Abstract: A method for generating insights into operational data using a language model includes receiving a user query; receiving summarized operational data, wherein summarized operational data is generated by: receiving operational data; generating an operational data graph, wherein the operational data graph comprises a plurality of nodes and edges; generating a plurality of vectors describing relationships between the plurality of nodes and edges; and applying a data model to the plurality of vectors to generate a natural language description of the plurality of vectors; generating, based on the user query and the summarized operational data, a prompt for querying a first large language model; transmitting the prompt to the first large language model; receiving a natural language response to the prompt; and generating, based on the natural language response and one or more properties of the operational data graph, one or more visualizations corresponding to the natural language response.

Abstract: Disclosed herein are embodiments of systems, methods, and products that generate semantic resolutions of wireless signals. An analytic server may train a plurality of inductive classifiers generate a respective set of deduction rules. For example, the analytic server may train a first inductive classifier to mine a first set of deduction rules matching service set identifiers (SSIDs) with business entities. As another example, the analytic server may train a second inductive classifier to mine a second set of deduction rules matching proximal groupings of wireless signals (also referred to as hyperclusters) with business entities. Upon receiving an unresolved wireless signal, the analytic server may apply at least one of the first and second set of deduction rules to assign a semantic meaning to the wireless signal.

Abstract: Disclosed herein are embodiments of systems, methods, and products that generate semantic resolutions of wireless signals. An analytic server may train a plurality of inductive classifiers generate a respective set of deduction rules. For example, the analytic server may train a first inductive classifier to mine a first set of deduction rules matching service set identifiers (SSIDs) with business entities. As another example, the analytic server may train a second inductive classifier to mine a second set of deduction rules matching proximal groupings of wireless signals (also referred to as hyperclusters) with business entities. Upon receiving an unresolved wireless signal, the analytic server may apply at least one of the first and second set of deduction rules to assign a semantic meaning to the wireless signal.

Abstract: Systems and methods for creating pattern awareness and proximal deduction of wireless devices is disclosed. The methods allow wireless awareness among a plurality of wireless signals and the ability of assigning meaning to a group of wireless signals associated with an observing WED, or wireless signal detector and take action based on the meaning.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which tracks and locates electronic devices (e.g., wireless devices and devices enabled with wireless transmitters). The analytic server monitors wireless signals detected by different observer devices at different time points. The analytic server analyzes the wireless signals to build signal graph and generate hyperclusters. When the analytic server receives a request of finding a lost/missing device, the analytic server receives observations from observer devices that detect the wireless signals from the missing device, determine signal context (e.g., hypercluster) and geolocation of the missing device based on observations from observer devices, performs resolution to narrow down the geolocation. The analytic server also combats different forms of active attacks. The analytic server generates a graphical user interface comprising the location information of the missing device.