International Business Machines Corporation

Kyndryl, Inc.

Nicolò Sgobba

Erik Rueger

Nadiya Kochura

Amit Anil Nanavati

Tiberiu (Jim) Suto

George Andrei Saon

Guillermo Rodriguez De Vera Beltri

Antonello Izzi

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.

Runtime environment determination for software containers

The present disclosure relates to a method of coordinating personal assistants. The method comprises: sending a request to a set of personal assistants based on a received query. Assistant responses may be received in response to the request from respective personal assistants of the set of personal assistants. Each assistant response of the assistant responses comprises a confidence score of the respective assistant response. The confidence scores may be adjusted to a common scale. The adjusted confidence scores may be weighted using domain-specific weights of the personal assistants in accordance with the domain of the query. One or more assistant responses of the received assistant responses may be selected based on the weighted scores and the selected assistant responses may be provided to the user.

Virtual assistants harmonization

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.

Vocal recognition using generally available speech-to-text systems and user-defined vocal training

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Fasano, Italy

Nicolò Sgobba