Abstract: A computer-implemented method is disclosed. The method includes: clustering a set of queries into first clusters; identifying, using a first large language model (LLM), queries in the first clusters that are semantically dissimilar from other queries in their cluster; clustering the queries identified as semantically dissimilar into one or more further clusters; and processing an incoming query based on matching said query to a particular cluster from the first clusters or the further clusters.

Abstract: A computer-implemented method is disclosed. The method includes: clustering a set of queries into first clusters; identifying, using a first large language model (LLM), queries in the first clusters that are semantically dissimilar from other queries in their cluster; clustering the queries identified as semantically dissimilar into one or more further clusters; matching a further query to a particular cluster from the first clusters or the further clusters; and obtaining a response to the further query based on providing, to a second LLM, data associated with the particular cluster.

Abstract: A computer-implemented method is disclosed. The method includes: clustering a set of queries into first clusters; identifying, using a first large language model (LLM), queries in the first clusters that are semantically dissimilar from other queries in their cluster; clustering the queries identified as semantically dissimilar into one or more further clusters; matching a further query to a particular cluster from the first clusters or the further clusters; and obtaining a response to the further query based on providing, to a second LLM, data associated with the particular cluster.

Abstract: A computer-implemented method is disclosed. The method includes: obtaining at least one query; clustering a set comprising the at least one query into first clusters; for each first cluster, identifying, by a large language model (LLM), queries in the cluster that are semantically dissimilar; clustering the queries identified as semantically dissimilar into one or more second clusters; receiving an incoming query; matching the incoming query to a particular cluster from the first or second clusters; obtaining one or more generated response messages based on providing, to the LLM, data associated with the particular cluster for the incoming query.

Abstract: A computer-implemented method is disclosed. The method includes: obtaining at least one query; clustering a set comprising the at least one query into first clusters; for each first cluster, identifying, by a large language model (LLM), queries in the cluster that are semantically dissimilar; clustering the queries identified as semantically dissimilar into one or more second clusters; receiving an incoming query; matching the incoming query to a particular cluster from the first or second clusters; obtaining one or more generated response messages based on providing, to the LLM, data associated with the particular cluster for the incoming query.

Abstract: Machine learning (ML) is provided at edge computing devices based on distributed feedback received from the edge computing devices. A trained instance of an ML model is received at the edge computing devices via communications networks from an ML model manager. Feedback data including labeled observations is generated by the execution of the trained instance of the ML model at the edge computing devices on unlabeled observations captured by the edge computing devices. The feedback data is transmitted from the edge computing devices to a machine learning model manager. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is received at the edge computing devices from the machine learning model manager. The re-trained instance of the machine learning model is executed at the edge computing devices.

Abstract: Machine learning (ML) is provided at edge computing devices based on distributed feedback received from the edge computing devices. A trained instance of an ML model is received at the edge computing devices via communications networks from an ML model manager. Feedback data including labeled observations is generated by the execution of the trained instance of the ML model at the edge computing devices on unlabeled observations captured by the edge computing devices. The feedback data is transmitted from the edge computing devices to a machine learning model manager. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is received at the edge computing devices from the machine learning model manager. The re-trained instance of the machine learning model is executed at the edge computing devices.

Abstract: A system and method for predicting the location of a user of social media utilizing information related to the interaction of the user with other users of the social media is described.

Abstract: A system and method for predicting the location of a user of social media utilizing information related to the interaction of the user with other users of the social media is described.

Abstract: A system and method for determining sentiment comprising receiving textual data, identifying a context for the textual data, selecting and/or modifying a natural language processor based on the context, analyzing the textual data with the natural language processor for a sentiment determination, and storing the sentiment determination on a non-transitory computer readable medium.

Abstract: A system and method for receiving social media data from multiple social media websites, categorizing the social media data, scoring the social media data, creating clusters of social media data and scoring and ranking the clusters of social media data.

Abstract: A system and method for determining intent of posters to a social media site for a predetermined topic through the analysis of the poster's posts. The system and method also allows for extrapolation and predictive analysis of the intent data determinations to provide insight into the views and intent of the general populace regarding a selected topic.