Abstract: A point saving system for pension payment according to one embodiment of the present disclosure comprises: a customer terminal in which an application for customers is installed configured to make a payment by using an account that is pre-registered in the application for customers; an affiliated store terminal in which an application for affiliated stores is installed, configured to receive customer information on customers from the customer terminal, and transmit an amount of payment, together with the received customer information, to a main server; and the main server of a company configured to receive the customer information and the amount of payment from the affiliated store terminal, save points, for each customer, that correspond to a certain percentage of the received amount of payment on the basis of the received customer information, set levels of the customers on the basis of the saved points for each, determine customers to receive a pension on the basis of the set levels and the customer inform

Abstract: Methods, systems, and storage media for assigning multiple identifiers to user accounts are disclosed. Exemplary implementations may: receive an indication of an event; associate the event with a user; associate the user with at least a first account identifier of a plurality of accounts of the user; associate the user with at least a second account identifier of the plurality of accounts of the user, the second account identifier different than the first account identifier; add at least one of the first account identifier or the second account identifier to a custom audience relating to the event; target the custom audience through directed advertising; and deliver advertisements to the custom audience.

Abstract: A method, system, and computer program product for simulating potential consequences and possible solutions due to a release of stored energy using augmented reality. The method may include aggregating IoT feeds from devices in a surrounding area. The method may also include calculating amounts of stored energy in the surrounding area based on the IoT feeds. The method may also include predicting contextual situations that could result due to a release of the stored energy in the surrounding area. The method may also include determining one or more consequences for each of the contextual situations. The method may also include calculating a degree of severity of the one or more consequences for each contextual situation. The method may also include determining one or more proposed solutions based on the degree of severity. The method may also include transmitting a recommendation of at least one proposed solution for implementation.

Abstract: Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

Abstract: A method including receiving an unknown vector including a data structure populated with unknown features describing a user. The method also includes executing a primary machine learning model (MLM) trained using a prediction data set to predict a score representing a prediction regarding the user. The prediction data set includes the unknown vector stripped of a biased data set including markers set that directly indicate that the user belongs to a cohort against which bias is to be avoided. The method also includes executing a supervisory MLM trained using the prediction data set to predict whether the user belongs to the cohort. The method also includes performing, using an industry tool, a computer-implemented action using the score after executing the primary MLM and the supervisory MLM.

Abstract: Improved systems and methods for enhancing the performance of network based computerized content rendering and hosting and providing of devices, systems and/or platforms by modifying the capabilities and providing non-native functionality to such devices, systems and/or platforms through a novel and improved data processing and networking framework.

Abstract: A system and method provides for the use of a proprietary platform to predict impressions and to predict and design ad campaigns. Specifically, a proprietary programmatic advertisement platform correlates television programming asset sources with actual viewing behavior of broadcasts associated with the television programming assets and ad content associated with the ad campaign. The correlation produces a predicted performance value used to generate an ad campaign. For instance, the invention enables television programming asset sources and viewership information providers to communicate with the programmatic advertisement platform in a way that adds value to or otherwise facilitates the valuation of the television programming asset sources in context of the particular ad.

Abstract: One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

Abstract: Systems, methods, and apparatus, including computer program products, for configuring online properties, such as content pages of a website, through an online user interface. A system generates the online user interface and receives, over a network and through the online user interface presented by a client device, a request to insert a restricted third party file into a user's online property. The online user interface is updated to inform the user that inclusion of the third party file in the user's online property is restricted and to provide the user with options for satisfying requirements for including the third party file in the user's online property. In response to detecting a user selection of at least one of the options, the restricted third party file is received from a content repository, and is inserted into the user's online property.

Abstract: There are provided systems and methods for a communication device interface for merchant check-in and shopping notifications. A user may enter shopping preferences to a module of a communication device in order to receive notifications and alerts when items matching the shopping preferences are available with one or more merchants. A service provider may receive the shopping preferences with user attributes, such as a user location and/or account balance. The attributes may be determined using applications and/or modules of the communication device or through a check-in by the user at a location. The service provider may determine shopping notifications having purchase offers for the items from available merchants. The purchase offers may include offers to sell the items and discounts for the items. The shopping notifications may be displayed to the user using a user device interface, such as cells within an application or mobile device home screen.

Abstract: One or more computing devices, systems, and/or methods for content recommendations using historical future data are provided. A model serving delay time is computed as an average of training delays of events. A historical data time interval is determined based upon the model serving delay time. A model is trained for predicting user content preferences using historic user distribution data and historic content distribution data associated with the historic data time interval. The model is utilized to generate and provide content recommendations to users.

Abstract: There is provided a system and process of delivering online media content files to websites which are not capable of being blocked. The system and process provides a publisher with a sub-domain name that is pointed to an IP provided by an ad server and ask publisher to update his DNS records. Concurrently, the system delivers the user a script on the newly suggested sub domain that publisher inserts in HEAD tag of web pages where it is determined that the unblockable ads are to be displayed.

