Abstract: One example method includes collecting real data, creating synthetic data by modeling the real data, augmenting the real data and the synthetic data, applying a loss function to minimize an error between the real data and the synthetic data, adding noise to the synthetic data to create finalized synthetic data, and generating a forecast based on the finalized synthetic data. The forecast may be used as a basis to guide the performance of a resource allocation process.

Abstract: A method for automatically responding to a user input includes receiving the user input. The method also includes identifying a current domain associated with the user input. Further, the method includes determining, using a previously trained learning model, a first belief state, where the first belief state is based on the current domain. In addition, the method includes determining, using a reinforcement learning model, a second belief state, where the second belief state is based on the current domain and a reward information. Moreover, the method includes determining an action based on the user input and one selected from the group consisting of the first belief state and the second belief state. Also, the method includes generating a response based on the action and presenting the response to a user.

Abstract: A method for automatically responding to a user input includes receiving the user input. The method also includes identifying a current domain associated with the user input. Further, the method includes determining, using a previously trained learning model, a first belief state, where the first belief state is based on the current domain. In addition, the method includes determining, using a reinforcement learning model, a second belief state, where the second belief state is based on the current domain and a reward information. Moreover, the method includes determining an action based on the user input and one selected from the group consisting of the first belief state and the second belief state. Also, the method includes generating a response based on the action and presenting the response to a user.

Abstract: Methods, apparatus, and processor-readable storage media for automated request processing using an ensemble machine learning framework are provided herein. An example computer-implemented method includes aggregating interaction data associated with a request; computing a weighted score for the request, wherein the weighted score comprises a first component that is based at least in part on a comparison of the aggregated interaction data to a set of keywords and a second component corresponding to a sentiment predicted by a first machine learning model for at least a portion of the aggregated interaction data; using a second machine learning model to determine whether the request is anomalous based at least in part on the weighted score; and in response to determining that the request is anomalous, initiating one or more automated actions for the request.

Abstract: Systems and methods are provided for directing offers to users. One exemplary method includes receiving location records for first and second users from communication devices associated with the respective first and second users, where the location records for the first and second users each include location data and a time stamp of the location data. The method also includes determining, by an offer engine, an overlap between the first user and the second user in at least one region based on at least the time stamp included in the location records, and recognizing, by the offer engine, a link between the first user and the second user. The method further includes identifying an offer based on transaction data of the first user and providing the identified offer to the second user based on the recognized link between the first user and the second user.

Abstract: A method for valuation of a vehicle based on telematics includes: storing a plurality of vehicle profiles, each related to a vehicle including a vehicle value and associated telematics data points; receiving a plurality of telematics data points associated with a target vehicle; identifying a subset of vehicle profiles where there is a correspondence between the associated telematics data points and the received plurality of telematics data points; estimating a valuation of the target vehicle based on, for each vehicle profile in the subset, a combination of the vehicle value included in the respective vehicle profile and a level of similarity to the received plurality of telematics data points and the associated telematics data points; and transmitting the estimated valuation to an external computing device.

Abstract: A method for guaranteeing a blockchain transaction includes: storing account profiles, each related to a transaction account including a current balance and identification data; receiving a blockchain transaction request including a sending identifier, receiving identifier, and blockchain currency amount; transferring an alternative currency amount based on the blockchain currency amount from a first account profile including the sending identifier to a second account profile including the receiving and sending identifiers; restricting access to the current balance in the second account profile from the first account; receiving a confirmation of a blockchain transaction within a predetermined period of time; and transferring, from the second account profile, the alternative currency amount to the first account profile if the confirmation is received within the predetermined period of time, or to a third account profile including the receiving identifier if the confirmation is not received within the predeterm

Abstract: Disclosed herein are techniques for identifying customers and their purchasing segments in advance so that advertising impressions can be correlated to the consumers that are being reached by an advertising campaign. A predetermined consumer panel may be characterized by purchasing segment and any other suitable demographics, and tracking techniques such as cookies tied to specific web properties may be used to track online activity by members of the consumer panel. In this manner, each advertising impression for one of the panel members from one of the web properties can be related to a particular purchasing segment, and effectiveness of the campaign can be evaluated based on the types of consumers that are being reached with certain advertisements.