Microsoft Technology Licensing, LLC

Matthew W Schuerman

Joseph Edwin Johnson, Jr

Emmanouil Koukoumidis

Hailong Mu

Ying Wang

Donald Brinkman

Donald Frank Brinkman, Jr

Systems and methods are disclosed for response retrieval using communication session vectors. In one implementation, a first communication session is received. The first communication session includes a first communication. The first communication session is encoded as a first vector. A second vector is identified within a defined proximity of the first vector. The second vector represents a second communication session that includes a second communication. The second communication is provided within the first communication session in response to the first communication.

Response retrieval using communication session vectors

Examples of the present disclosure describe systems and methods relating to conversational system user behavior identification. A user of the conversational system may be evaluated based on one or more factors. The one or more factors may be compared to an aggregated measure for a larger group of conversational system users, such that 'anomalous' behavior (e.g., behavior that deviates from a normal behavior) may be identified. When a user is identified as exhibiting anomalous behavior, the conversational system may adapt its interactions with the user in order to encourage, discourage, or further observe the identified behavior. As a result, the conversational system may be able to verify a user's anomalous behavior, discourage the anomalous behavior, or take other action while interacting with the user.

Conversational system user behavior identification

Methods, systems, and computer programs are presented for providing a user interface (UI) for monitoring and debugging an Artificial Intelligence (AI) chatting hot. One method includes operations for receiving a selection on the UI to replay an electronic conversation between a first and a second party, selecting conversation data associated with the electronic conversation from a data log having conversation data from several electronic conversations, and analyzing the conversation data to identify conversation parameters. The conversation parameters include text in each entry of the electronic conversation, timing of the entries, and debugging parameters for each entry. The method further includes an operation for causing presentation of the electronic conversation on the UI, which includes presenting the text of each entry, the audio corresponding to speech associated with each entry timed according to the timing of the entry, and the debugging parameters embedded within the presented text.

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Kirkland, WA, United States of America

Matthew W Schuerman