Abstract: An actionable alert platform is provided. The platform may utilize artificial intelligence (“AI”) to formulate alert triggers that inform a user regarding unusual activity associated with a target transaction. The platform allows a user to enter customized criteria for an alert trigger and actionable responses for remediating the detected alert trigger. In response to detecting an alert trigger, the platform may formulate and transmit push notifications to a mobile device of user. The user may implement the actionable response directly from within the push notification. The platform may also provide push notifications to the user that advise on a status of the target transaction after applying the actionable response. An AI engine may generate additional alert triggers based on the criteria entered by the user. The AI engine may generate the actionable responses for any alert trigger.

Abstract: Certain aspects of the present disclosure provide techniques for generalized metric for machine learning model evaluation for unsupervised classification including: for each unsupervised machine learning model of one or more unsupervised machine learning models: generating a first set of synthetic inputs for the model of the one or more unsupervised machine learning models; providing the first set of synthetic inputs to the model trained to output a prediction for each input of the first set of synthetic inputs, wherein the prediction indicates whether the input is of a first class; identifying, based on an output of the model, a second set of synthetic inputs predicted to be of the first class; determining, based on a set of expected normal inputs for the model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model; and providing the accuracy score for display.

Abstract: Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a selection of a first objective and a second objective, the first objective and second objective comprising goals associated with distribution of a plurality of data elements; receiving an indication that the first objective has a higher priority than the second objective; receiving a first goal metric associated with the first objective and a second goal metric associated with the second objective; determining a first forecasted metric based on the first goal metric associated with the first objective; determining a second forecasted metric based on the second goal metric associated with the second objective; and allocating resources for the distribution of a plurality of data elements based on the first goal metric, the second goal metric, the first forecasted metric, the second forecasted metric, and the indication that the first objective has a higher priority than the second objective.

Abstract: Approaches for using self-balancing shaped rewards include randomly selecting a start and goal state, traversing first and second trajectories for moving from the start state toward the goal state where a first terminal state of the first trajectory is closer to the goal state than a second terminal state of the second trajectory, updating rewards for the first and trajectories using a self-balancing reward function based the terminal states of the other trajectory, determining a gradient for the goal-oriented task module, and updating one or more parameters of the goal-oriented task module based on the gradient. The second trajectory contributes to the determination of the gradient and the first trajectory contributes to the determination of the gradient when the first terminal state is within a first threshold distance of the second terminal state or the first terminal state is within a second threshold distance of the goal state.

Abstract: A system for generating a vibrant compatibility plan using artificial intelligence. The system includes at least a server wherein the at least a server is designed and configured to receive at least a composition datum from a user client device wherein the at least a composition datum includes at least an element of user body data and at least an element of desired dietary state data. At least a server is configured to select at least a correlated dataset. At least a server is configured to create at least an unsupervised machine-learning model including at least a hierarchical clustering model to output at least a compatible food element. At least a server is configured to generate at least a vibrant compatibility plan wherein the at least a vibrant compatibility plan further comprises a plurality of compatible food elements each containing at least a food element compatibility index value score as a function of the at least a hierarchical clustering model.

Abstract: An artificial intelligence apparatus for generating training data includes a memory configured to store a target artificial intelligence model, and a processor configured to receive sensor data, determine whether the received sensor data is irrelevant to a learning of the target artificial intelligence model, determine whether the received sensor data is useful for the learning if the received sensor data is determined to be relevant to the learning, extract a label from the received sensor data by using a label extractor if the received sensor data is determined to be useful for the learning, determine a confidence level of the extracted label, and generate training data including the received sensor data and the extracted label if the determined confidence level exceeds a first reference value.

Abstract: A computer-implemented method for providing interpretable predictions from a machine learning model includes receiving a data structure that represents a hierarchical structure of a set of features (X) used by one or more predictive models to generate a set of predictions (Y). An interpretability model is built corresponding to the predictive models, by assigning an interpretability to each prediction Yi based on the hierarchical structure. Assigning the interpretability includes decomposing X into a plurality of partitions Xj using the hierarchical structure, wherein X=U1NXj, N being the number of partitions. Further, each partition is decomposed into a plurality of sub-partitions using the hierarchical structure until atomic sub-partitions are obtained. A score is computed for each partition as a function of the predicted scores of the sub-partitions, wherein the predicted scores represent interactions between the sub-partitions. Further, an interpretation of a prediction is outputted.

Abstract: Systems and methods for statistical dynamic geofencing are discussed. For example, a method for statistical dynamic geofencing can include operations such as receiving a location-based campaign request, analyzing demographic data, determining a geofence parameter, generating a geofence, and distributing location-aware advertisements. The location-based campaign request can include a list of points of interest (POIs) and a target demographic parameter. The demographic data can include data from geographic areas surrounding at least a portion of the POIs as well as data related to the target demographic parameter. The geofence can be generated based at least in part on the geofence parameter, where the geofence parameter directly affects either the size or shape of the geofence. The location-aware advertisements are distributed to a plurality of mobile devices determined to be within the geofence.