-
公开(公告)号：US20210326747A1
公开(公告)日：2021-10-21
申请号：US16853442
申请日：2020-04-20
Applicant: Microsoft Technology Licensing, LLC.
Inventor： Baoxu Shi , Shan Li , Jaewon Yang , Mustafa Emre Kazdagli , Feng Guo , Fei Chen , Qi He
Abstract: In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.
-
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
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.
-
公开(公告)号：US20220391690A1
公开(公告)日：2022-12-08
申请号：US17340607
申请日：2021-06-07
Applicant: Microsoft Technology Licensing, LLC
Inventor： Shuai Wang , Piede Zhong , Ji Yan , Feng Guo , Dan Shacham , Fei Chen
Abstract: Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.
-
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.
-
公开(公告)号：US20210012267A1
公开(公告)日：2021-01-14
申请号：US16505306
申请日：2019-07-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Nadia Fawaz , Nikhil N. Jannu , Feng Guo , Somya Gupta , Uma K. Sawant , Praveen Sampath , Janani Sriram , Liang Zhang
IPC: G06Q10/06 , G06N5/02 , G06F16/9535 , G06N20/00 , G06Q10/10
Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of rules for filtering job recommendations, wherein the rules are selected to maximize a reduction in negative outcomes associated with the job recommendations. Next, the system generates a label for a set of candidate-job pairs that match one or more of the rules and inputs the label with a set of candidate-job features for the set of candidate-job pairs as training data for a filtering model. The system then applies the filtering model to additional candidate-job features associated with a candidate and a set of jobs to produce a set of scores, wherein each score represents a likelihood that the candidate perceives a corresponding job as an undesirable recommendation. Finally, the system outputs a subset of the jobs as recommendations to the candidate based on the set of scores.
-
Patent Agency Ranking
公开(公告)号：US12229669B2
公开(公告)日：2025-02-18
申请号：US17340607
申请日：2021-06-07
Applicant: Microsoft Technology Licensing, LLC
Inventor： Shuai Wang , Peide Zhong , Ji Yan , Feng Guo , Dan Shacham , Fei Chen
IPC: G06N3/08 , G06F16/334 , G06F18/2113 , G06F18/214 , G06F18/2413 , G06N3/04 , G06Q10/1053
Abstract: Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.
-
公开(公告)号：US11710070B2
公开(公告)日：2023-07-25
申请号：US16853442
申请日：2020-04-20
Applicant: Microsoft Technology Licensing, LLC
Inventor： Baoxu Shi , Shan Li , Jaewon Yang , Mustafa Emre Kazdagli , Feng Guo , Fei Chen , Qi He
CPC classification number: G06N20/00 , G06N3/08 , G06N5/04 , G06Q10/1053
Abstract: In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.
-
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.
-
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.
-
10.发明授权公开(公告)号：US11663536B2
公开(公告)日：2023-05-30
申请号：US16443608
申请日：2019-06-17
Applicant: Microsoft Technology Licensing, LLC
Inventor： Baoxu Shi , Feng Guo , Jaewon Yang , Qi He
IPC: G06Q10/06 , G06Q10/10 , G06N20/00 , G06Q10/0631 , G06Q10/1053 , G06Q10/0639
CPC classification number: G06Q10/063112 , G06N20/00 , G06Q10/06393 , G06Q10/06398 , G06Q10/1053
Abstract: Techniques for scoring data items using a machine-learned model are provided. In one technique, multiple skills are identifying based on a job posting. Multiple attribute values of the job posting are identified. For each identified skill, multiple probabilities are identified, each probability corresponding to a different attribute value of the identified attribute values. The probabilities are input into a machine-learned model to generate multiple scores. Multiple skills of a candidate user are identified. An affinity score between the job posting and the candidate user is generated based the scores and the skills of the candidate user.
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.017 seconds)
