A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: generate features from the input signal; for each of a plurality of classifier models, each classifier model corresponding to a dialogue slot, and for one or more values corresponding to the dialogue slot, input features generated from the input signal, the classifier model outputting a probability corresponding to each of three or more relations, wherein the relations specify the relation of the value to the dialogue slot; update a belief state based on the outputs of the classifier models; determine a system dialogue act by inputting information relating to the belief state into a policy model; output speech or text information specified by the determined dialogue act at the output.

Dialogue system, a dialogue method, a method of generating data for training a dialogue system, a system for generating data for training a dialogue system and a method of training a dialogue system

The present invention extends to systems and methods for notifying entities of relevant events. An entity defines a rule formula that is triggered when one or more detected events match the rule formula including defining one or more event types and one or more locations types. A boundary associated with a selected location type is also received along with a monitoring area. The received elements are combined into the rule formula notification preferences are associated with the defined rule. One or more events are detected and then compared to the rule formula to determine if the combination of one or more event types occurred within the boundary of the one or more location types within the area.

Notifying entities of relevant events

Display screen or portions thereof with a graphical user interface

The present invention extends to methods, systems, and computer program products for detecting events from signal data. A signal is ingested. A portion of the signal is selected from within the signal. A first score is computed from the selected portion. The first score indicates a likelihood of the signal including information related to an event type. It is determined that processing of another signal is warranted based on the indicated likelihood. Resources are allocated to process the other signal. The other signal is ingested. Parameters associated with the other signal are accessed. A second score is computed from the parameters utilizing the allocated resources. A previously unidentified event of the event type is identified based on the second score and utilizing the allocated resources.

Detecting an event from signal data

A computer-implemented method for tracking information technology (IT) incidents across a network of retail stores via a graphical user interface (GUI) on a display of a user device is disclosed. The method includes, via an IT manager that is accessible from the user device, receiving, from the user device, a store identifier; determining one or more IT incidents based on the store identifier; and via an application running on the user device, displaying on the display of the user device, a status of the one or more IT incidents based on the store identifier.

Retail store information technology incident tracking mobile application

A virtual agent can implement a "chatbot" to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events.

Machine natural language processing for summarization and sentiment analysis

Embodiments described herein relation to IT incident management that can involve predictive analytics, prescriptive analytics, and descriptive analytics. An IT incident management platform can enable IT incident solution prediction using prescriptive models and natural language processing. An IT incident management platform can enable IT incident ticket volume prediction using predictive models and natural language processing. An IT incident management platform can generate visual elements for display at an interactive interface that represents data centre topology network graphs using descriptive models.

Cai Yixian

Papers

Machine natural language processing for summarization and sentiment analysis

Technology incident management platform