Methods, apparatus, systems and articles of manufacture to monitor media presentations are disclosed. An example tangible computer readable storage medium includes instructions that, when executed, cause a machine to at least instrument media with monitoring instructions; provide the instrumented media to a media device via a hypertext transport secure protocol, the monitoring instructions to cause the media device to request a panelist identifier associated with the media device; detect a media event based on an action of the media device; determine media-identifying information based on the media event; and generate a record including the media-identifying information and the panelist identifier.

Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

An attribution system aggregates and merges online data and offline chronologically. The attribution system examines merged data for unique visitor (UV) sessions initiated at an online medium (e.g., a website) within an attribution window for a spot that aired on an offline medium (e.g., a television network) and, for each conversion event that occurred in a UV session, assigns a session timestamp to it so that the conversion event is correlated to the spot. The attribution system then determines an overall conversion rate of UVs to the online medium in the attribution window and the attribution by the spot that aired on the offline medium to the overall conversion rate of UVs to the online medium in the attribution window. Results of the offline attribution to the online conversions can be visualized and presented on a client device communicatively connected to the attribution system.

Provided is a system for controlling progress of audio-video content based on sensor data of multiple users, composite neuro-physiological state (CNS) and/or content engagement power (CEP). Sensor data is received from sensors positioned on an electronic device of a first user to sense neuro-physiological responses of the first user and second users that are in field-of-view (FOV) of the sensors. Based on the sensor data and at least one of a CNS value for social interaction application and a CEP value for immersive content, recommendations of action items for first user are predicted. Content of a feedback loop, created based on sensor data, CNS value, CEP value, and predicted recommendations, is rendered on output unit of electronic device during play of the at least one of social interaction application and immersive content experience. Progress of social interaction and immersive content experience is controlled by first user based on predicted recommendations.

A method comprising using at least one hardware processor to: generate a que of users willing to respond to queries; receive a query and a required number of responses from an application; and assign from the que a plurality of users to respond to the query, wherein the number of the plurality of users is based on the required number of responses and wherein the users are assigned based on a score that is indicative of a quality determination of prior responses for each of the plurality of users, wherein the score is based at least on positive feedback including investing, wherein investing comprises: displaying a list of all responses of a particular user, receiving a selection of a response from the list, receiving a payment representative of the investment, initiating an investigation; and providing a results of the investigation.

An information processing device includes a controller, a ride detection device configured to detect a ride of a user, a storage device configured to store action data of the user, an output device configured to output question data for requesting an answer from the user, and an input device configured to receive an input from the user. The controller outputs, from the output device, output data including at least a question regarding an action of the user taken before riding in a vehicle in accordance with positional information of the vehicle when detecting the ride of the user based on a signal acquired from the ride detection device, acquires an answer to the question from the user as input data via the input device, and associates the input data with the positional information of the vehicle or a POI to store the associated data in a storage device.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

Systems and methods for determining how topics evolve in a messaging system extract at least one N-gram from data content (e.g., caption of messages) in the messaging system and detect anomalous behavior in N-gram frequencies over time. The anomalous behavior is used to select candidate N-grams for a determination of whether a topic of a candidate N-gram is evolving or fading. The candidate N-grams are clustered into cluster groups that are used to train at least one time series forecasting model to predict N-gram frequencies in a future time window. A time series of the N-gram frequency is divided into old and recent partitions and pattern recognition is applied to the predicted N-gram frequencies to identify an evolving or fading topic when the difference between a frequency of each anomaly and an average rolling median for each partition is greater for the most recent partition.

An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.