Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products that provide for an improved and more efficient system for transmitting content to client devices. The embodiments disclose an apparatus that clusters user behavior based on platform session records. The platform session records comprise stored data associated with a plurality of user inputs received by the system for the duration of a platform session. The apparatus and methods transform the platform session records into data comprising a form that is usable for identifying clusters of platform sessions. As a result of the clustering methodologies, the apparatus is configured to deliver content specifically optimized for users of client devices. The present disclosure thus provides for an improved networked system that reduces the amount of data transferred via the network and allows for a more efficient system that has reduced infrastructure requirements and improved performance.

Abstract: An example operation includes one or more of accessing, by a transport, data requested by an occupant of the transport, determining, by the transport, one or more risk levels associated with content of the data and a driving environment of the transport, responsive to the determining, bifurcating, by the transport, the data, distributing, by the transport, a portion of the bifurcated data with a lower risk level during a safe driving environment, and distributing, by the transport, a remaining portion of the bifurcated data with a higher risk level, after the occupant has departed the transport.

Abstract: Systems, methods, and apparatuses for storing verified identification information in a distributed database and for verifying entities to requestors are provided herein. Identity information relating to an entity is verified by a generally recognized identity provider (GRIP). Once verified, the GRIP populates a distributed database with an entry corresponding to the verified identity information. The distributed database is publically accessible such that identity requestors can see the entries in the distributed database. In some arrangements, the distributed database is write-protected such that only an authorized GRIP can modify the distributed database. An entity can provide information to a requestor to verify an identity attribute of the entity to the requestor. The requestor uses information provided by the entity to retrieve verified entity identification information from the distributed database and to confirm that the identity attribute was verified by an authorized GRIP.

Abstract: Systems and methods include extraction of a plurality of clauses from each of a plurality of electronic documents, determination, for each of the plurality of clauses and using a machine-learned algorithm, an associated clause type, identification of one or more data privacy protection entities present within each of one or more of the plurality of clauses, determination, for each of the one or more of the plurality of clauses, of a weighted frequency for each of the one or more data privacy protection entities present within the clause based on a type of the data privacy protection entity, determination of a weighted frequency associated with each of the plurality of electronic documents based on the determined weighted frequency for each of the one or more data privacy protection entities present within clauses of the plurality of electronic documents, and storage of an identifier of each of the plurality of electronic documents in association with a respective determined weighted frequency.

Abstract: A retail ticking system including a processing unit, a web services server for receiving sent messages from a terminal client device and delivering presentation data to the terminal, a credential server for receiving and verifying a processed credential access request, a profile management server for storing and retrieving user information, and a message broker adapted to receive and manage event messages.

Abstract: The present disclosure is directed to a system and method to allow sellers of products to establish individual products. A user is provided a code such that the code may be passed onto followers and allowed to advertise the product to a second purchaser. The second purchaser is allowed to purchase the product and provide advertisements to other purchasers. Commissions paid to respective downline purchasers for a single product may be limited by a preset number of defined levels after a product is purchased and potential product conflicts are detected.

Abstract: The systems described herein include a system and method for redeeming multiple redeemable codes. For example, a mobile device can be configured to enable redemption of multiple redeemable codes in a single transaction. In some implementations, the mobile device can store the plurality of redeemable codes for obtaining discounts on the items obtained from the merchant store. Particular implementations provide at least the following advantages: A mobile device can store and manage multiple redeemable codes for a user. The mobile device can automatically select and provide the redeemable codes to a point of sale terminal when the user is making a purchase. The codes can be automatically selected and applied to the purchased goods or services to generate a discount for the user. Redeemable codes can be automatically removed from the user's device after they have been used. Thus, the user is relieved of the burden of managing redeemable codes.

Abstract: The systems and methods that generate a one-touch frame are provided. A line of code including a tag is inserted into a webpage. The tag includes a link to a server from which parameters for the frame are obtained. The browser uses the parameters to generate the frame. In addition, the server or a local memory of a computing device on which the frame is generated also store credentials associated with a user account. These credentials are also obtained and incorporated into the frame. The frame may then be activated to perform one-touch functions using the credentials.

Abstract: Systems and methods adapted for training a machine learning model to predict data labels are described. The approach includes receiving a first data set comprising first data objects and associated first data labels, and processing, with a user representation model, respective first data objects and associated data labels associated with a unique user representation by fusing the respective first data object and the associated first data labels. First data object representations of the respective first data objects are generated, and the first data object representations and the user representation model outputs are fused to create a user conditional object representation. The machine learning model updates corresponding parameters based on an error value based on a maximum similarity of the projections of the respective user conditional object representation and first data labels in a joint embedding space.

