Abstract: Techniques for intelligently managing task automation across multiple tasks and multiple automated assistants are provided. In one aspect, an intelligent multitasking system includes: a context manager configured to use human-centric input data to determine a context of a user; an intent mapper configured to map human communication to automated task intents; and an interruption manager configured to schedule automated tasks for performance by automated assistants based on the context of the user and the automated task intents. The context manager can be hosted on a cloud having MQTT clients (e.g., IoT sensors) and an MQTT broker. The intent mapper can include a human-machine software communication interface that identifies verbal and/or non-verbal communications. A method for intelligent multitasking is also provided.

Abstract: A multi-layer language translator operating in conjunction with a syntax-based model, coupled with machine learning and artificial intelligence, performs language translations from a source language text to text expressed in a target language. A relevancy-based “chunking” module breaks a source text into smaller units and applies a part-of-speech tag to some or all of the units. A hierarchy-based structuring module determines grammatical structure of the source text based, at least in part, on the applied part-of-speech tags. The hierarchy-based structuring module recursively combines grammatically linked units into one or more phrases, and applies to the phrases higher-level tags. A syntax-based translating module translates the units and/or phrases into the target language, and based on syntax differences between the source and target languages, reconfigures the translated text, as needed, such that the translated text is expressed in the target language using target language syntax rules and conventions.

Abstract: A multi-layer language translator operating in conjunction with a syntax-based model, coupled with machine learning and artificial intelligence, performs language translations from a source language text to text expressed in a target language. A relevancy-based “chunking” module breaks a source text into smaller units and applies a part-of-speech tag to some or all of the units. A hierarchy-based structuring module determines grammatical structure of the source text based, at least in part, on the applied part-of-speech tags. The hierarchy-based structuring module recursively combines grammatically linked units into one or more phrases, and applies to the phrases higher-level tags. A syntax-based translating module translates the units and/or phrases into the target language, and based on syntax differences between the source and target languages, reconfigures the translated text, as needed, such that the translated text is expressed in the target language using target language syntax rules and conventions.

Abstract: A processor may identify that a user has been added to a group of two or more users. One or more communications between the group may be hosted on one or more communication platforms. The processor may analyze each of the one or more communications. The processor may identify, from the analyzing, content of the one or more communications. The processor may generate a report. The report may indicate the content of the one or more communications. The processor may display the report to the user.

Abstract: A processor may identify that a user has been added to a group of two or more users. One or more communications between the group may be hosted on one or more communication platforms. The processor may analyze each of the one or more communications. The processor may identify, from the analyzing, content of the one or more communications. The processor may generate a report. The report may indicate the content of the one or more communications. The processor may display the report to the user.