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公开(公告)号：US11481448B2
公开(公告)日：2022-10-25
申请号：US16836546
申请日：2020-03-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Peide Zhong , Feishe Chen , Weizhi Meng , Wei Kang , Feng Guo , Fei Chen , Jaewon Yang , Qi He
Abstract: During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
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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：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：US10536511B2
公开(公告)日：2020-01-14
申请号：US15379959
申请日：2016-12-15
Applicant: Microsoft Technology Licensing, LLC
Inventor： Tomer Cohen , Guy Lebanon , Qi He
Abstract: A machine may be configured to generate a digital content feed based on at least explicit feedback from a member of a Social Networking Service (SNS). For example, the machine generates explicit input data describing digital content preferences of a member of the SNS based on a communication including explicit feedback data. The communication is received from a client device associated with the member. The machine accesses feature data pertaining to one or more items of digital content determined to be relevant to the member. The feature data describes one or more characteristics associated with the one or more items. The machine generates a feed of items of digital content for the member based on the explicit input data and the feature data. The machine causes a presentation of the feed of the items of digital content in a user interface of the client device associated with the user.
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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：US11487947B2
公开(公告)日：2022-11-01
申请号：US16716402
申请日：2019-12-16
Applicant: Microsoft Technology Licensing, LLC
IPC: G06F17/00 , G06F40/30 , G06F16/93 , G06F40/289 , G06F40/284 , G06N20/00
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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公开(公告)号：US20210303638A1
公开(公告)日：2021-09-30
申请号：US16836546
申请日：2020-03-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Peide Zhong , Feishe Chen , Weizhi Meng , Wei Kang , Feng Guo , Fei Chen , Jaewon Yang , Qi He
IPC: G06F16/903 , G06K9/62 , G06F40/30 , G06N20/00
Abstract: The disclosed embodiments provide a system for processing user-generated input. During operation, the system obtains a first embedding produced by an embedding model from an input string representing an entity and a hierarchy of clusters of embeddings generated by the embedding model from a set of standardized entities. Next, the system searches the hierarchy of clusters for a subset of the embeddings that are within a threshold proximity to the first embedding in a vector space. The system then calculates embedding match scores between the input string and a first subset of the standardized entities represented by the subset of the embeddings based on distances between the subset of the embeddings and the first embedding in the vector space. Finally, the system modifies, based on the embedding match scores, content outputted in response to the input string within a user interface of an online system.
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公开(公告)号：US20210065047A1
公开(公告)日：2021-03-04
申请号：US16556097
申请日：2019-08-29
Applicant: Microsoft Technology Licensing, LLC
Inventor： Baoxu Shi , Jaewon Yang , Qi He
Abstract: Techniques for learning entity representations in a scalable manner are provided. A graph that comprises a plurality of nodes representing a set of entities is stored. A first subset of the set of entities and a second subset of the set of entities are identified. For each entity in the first subset of the set of entities, one or more machine learning techniques are used to generate a machine-learned embedding for the entity. For each entity in the second subset of the set of entities, a subset of entities in the first subset that are associated with the entity is identified. One or more embeddings are identified for the subset of entities. Based on the one or more embeddings, an inferred embedding is generated for the entity.
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公开(公告)号：US20190130281A1
公开(公告)日：2019-05-02
申请号：US15799396
申请日：2017-10-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jaewon Yang , Qi He , How Jing , Bee-Chung Chen , Liangyue Li
IPC: G06N5/02
Abstract: Techniques for predicting a next company and next title of a user are disclosed herein. In some embodiments, an encoder is used for encoding a representation of the user's profile. The encoding includes accessing discrete entities comprising context information included in the user's profile, constructing a plurality of embedding vectors from the context information, and generating a context vector from the plurality of embedding vectors. The plurality of embedding vectors including a skill embedding vector, a school embedding vector, and a location embedding vector. A decoder is for decoding a career path from the context vector. The decoding includes applying a long short-term memory (LSTM) model to the context vector to generate perform the prediction of the user's next company and next title for presentation in a user interface.
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公开(公告)号：US10255586B2
公开(公告)日：2019-04-09
申请号：US15199423
申请日：2016-06-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Dan Shacham , Uri Merhav , Peide Zhong , Qi He , Angela Jiang
IPC: G06Q10/00 , G06Q10/10 , G06Q50/00 , G06F16/9535 , G06F16/2457
Abstract: An online social networking system receives an unstructured job title record from a profile of a member or a job posting. The system extracts a raw job title from the unstructured job title record, and extracts a first seniority level from the raw job title. The first seniority level is a seniority modifier associated with the raw job title. The system determines a second seniority level. The second seniority level is a company seniority within the company associated with the unstructured job title record. The system determines a third seniority level. The third seniority level is a seniority score for the member or the job posting. The system compares the seniority score with a second seniority score, and communicates with the member, or transmits the job posting to the member, based on the comparison of the seniority score and the second seniority score.
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