Abstract: Tiered advertisement bidding is disclosed. One or more quality metrics associated with a user profile are determined. An advertisement bid is selected from a plurality of tiered bids based at least in part on the determined quality metrics. Determining the quality metrics can include determining a conversion assessment. Determining the quality metric can also include determining whether a need associated with a user profile has been met for a category. In some cases, persona detection is performed with respect to the user profile.

Abstract: A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Abstract: A method for dynamic intelligent testing of a target, to be tested according to projects, includes calling up a data distribution model of a project in response to a target being tested by the project, and obtaining a test range corresponding to the project based on the data distribution model. The method further includes obtaining a test value when the target is at a minimum power consumption value by testing the target based on the test range, and updating the data distribution model and the test range of the project based on the test value.

Abstract: Methods and apparatus to deliver targeted advertising are disclosed. An example apparatus includes processor circuitry to: obtain user behavior information corresponding to interaction with a first media object from a user, the first media object including a first customizable media object element and a second customizable media object element independent of the first customizable media object element; analyze a performance of the first media object based on the user behavior information; and update at least one of a user profile of the user or a group profile corresponding to the user using a machine learning model to adjust ratings of the first customizable media object element and the second customizable media object element based on a comparison of the performance of the first media object to a second custom object including at least one of the first customizable media object element or the second customizable media object element.

Abstract: There is provided a method, comprising: extracting user feature profiles for users of a social network, each feature profile being structured and including user features extracted from unstructured user generated text, indications of participation in groups, and structured user profiles, training a clustering-component of a model to cluster the feature profiles, training a matching-component of the model to compute a distance score indicative of statistical similarity between a feature profile of a target user and features profiles of other users of a same cluster, using a training dataset of pairs of feature profiles extracted from common clusters, each pair assigned a distance score label, providing the model for: identifying a certain cluster of a certain user, and computing distance scores between the feature profile of the certain user and other feature profiles of other users of the certain cluster for selecting one user for establishment of a communication session.

Abstract: Systems and methods for providing an online platform that enables an organization to provide information to interested individuals are described. The organization requests individuals to contact elected officials to express support, rejections or comments for specific issues. The online platform determines an advocate's elected official(s) and facilitates a communication connection between the advocate and an elected official(s). Geocoding is performed using the individual's street address and zip code to obtain geographical coordinates, and the coordinates are geomatched to district matching databases to determine the individual's elected officials. The individual selects a preferred method of connecting, and the platform enables and facilitates the connection.

Abstract: Systems and methods are for routing electronic payment transactions to PIN-less networks using payment pseudo-networks and electronic transaction simulation.

Abstract: In some examples, sensor data comprising a first set and second set of sensor data can be received. The first set of sensor data can be provided to a rule-based classifier to identify a first normalized data tag. The second set of sensor data can be provided to a trained classifier to identify a second normalized data tag. The trained classifier can include a machine-learning model that has been trained based on tag sensor training data for classifying the second set of sensor data into a respective class associated with the second normalized data tag. The first and second sets of sensor data can be updated with respective first and second normalized data tags to normalize the first and second sets of sensor data to provide a standardized data label for each of the first and second sets of sensor data.

Abstract: A method for authentication of coupons using a blockchain includes: storing transaction data for a payment transaction, wherein the transaction data includes at least a transaction amount; receiving an identification value; receiving a block included in a blockchain, wherein the block includes at least a block header and a plurality of transaction values, each transaction value including at least a coupon identifier and coupon data; executing a query on the received block to identify a specific transaction value of the plurality of transaction values where the included coupon identifier corresponds to the received identification value; and executing a query on the memory to update at least the transaction amount included in the stored transaction data based on the coupon data included in the identified specific transaction value.

Abstract: A method and system for providing data imbalance detection and validation for a trained a ML model includes receiving a request to perform data imbalance detection on the trained ML model, identifying a feature of a dataset associated with the trained model for which data imbalance detection is to be performed, receiving access to the dataset, receiving access to the trained ML model, examining at least one of the dataset or outcome data generated by the trained ML model to determine a distribution of the feature or a distribution of the outcome data, and determining if the trained ML model exhibits data imbalance based at least in part on the distribution of the feature or the distribution of the outcome data.

Abstract: Systems and methods are provided to monitor performance of a machine learning model, the method may include steps of: receiving or storing one or more model data sets representative of the machine learning model, wherein the machine learning model has being trained with a first set of training data; analyzing the first set of training data based on one or more performance parameters for the machine learning model, to generate one or more performance data sets; and process the one or more performance data sets to determine one or more values representing a performance of the machine learning model.