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    Skill validation
    2.
    发明授权
    Skill validation 有权

    公开(公告)号：US11610161B2

    公开(公告)日：2023-03-21

    申请号：US16521141

    申请日：2019-07-24

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Xiao Yan ,  Jaewon Yang ,  Mikhail Obukhov ,  Lin Zhu ,  Joey Bai ,  Shiqi Wu ,  Qi He ,  Farzard Eskafi

    IPC: G06Q30/00 ,  G06Q10/0631 ,  G06F17/18 ,  G06N20/00 ,  G06Q10/1053

    Abstract: Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.

    ANALYTICAL PROCESSING SYSTEM SUPPORTING NATURAL LANGUAGE ANALYTIC QUESTIONS
    4.
    发明申请
    ANALYTICAL PROCESSING SYSTEM SUPPORTING NATURAL LANGUAGE ANALYTIC QUESTIONS 审中-公开

    公开(公告)号：US20200210524A1

    公开(公告)日：2020-07-02

    申请号：US16235916

    申请日：2018-12-28

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Jaewon Yang ,  Maneesh Varshney ,  Mikhail Obukhov ,  Sung Yoon

    IPC: G06F17/27 ,  G06F16/28 ,  G06F16/33 ,  G06F16/332 ,  G06N20/20

    Abstract: Online analytical processing system supporting natural language analytic questions. In one embodiments, for example, a computer-implemented method includes: receiving a natural language question; determining an intent of the natural language question; based on the intent of the natural language question, predicting a metric query language statement based on the natural language question; translating the metric query language statement to a structured query language statement; causing an execution of the structured query language statement against multidimensional database data; and providing an answer to the natural language question based on a result of the execution of the structured query language statement against the multidimensional database data.

    Techniques for suggesting skills
    7.
    发明授权
    Techniques for suggesting skills 有权

    公开(公告)号：US11461421B2

    公开(公告)日：2022-10-04

    申请号：US17136864

    申请日：2020-12-29

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Yiming Wang ,  Xiao Yan ,  Lin Zhu ,  Jaewon Yang ,  Yanen Li ,  Jacob Bollinger

    IPC: G06F16/9535 ,  G06N3/04 ,  G06N20/20 ,  G06F16/36

    Abstract: Techniques for ranking skills using an ensemble machine learning approach are described. The outputs of two heterogenous, machine-learned models are combined to rank a set of skills that may be possessed by an end-user of an online service. Some subset of the highest-ranking skills is then presented to the end-user with a recommendation that the skills be added to the end-user's profile. The ensemble learning technique involves a concept referred to as “boosting”, in which a weaker performing model is enhanced (e.g., “boosted”) by a stronger performing model, when ranking the set of skills. Accordingly, by using a combination of models, better results are achieved than might be with either one of the individual models alone. Furthermore, the approach is scalable in ways that cannot be achieved with heuristic-based approaches.

    MINING TRAINING DATA FOR TRAINING DEPENDENCY MODEL
    8.
    发明申请
    MINING TRAINING DATA FOR TRAINING DEPENDENCY MODEL 有权

    公开(公告)号：US20230086724A1

    公开(公告)日：2023-03-23

    申请号：US17412753

    申请日：2021-08-26

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Liwei Wu ,  Wenjia Ma ,  Jaewon Yang ,  Yanen Li

    IPC: G06Q10/10 ,  G06Q10/06 ,  G06N20/20 ,  G06N5/00

    Abstract: Techniques for mining training data for use in training a dependency model are disclosed herein. In some embodiments, a computer-implemented method comprises: obtaining training data comprising a plurality of reference skill pairs, each reference skill pair comprising a corresponding first reference skill and a corresponding second reference skill, the plurality of reference skill pairs being included in the training data based on a co-occurrence of the corresponding first and second reference skills for each reference skill pair in the plurality of reference skill pairs, the co-occurrence comprising the corresponding first and second reference skills co-occurring for a same entity; and training a dependency model with a machine learning algorithm using the training data, the dependency model comprising a logistic regression model or a data gradient boosted decision tree (GBDT) model. The dependency model may then be used to identify corresponding dependency relations for a plurality of target skill pairs.

    MACHINE LEARNING TECHNIQUES FOR ANALYZING TEXTUAL CONTENT
    10.
    发明申请
    MACHINE LEARNING TECHNIQUES FOR ANALYZING TEXTUAL CONTENT 有权

    公开(公告)号：US20210182496A1

    公开(公告)日：2021-06-17

    申请号：US16716402

    申请日：2019-12-16

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Baoxu Shi ,  Angie Zhu ,  Feng Guo ,  Jaewon Yang ,  Fei Chen ,  Qi He

    IPC: G06F40/30 ,  G06F16/93 ,  G06N20/00 ,  G06F40/284 ,  G06F40/289

    Abstract: Techniques are provided for using machine learning techniques to analyze textual content. In one technique, a potential item is identified within a document. An analysis of the potential item is performed at multiple levels of granularity that includes two or more of a sentence level, a segment level, or a document level. The analysis produces multiple outputs, one for each level of granularity in the multiple levels of granularity. The outputs are input into a machine-learned model to generate a score for the potential item. Based on the score, the potential item is presented on a computing device. In response to user selection of the potential item, an association between the potential item and the document is created. The association may be used later to identify a set of users to which the document (or data thereof) is to be presented.

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