Microsoft Technology Licensing, LLC

Other

In the Chat Communications Inc.

Omar Zia Khan

Ruhi Sarikaya

Paul Anthony Crook

Vipul Agarwal

Marius Alexandru Marin

Jean-Philippe Robichaud

Dmitriy Meyerzon

Vishal R Joshi

Imed Zitouni

Alexandre Rochette

John Michael Winn

Hisami Suzuki

Crystal L Hoyer

John Guiver

Muhammad Bilal Aslam

Bradley Bartz

Matteo Venanzi

Hui Li

Ravi Kumar Bikkula

Alexander Armin Spengler

Ivan Korostelev

Pavel Myshkov

Yordan Kirilov Zaykov

Elena Pochernina

Sayed Ibrahim Hashimi

Younus Aftab

Nikhil Holenarsipur Ramesh

Jonathan Kevin Wall

Bill Staples

Martin Kukla

Ye-Yi Wang

Vipindeep Vangala

Clea H Allington

Nitin Agrawal

Shobana Balakrishnan

Pankaj Khanzode

Vladimir V Gvozdev

Wai Man Yuen

Junyi Chai

Vasiliy Radostev

David L A Anson

Divya Jetley

Eric H Wong

Karthik Tangirala

Ryan A Nowak

Noura Farra

Khushboo Aggarwal

Hussein Ahmed Hassan Elmessilhy

Sravya Narala

Jonathan C Lee

Murad Pandit

Salim Ejaz

John Thomas Huehn

Danko Panic

Jennifer X Zhang

Paul Crook

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by a user-based mining system, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Enterprise knowledge graphs using user-based mining

Systems and methods are disclosed for task identification and tracking using shared conversational context. In one implementation, a first communication from a first user is received within a communication session. The first communication is processed to identify a first content element within the first communication. A second communication is received within the communication session. The second communication is processed to identify a second content element within the second communication. The first content element is associated with the second content element. Based on an association between the first content element and the second content element, a task is identified. An action is initiated with respect to the task.

Task identification from electronic user communications using a shared conversational context

Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. The plurality of entity names are linked based on entity metadata by traversing various relationships between people, files, sites, groups, associated with entities. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Enterprise knowledge graphs using multiple toolkits

Conversational understanding systems allow users to conversationally interface with a computing device. In examples, a query may be received that includes a request for execution of a task. A data exchange task definition may be accessed. The data exchange task definition assists a conversational understanding system in managing task state tracking for information needed for task execution. Using the data exchange task definition, a per-turn policy for interacting with the user computing device is generated based on the state of a dialogue with a computing device and an evaluation of a process flow chart provided by a task owner resource. The task owner resource may be independent from the conversational understanding system. A response to the query may be generated and output based on the per-turn policy. In examples, the per-turn policy is used to generate one or more responses during a dialogue with a user via a computing device.

Policy authoring for task state tracking during dialogue

Mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. A plurality of entity records are generated within a knowledge graph for mined entity names from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity names. Pattern recognition is applied to an active document using an enterprise named entity recognition (ENER) system to identify potential entity names within the document that match a respective one of a plurality of entity records in the knowledge graph. One or more matching entity names are annotated within the document with information from the knowledge graph for the respective ones of the plurality of entity records. The annotated information is displayed with the active document.

Annotations for enterprise knowledge graphs using multiple toolkits

A computer-implemented technique is described herein for creating a relational data structure by extracting user data items from a collection of one or more applications sources. These data items evince interests exhibited by the users, and may include messages, documents, tasks, meetings, etc. The technique also collects knowledge data items from one or more knowledge sources. In one implementation, these data items may include terms used to describe skills possessed by the users. The technique constructs the data structure by providing objects associated with respective data items, and links between respective pairs of objects. In its real-time phase of operation, the technique allows a user to interrogate the relational data structure, e.g., to identify skills possessed by a particular user, to find users associated with a specified skill, etc.

Querying a relational knowledgebase that provides data extracted from plural sources

Aspects herein provide third party application authors with a user interface authoring platform that automates and simplifies a task definition process while also providing the ability to leverage pre-existing language understanding models and canonicalization and resolution modules that are provided by the operating system on which the CU system resides or as provided by other third parties. In particular, the present disclosure provides a method and system for authoring a task using a user interface authoring platform.

Natural language task completion platform authoring for third party experiences

Systems and methods are disclosed for personalized communications using semantic memory. In one implementation, a first communication is received from a user and processed to identify a first content element within the communication. The first content element is associated with a second content element within a content repository. A second communication that includes the first content element is received from the user. Based on an association between the first content element and the second content element within the content repository, a third communication that includes the second content element is generated and provided to the user in response to the second communication.

Personalized communications using semantic memory

Assigning tasks and monitoring task performance based on context extracted from a shared contextual graph

Examples of the present disclosure improve decision-making for input understanding to assist in determining how to best respond to a user input. A received input is analyzed using an input recognition component, input understanding component and input context component. Potential response options are determined. If uncertainty exists with respect to responding to the received input, an uncertainty value and a cost of misclassification are generated for the potential response options to assist in making a decision as to how to best respond to the received input. The uncertainty value is determined for a potential response and parameters associated with the potential response and the cost of misclassification is a cost associated with pursuing a potential response if the potential response turns out to be incorrect. A response is selected to transmit to a user based on analyzing the generated uncertainty value and the generated cost of misclassification for the potential responses.

Managing user interaction for input understanding determinations

A development tool is described herein. The development tool automatically identifies one or more resources associated with a first version of a Web site that is being developed by a user of the tool. The development tool also automatically interacts with at least one entity within a Web hosting environment to cause a companion resource to be provisioned in the Web hosting environment for each local resource so identified. The development tool may also automatically interact with at least one entity within the Web hosting environment to properly configure a published version of the Web site to utilize at least one companion resource.

System and method for automatic provisioning of companion resources in a web hosting environment

Systems and methods for building conversational understanding systems are provided. More specifically, the systems and methods utilize prebuilt conversational items that can form a CU system upon selection by a builder without requiring any further input from the builder. Accordingly, the systems and methods for building a conversational understanding system reduce the expertise, time, and resources necessary to build a conversational understanding system for an application when compared to systems and methods that utilize conversational items that require further input from the builder.

Systems and methods for building conversational understanding systems

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

Non-limiting examples of the present disclosure describe decoupling task state tracking that is managed by a shared task completion platform from execution of tasks by a task resource owner. Task registration data is received at a shared task state platform for a task that is executable by a task owner resource. Task registration data comprises parameters to be collected for execution of the task and ancillary information, such as the name of the task and whether to confirm the values of the parameters after collection. During interaction with a user, the shared task completion platform receives an input and determines the task is associated with the received input. During the interaction, parameters of the received task registration data are utilized to collect data for execution of the task. The collected data is transmitted to the task owner resource for execution of the task.

Task state tracking in systems and services

Growing community of inventors

Bellevue, WA, United States of America

Omar Zia Khan