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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.
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公开(公告)号：US11604990B2
公开(公告)日：2023-03-14
申请号：US16902587
申请日：2020-06-16
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Wenjia Ma , Jaewon Yang , Jacob Bollinger , Qi He , Lin Zhu , How Jing
Abstract: In an example embodiment, a framework to infer a user's value for a particular attribute based upon a multi-task machine learning process with uncertainty weighting that incorporates signals from multiple contexts is provided. In an example embodiment, the framework aims to measure a level of a user attribute under a certain context. Rather than attempting to devise a universal, one-size-fits-all value for the attribute, the framework acknowledges that the user's value for that attribute can vary depending on context and factors in the context under which the user's attribute levels are measured. Multiple contexts are defined depending on different situations where users and entities such as companies and organizations need to evaluate user attribute levels. Signals for attribute levels are then collected for each context. Machine learning models are utilized to estimate attribute values for different contexts. Multi-task deep learning is used to level attributes from different contexts.
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Patent Agency Ranking
公开(公告)号：US11775778B2
公开(公告)日：2023-10-03
申请号：US17090776
申请日：2020-11-05
Applicant: Microsoft Technology Licensing, LLC
Inventor： Zhuliu Li , Xiao Yan , Yiming Wang , Jaewon Yang
IPC: G06F40/00 , G06F40/58 , G06N3/08 , G06N3/04 , G06F40/295
CPC classification number: G06F40/58 , G06F40/295 , G06N3/04 , G06N3/08
Abstract: Embodiments of the disclosed technologies incorporate taxonomy information into a cross-lingual entity graph and input the taxonomy-informed cross-lingual entity graph into a graph neural network. The graph neural network computes semantic alignment scores for node pairs. The semantic alignment scores are used to determine whether a node pair represents a valid machine translation.
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公开(公告)号：US11436542B2
公开(公告)日：2022-09-06
申请号：US16456838
申请日：2019-06-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Xiao Yan , Mikhail Obukhov , Jaewon Yang , Qi He
Abstract: Techniques for selecting candidates using a personalized model are disclosed herein. In some embodiments, a computer system, for each candidate of a plurality of candidates, generating a corresponding confidence score for a combination of the candidate, a particular viewer, and a particular attribute based on a scoring model, with the corresponding confidence score being configured to indicate a likelihood that the particular viewer will select the corresponding candidate as a preference with respect to the particular attribute. The computer system then selects a subset of the plurality of candidates based on the corresponding confidence scores of the candidates in the subset, and causes the subset of candidates to be displayed on a computing device of the viewer along with a prompting for the viewer to select one of the selected subset of candidates as the preference with respect to the particular attribute.
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公开(公告)号：US20220207099A1
公开(公告)日：2022-06-30
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 , G06F16/9536 , G06F16/36 , G06N20/20
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.
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公开(公告)号：US20210027233A1
公开(公告)日：2021-01-28
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
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.
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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.
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