公开(公告)号：US11803703B2

公开(公告)日：2023-10-31

申请号：US17332169

申请日：2021-05-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Michael Gamon ,  Sujay Kumar Jauhar ,  Bahareh Sarrafzadeh ,  Mark James Encarnacion ,  Liye Fu

IPC: G06F17/00 ,  G06F40/194 ,  G06F40/197 ,  G06F40/106 ,  G06F40/169

CPC classification number: G06F40/194 ,  G06F40/106 ,  G06F40/169 ,  G06F40/197

Abstract: Systems, storage media and methods for providing information for user prioritization of tasks associated with collaboratively developed content are described. Some examples may include: receiving a conversation thread associated with collaboratively developed content, the conversation thread including a plurality of comments authored by multiple different authors, generating a predicted measure of completion for the received conversation thread, the predicted measure of completion being at least one of a predicted number of remaining actions until the received conversation thread is resolved or a predicted number of total actions for the conversation thread to be resolved and providing, for display at a user interface, the predicted measure of completion for the received conversation thread, the predicted measure of completion being associated with the conversation thread at the user interface.

公开(公告)号：US11080468B2

公开(公告)日：2021-08-03

申请号：US16722544

申请日：2019-12-20

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ashequl Qadir ,  Michael Gamon ,  Patrick Pantel ,  Ahmed Hassan Awadallah

IPC: G06F40/117 ,  H04L12/58 ,  G06F40/30

Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.

公开(公告)号：US12002012B2

公开(公告)日：2024-06-04

申请号：US17664041

申请日：2022-05-18

Applicant: Microsoft Technology Licensing, LLC

Inventor： Mark James Encarnación ,  Nalin Singal ,  Michael Gamon ,  Shawon Sarkar ,  Nouran Soliman

IPC: G06Q10/00 ,  G06N3/08 ,  G06Q10/0633 ,  G06Q10/0635 ,  G06Q10/10

CPC classification number: G06Q10/103 ,  G06N3/08 ,  G06Q10/0633 ,  G06Q10/0635

Abstract: A computing system for identifying tasks at risk in a collaborative project includes one or more processors configured to execute, during an inference-time phase, a collaborative project management program and a machine learning model. The collaborative project management program is configured to receive telemetry data associated with a task, process the telemetry data based at least in part on one or more task attributes, and output at least one feature associated with the task. The machine learning model is configured to receive, as inference-time input, the at least one feature associated with the task, and, responsive to receiving the at least one feature, output a risk prediction for the task. The system is configured to output an alert when the task is predicted to be at risk of not being completed by a predetermined due date.

公开(公告)号：US11556776B2

公开(公告)日：2023-01-17

申请号：US16164366

申请日：2018-10-18

Applicant: Microsoft Technology Licensing, LLC

Inventor： Sujay Kumar Jauhar ,  Michael Gamon ,  Patrick Pantel

IPC: G06N3/08 ,  G06N3/04 ,  G06F40/40 ,  G06F40/30

Abstract: A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.

公开(公告)号：US11328259B2

公开(公告)日：2022-05-10

申请号：US17167277

申请日：2021-02-04

Applicant: Microsoft Technology Licensing, LLC

Inventor： Michael Gamon ,  Saliha Azzam ,  Yizheng Cai ,  Nicholas Caldwell ,  Ye-Yi Wang

IPC: G06Q10/10

Abstract: Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.

公开(公告)号：US11003858B2

公开(公告)日：2021-05-11

申请号：US15993045

申请日：2018-05-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Chu-Cheng Lin ,  Michael Gamon ,  Dongyeop Kang ,  Patrick Pantel ,  Madian Khabsa ,  Ahmed Hassan Awadallah

IPC: G10L15/16 ,  G06F40/30 ,  G06N3/08 ,  G06N3/04 ,  G06Q10/10 ,  G06F40/169

Abstract: A method includes receiving an email addressed to a recipient user, processing the received email using a reparametrized recurrent neural network model to identify an action based on the received email, and wherein the reparametrized recurrent neural network model has been trained on an email dataset annotated with recipient corresponding actions and reparametrized on unannotated conversation data having structures similar to email data.

公开(公告)号：US20200050500A1

公开(公告)日：2020-02-13

申请号：US16654340

申请日：2019-10-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ahmed Hassan Awadallah ,  Mark Encarnacion ,  Michael Gamon ,  Madian Khabsa ,  Patrick Pantel ,  Yu Su

IPC: G06F9/54 ,  G06N7/00 ,  G06F17/27

Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.

公开(公告)号：US20160337295A1

公开(公告)日：2016-11-17

申请号：US14714137

申请日：2015-05-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Paul Nathan Bennett ,  Nirupama Chandrasekaran ,  Michael Gamon ,  Nikrouz Ghotbi ,  Eric Joel Horvitz ,  Richard L. Hughes ,  Prabhdeep Singh ,  Ryen William White

IPC: H04L12/58

CPC classification number: H04L51/18 ,  G06Q10/06 ,  G06Q10/10 ,  H04L51/04 ,  H04L51/046 ,  H04L51/10

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Abstract translation: 分析电子通信内容的系统可以自动从电子通信中提取请求或承诺。 在一个示例性过程中，处理组件可以分析内容以确定内容的一个或多个含义; 查询与电子通信相关的一个或多个数据源的内容; 并且至少部分地基于（i）内容的一个或多个含义，以及（ii）一个或多个数据源的内容，自动地从内容中识别和提取请求或承诺。 可以从初始识别和提取，包括对请求或承诺的描述的确认和细化，以及帮助一个或多个发件人，收件人或其他人跟踪和解决请求或承诺的行为，包括创建 附加消息，提醒，约会或待办事项列表。

公开(公告)号：US12216991B2

公开(公告)日：2025-02-04

申请号：US17574878

申请日：2022-01-13

Applicant: Microsoft Technology Licensing, LLC

Inventor： Sujay Kumar Jauhar ,  Nirupama Chandrasekaran ,  Elnaz Nouri ,  Mark J. Encarnacion ,  Michael Gamon

IPC: G06F40/186 ,  G06F3/0484 ,  G06F9/451 ,  G06F16/2457

Abstract: Aspects of the present disclosure relate to task template generation and social task discovery. In examples, a task template catalog comprises task templates, which may be automatically generated and/or user-submitted, among other examples. Task templates can be reviewed, shared, and curated within the task template catalog. A user may browse the task catalog or search the task catalog for task templates. Once the user selects a task template, a task is generated based on the task template and added to the user's task list. In some examples, aspects of a task template may be customized. For example, a task may comprise parametric or conditional subtasks, thereby enabling a user to further tailor the task template to his or her needs. Thus, the task catalog provides a starting point from which the user can author a task in a task management application.

公开(公告)号：US10963318B2

公开(公告)日：2021-03-30

申请号：US16654340

申请日：2019-10-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ahmed Hassan Awadallah ,  Mark Encarnacion ,  Michael Gamon ,  Madian Khabsa ,  Patrick Pantel ,  Yu Su

IPC: G06F9/54 ,  G06N7/00 ,  G06F40/30

Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.