Abstract: Disclosed herein is a method of facilitating provisioning contexts for business situations. Accordingly, the method may include receiving at least one business dataset associated with a business organization in real time from a device, analyzing the at least one business dataset, extracting a plurality of business events from the at least one business dataset based on the analyzing of the at least one business dataset, analyzing the plurality of business events, determining a plurality of business insights from the plurality of business events using the event assessment algorithm, analyzing the plurality of business insights, determining at least one connection between at least two of the plurality of business insights, constructing a semantic graph for providing a context for each of at least one business situation associated with the business organization, and storing the semantic graph.

Abstract: Systems and methods are described for automatically identifying cause-effect relationships using hybrid systems. A server may retrieve selected expert rule templates that have input parameters that match the parameters of event data objects derived from a stream of input data. Cause-effect relationships may be determined between parameters of the data set when the selected expert rule templates are satisfied. The rule-based aspect is augmented using statistical correlation to identify a correlated pair of different parameters based on pairwise comparison of all parameters of a data set and a coincidence probability of the different parameters. Using the identified correlated pair, the server may create a new expert rule template in an expert rule database. Subsequent data in the stream of input data may trigger generating an alert when one of the expert rule templates is contradicted by the incoming data, thereby ensuring that the expert rule templates are up-to-date and accurate.

Abstract: Systems and methods are described for automatically generating natural language queries and chatbot-assisted responses to the queries. A server may generate observed fact data structures from numeric-type data columns of a data set received from a client device over a network connection. From the generated observed facts, a subset of priority observed facts may be identified based on a plurality of priority factors associated with each observed fact. To generate actionable text recommendations, the server may combine one of the priority observed facts with a natural language template retrieved from a template database. The resulting populated natural language template may then be augmented with a selected call to action query selected based on a received user explanation type. The chatbot service may then cause a text recommendation to be transmitted to a user device that is responsive to the format and values contained within the augmented natural language template.

Abstract: A cellular-based electronic house arrest monitoring (EHAM) system (10) electronically monitors parolees, or other personnel, required to remain at a house arrest location (12) or to report in at the house arrest location during certain hours. Monitoring occurs automatically under control of a host computer (50) at a central monitoring location remote from the house arrest location, regardless of whether conventional telephone service is available at the house arrest location. Tamper detect circuitry detects any attempt to tamper with the components of the system. The EHAM system includes an electronic tag (14) worn by the person being monitored that periodically transmits a unique identifying (ID) signal (16). The ID signal is transmitted at low power, and is receivable only over a limited range, e.g., 150 feet. A field monitoring device (FMD) (20) placed within the house arrest location receives the ID signal only if the tag is within range of the receiver, i.e.

Abstract: An electronic house arrest monitoring (EHAM) system allows a monitoring officer, charged with the responsibility of making periodic and/or random physical checks with individuals whose presence at specific locations is being electronically monitored through the EHAM system, to immediately and silently report to a central monitoring location that backup help or assistance is needed at the monitoring location. The EHAM system includes an in-house monitoring unit (IMU) installed at or positioned near specific monitoring locations, that electronically monitors the specific monitoring location for the presence of a unique identifying signal, periodically transmitted from an electronic tag worn by a monitored individual. Telecommunicative contact is periodically and/or randomly established between the central monitoring location and the IMU. The monitoring office carries a small pocket transmitter.

Abstract: A transponder device receives a carrier signal from an interrogator unit. This carrier signal, of frequeny F, is rectified by a rectifying circuit in order to generate operating power. Logic/timing circuits derive a clock signal and second carrier signal of frequency F/n from the received carrier signal. This clock signal reads a unique identifying data word from a programmable read only memory (PROM). The data word is encoded and mixed with the carrier signal in a balanced modulator circuit. The output of the balanced modulator is transmitted to the interrogator unit where it is decoded and used as an identifying signal. The identifying signal identifies the particular transponder device from which it originated. The rectifier and balanced modulator circuits are realized from the same diode elements. All electrical circuits of the transponder device are realized on the same monolithic semiconductor chip.