Abstract: In some implementations, a device may identify a plurality of individuals that form a target pool for transacting with a plurality of entities. The device may determine responsiveness associations between the plurality of individuals and the plurality of entities. The device may perform, based on the responsiveness associations and a composition of the target pool, multiple iterations of computations of respective quantities of individuals predicted to transact with one or more of the plurality of entities and respective communication allocations predicted to realize transactions for the respective quantities of individuals. Each iteration, of the multiple iterations of computations, may be initiated by an update to the composition of the target pool in response to an entity accepting a communication allocation. The device may cause, based on the entity accepting the communication allocation, transmission of a plurality of communications in accordance with the communication allocation.

Abstract: In some implementations, a device may identify a plurality of individuals that form a target pool for transacting with a plurality of entities. The device may determine responsiveness associations between the plurality of individuals and the plurality of entities. The device may perform, based on the responsiveness associations and a composition of the target pool, multiple iterations of computations of respective quantities of individuals predicted to transact with one or more of the plurality of entities and respective communication allocations predicted to realize transactions for the respective quantities of individuals. Each iteration, of the multiple iterations of computations, may be initiated by an update to the composition of the target pool in response to an entity accepting a communication allocation. The device may cause, based on the entity accepting the communication allocation, transmission of a plurality of communications in accordance with the communication allocation.

Abstract: In some implementations, a device may identify a plurality of individuals that form a target pool for transacting with a plurality of entities. The device may determine responsiveness associations between the plurality of individuals and the plurality of entities. The device may perform, based on the responsiveness associations and a composition of the target pool, multiple iterations of computations of respective quantities of individuals predicted to transact with one or more of the plurality of entities and respective communication allocations predicted to realize transactions for the respective quantities of individuals. Each iteration, of the multiple iterations of computations, may be initiated by an update to the composition of the target pool in response to an entity accepting a communication allocation. The device may cause, based on the entity accepting the communication allocation, transmission of a plurality of communications in accordance with the communication allocation.

Abstract: Techniques for granting, for a device, access to additional functionality requested by user inputs received by the device are described. A system may receive, from a device, a first user input requesting content of a content type requiring additional functionality access in order for the content to be sent to the device. The system may exchange data with an access control system in order to determine the device is capable of performing the additional functionality. The system may prompt the user as to whether the additional functionality access should be granted. In response to receiving a confirmatory user input, the system may cause the access control system to grant the additional functionality access for the device.

Abstract: A system and a method for detecting fraudulent transactions at a transaction site by analyzing pattern of events associated with one or more transactions are provided. The present invention provides for forming a collection of most probable fraudulent patterns and true patterns associated with one or more transactions, selecting a pattern classification technique, generating a data input from an ongoing transaction that is interpretable by the selected pattern classification technique, and effectively and efficiently categorising ongoing transaction into fraudulent and genuine transactions using selected pattern classification technique. The present invention may be utilized in a variety of applications where discrete time-ordered visual events are associated with a transaction, for example: vehicles detected in relation to a transit point, badge or card swipes from an automated door lock etc., which indicate trespassing, theft and unauthorized access to restricted areas etc.

Abstract: A system and a method for detecting anomalous activities in a distributed and decentralised network is provided. Anonymous users transacting in the network are identified and one or more transactional attributes are retrieved to define characteristics of users and associated transactional behaviour with other users. Further, user-level statistics are evaluated based on transactional attributes. Datatype representative of transactional behavior of users with other users is generated using user-level statistics of identified users. Users with similar transactional behavior are classified based on generated transactional attributes. One or more anomaly detection techniques are implemented for identifying optimum classification of users into data clusters based on the change detected in the classification of users in data clusters. Anomalous users are identified from the optimum classification for efficiently and effectively detecting anomalous activities in the network.

Abstract: A system and a method for detecting anomalous activities in a distributed and decentralised network is provided. Anonymous users transacting in the network are identified and one or more transactional attributes are retrieved to define characteristics of users and associated transactional behaviour with other users. Further, user-level statistics are evaluated based on transactional attributes. Datatype representative of transactional behavior of users with other users is generated using user-level statistics of identified users. Users with similar transactional behavior are classified based on generated transactional attributes. One or more anomaly detection techniques are implemented for identifying optimum classification of users into data clusters based on the change detected in the classification of users in data clusters. Anomalous users are identified from the optimum classification for efficiently and effectively detecting anomalous activities in the network.

Abstract: A system and a method for detecting fraudulent transactions at a transaction site by analyzing pattern of events associated with one or more transactions are provided. The present invention provides for forming a collection of most probable fraudulent patterns and true patterns associated with one or more transactions, selecting a pattern classification technique, generating a data input from an ongoing transaction that is interpretable by the selected pattern classification technique, and effectively and efficiently categorising ongoing transaction into fraudulent and genuine transactions using selected pattern classification technique. The present invention may be utilized in a variety of applications where discrete time-ordered visual events are associated with a transaction, for example: vehicles detected in relation to a transit point, badge or card swipes from an automated door lock etc., which indicate trespassing, theft and unauthorized access to restricted areas etc.