Abstract: A method provides a set of predicted responses to a user. The method includes receiving a message from a user, the message having natural language information. The method includes processing, using cognitive switching logic (CSL), the natural language information and information from previous messages from the user. The method includes identifying, using CSL, a context of the natural language information based on the information from the previous messages. The method includes identifying, using CSL, at least one knowledge domain which contains a response to the message, based on the identified context and on user persona information. The method includes retrieving a response from each identified knowledge domain. The method further includes, in response to retrieving more than one response, transmitting feedback to CSL to refine identifying the at least one knowledge domain until only one response is retrieved. The method further includes presenting the one response to the user.

Abstract: Static parameters of a software container are identified that relate to metadata of the software container itself. The software container is assigned to a selected runtime environment based on the static parameters using a first machine learning model. Runtime parameters for the software container are identified by analyzing the software container at runtime. The runtime parameters relate to operations that the software container requires during runtime. Using a second machine learning model, it is determined whether the selected runtime environment matches the runtime parameters. Where the runtime environment matches, the software container continues to run in this environment. Where the runtime environment does not match, the software container is run in a different runtime environment that matches both the static and runtime parameters.

Abstract: A user input is received. The user input is classified using a neural network. A set of contextual information for the user input is determined, based on the classification. The user input is modified, based on the classification and the set of contextual information. The modified user input is passed to a virtual assistant.

Abstract: Techniques for augmenting the output of generally available speech-to-text systems using local profiles are presented. An example method includes receiving an audio recording of a natural language command. The received audio recording of the natural language command is transmitted to a speech-to-text system, and a text string generated from the audio recording is received from the speech-to-text system. The text string is corrected based on a local profile mapping incorrectly transcribed words from the speech-to-text system to corrected words. A function in a software application is invoked based on the corrected text string.

Abstract: A method provides a set of predicted responses to a user. The method includes receiving a message from a user, the message having natural language information. The method includes processing, using cognitive switching logic (CSL), the natural language information and information from previous messages from the user. The method includes identifying, using CSL, a context of the natural language information based on the information from the previous messages. The method includes identifying, using CSL, at least one knowledge domain which contains a response to the message, based on the identified context and on user persona information. The method includes retrieving a response from each identified knowledge domain. The method further includes, in response to retrieving more than one response, transmitting feedback to CSL to refine identifying the at least one knowledge domain until only one response is retrieved. The method further includes presenting the one response to the user.

Abstract: A method that includes joining, by a first node, a network connecting a set of nodes, where each node has an agent and is associated with a sensor, and where the sensor generates sensor data, and where each agent sends and receives broadcast messages, and generating, by the sensor, sensor data associated with a second node. The method also includes analyzing, by the agent, the sensor data, where the analyzing causes a detection of a first fault condition of the second node, and where the first fault condition is an indication of a problem with the second node, generating, by the agent, a broadcast message, where the broadcast message includes the first fault condition, and sending, by the first node, the broadcast message to at least one member of the set of nodes via the network.

Abstract: Embodiments for managing natural language user input are provided. Natural language input is received from a user utilizing a computing node. The natural language input is analyzed. The analyzing of the natural language input includes attempting to identify at least one of an intent associated with the natural language input, an entity associated with the natural language input, and context data associated with the natural language input. The natural language input is evaluated against a pre-trained model based on the analyzing of the natural language input. A quality score for the natural language input is calculated based on the evaluating of the natural language input. An action is caused to be performed utilizing the computing node based on the calculated quality score.

Abstract: Techniques for augmenting the output of generally available speech-to-text systems using local profiles are presented. An example method includes receiving an audio recording of a natural language command. The received audio recording of the natural language command is transmitted to a speech-to-text system, and a text string generated from the audio recording is received from the speech-to-text system. The text string is corrected based on a local profile mapping incorrectly transcribed words from the speech-to-text system to corrected words. A function in a software application is invoked based on the corrected text string.

Abstract: User bioelectrical authentication is provided. A bioelectrical configuration signature of a user in physical contact with a hardware token is received, via a token reader, during a low range radio frequency user authentication process to permit a secure transaction by the user. The bioelectrical configuration signature of the user is utilized as part of the low range radio frequency user authentication process to authenticate the user to perform the secure transaction.

Abstract: Embodiments provide thought classifier devices that convert analog electroencephalogram signals obtained from mental activity of a person to a digital signal bitstream data; identify a portion of the bitstream as a thought chunk representing discrete thought activity in response to matching, via a first artificial neural network comparison, digital signal bitstream thought chunk portion metadata to metadata labeled in association with a thought within a thoughts data set, the first artificial neural network trained on the thoughts data set; identify a user category in response to matching, via a different, second artificial neural network comparison, metadata of the thought chunk portion to labeled metadata within the thoughts data set, the second artificial neural network trained on the thoughts data set; and identify a specific thought of the thoughts data that has metadata that has corresponding metadata.