Microsoft Technology Licensing, LLC

Divya Jetley

Ye-Yi Wang

Chenlei Guo

Byungki Byun

Jianfeng Gao

Xinying Song

Brian D Remick

Yelong Shen

Edward Thiele

Mohammed Aatif Ali

Marcus Gois

Stephen Friesen

Ruhi Sarikaya

Xiaodong He

Omar Zia Khan

Jianshu Chen

Yu Gu

Xiaojiang Huang

Mariana Stepp

Hong Hong

Pavel Metrikov

Xiaocheng Deng

Yang Zou

Systems and methods are disclosed to provide optimized scheduling of calendar events based on flexibility scores of calendar events. A flexibility score may be representative of a probability or likelihood that a calendar event can or will be rescheduled in response to a conflicting calendar event. Flexibility scores of calendar events may be calculated based on one or more factors, which may be weighted, using one or more machine-learning models. Factors may include: event densities of invitees' calendars, organizational rankings of respective invitees, the remaining time before an event start time, an urgency of respective calendar events, etc. In this way, if open time slots are not available for all invitees to a proposed calendar request, an event organizer may identify time slots occupied by existing calendar events with the highest likelihood of being rescheduled in view of the proposed calendar event, thereby facilitating scheduling of the proposed calendar event.

Optimized scheduling of calendar events

Embodiments build a knowledge base that includes a list of acronyms and their expansions. The list of acronyms may be associated with a particular organization, e.g. a product team, such that the acronym may have a different meaning to a different organization. In some embodiments, acronyms and their expansions are extracted from artifacts associated with the organization, e.g. documents, emails, attachments, calendar items, etc. Multiple potential definitions identified within the artifacts may be ranked based on contextual data extracted from the artifacts, e.g. who authored the artifact, when was the artifact modified, how often did the author use the acronym, an author's rank in the organization, how long has an author been part of the organization, an author's relationship to other authors, etc. By basing the analysis on artifacts associated with the organization the resulting definitions may be more accurate than if broader resources, such as dictionary definitions, were used.

Generating an acronym index by mining a collection of document artifacts

Systems, components, devices, and methods for resolving ambiguity in a conversational understanding system are provided. A non-limiting example is a system or method for resolving ambiguity in a conversational understanding system. The method includes the steps of receiving a natural language input and identifying an agent action based on the natural language input. The method also includes the steps of determining an ambiguity value associated with the agent action and evaluating the ambiguity value against an ambiguity condition. The method includes the steps of when determined that the ambiguity value meets the ambiguity condition: selecting a prompting action based on the ambiguity associated with the identified agent action, performing the prompting action, receiving additional input in response to the prompting action, and updating the agent action to resolve the ambiguity based on the additional input. The method also includes the step of performing the agent action.

Ambiguity resolving conversational understanding system

Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.

Contextual people recommendations

Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.

Relevance group suggestions

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Bellevue, WA, United States of America

Divya Jetley