Abstract: A system and method for automatically parsing an unstructured conversation to determine target entities for an engagement activity are provided. The method includes (i) obtaining the unstructured conversation associated with an asset using a dialogue manager, (ii) parsing the unstructured conversation using a natural language processing (NLP) model to obtain a target information associated with the asset, (iii) extracting attributes of one or more target entities from the target information using a Natural Language Understanding (NLU) model of domain of the unstructured conversation, (iv) generating, using the attributes of the one or more target entities, a definition of the cohort and at least one criteria for cohort curation for the asset by converting the unstructured conversation to a structured target information, and (v) determining a size of the one or more target entities based on the definition and the criteria for cohort curation for the asset for an engagement activity.

Abstract: A system for real time cohort creation of entities based on entity attributes derived from partially observable location data is provided. The system (i) obtains, in real time, one or more data streams from one or more independently controlled entity sources that include a unique entity identifier, entity attributes, time-stamp data, location indexed data (ii) de-duplicates the one or more data streams associated with an entity by analyzing the entity attributes associated with the unique entity identifier, (iii) classifies a unified entity event from the one or more data streams along with dynamic entity attributes retrieved from a memory store, and storing the dynamic attributes in an entity attribute document of the entity (iv) reverse searches, to match the entity attribute document with at least one query (v) generates entity cohorts based on the matched entity attribute document of the entities with at least one cohort labels and communicates a target media content to the entity cohorts over a network.

Abstract: A processor-implemented method for linking identifiers to generate a unique entity identifier for deduplicating high-speed data streams in real time, the method comprising (i) obtaining one or more data streams with an identifier from independently controlled entities, wherein the one or more data streams comprises timestamp data and location indexed data that partially characterizes an activity of an entity, (ii) determining home location or internet protocol address of the entity by analyzing data obtained from the one or more data streams, (iii) clustering entity devices based on an association between an internet protocol address, a real-time event, a period of time or a location, (iv) disambiguating the clusters of entity devices into sub-clusters that resolve to an entity by analyzing data streams until a candidate pair of identifiers is obtained, (v) generating score for the candidate pair using a machine learning classifier to discern the candidate pair of identifiers into to same or different entity,

Abstract: Disclosed is a system for automatically estimating spatio-temporal entity counts in real time and for a future time window using machine learning from partially observable location data. The system includes a data aggregator, a hyper-cube computational data structure, a geo coder, a geolocation mapper, a key value data structure updater, a hyper cube estimator, a census-based extrapolator, and an entity estimator. The entity estimator (i) determines an entity count for each or combinations of the one or more spatio temporal dimensions in real time by combining lower bound number and upper bound number of the entity count from the hyper cube estimator and the census based extrapolator, and (ii) estimates, using a machine learning based time series model, spatio temporal entity count for a future time window in response to a query criterion.