candidate GPS route is more efficient that a map route determined based on ground truth map data. The network system identifies an ordered sequence of one or more known road segments in the ground truth map data that the transportation vehicle uses to complete a trip represented by the candidate GPS route. To determine the ordered sequence of road segments, the network system may relax constraints on attributes of the map data to identify the ordered sequence of road segments.

Abstract: A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

Abstract: Systems and methods for maintaining a current database of point of interest (POI) by automatically detecting a change associated with a POI based on cohort analysis are provided. A networked system accesses trip data associated with the POI. Using the analyzed trip data, the networked system generates a cohort associated with the POI. The networked system monitors for, and detects a change in, trip behavior in the cohort associated with the POI. In response to detecting the change in trip behavior, the networked system updates a database in a data store to indicate a status update.

Abstract: Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.

Abstract: Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Abstract: Various embodiments determine accuracy of place data by determining a context for a place record (that is included in the place data) and determining accuracy of the place record based on a set of criteria associated with the context determined for the place record. For some embodiments, the set of criteria is used in place of, or in conjunction with, another set of fixed criteria for determining accuracy of the place record. Context for the given place record may be determined based on a set of features for the given place record, and the set of features may be generated (e.g., derived or extracted) based on values of one or more attributes (e.g., record fields or fields) included in the given place record. For some embodiments, a quality of a place record is determined based on at least the determination of an accuracy of the place record.

Abstract: Various embodiments determine relevance of place data by determining whether a place record is relevant based on a set of features associated with the place record. For a given place record, a set of features may be generated based on values of one or more attributes included in the given place record. A given place record may be processed by at least one machine learning model, such as a classifier, which receives as input a set of features of the given place record and outputs a prediction score indicating the certainty or probability that the given place record is associated with, or belongs to, a particular class. The certainty/probability of association between a given place record and a particular class can assist some embodiments in determining (e.g., predicting) whether the given place record is relevant or non-relevant for an intended use, such as a software application for a ride service.