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公开(公告)号:US12106146B2
公开(公告)日:2024-10-01
申请号:US17382973
申请日:2021-07-22
Applicant: Google LLC
Inventor: Kun Qiu , Seiji Charles Armstrong , Theodore R. Pindred , Rui Zhong , Maxwell Corbin , Allan Martucci , Aditya Padala , Dayu Yuan , Ngoc Thuy Le
CPC classification number: G06F9/50 , G06F9/5027 , G06N5/04 , G06N20/00
Abstract: The disclosure is directed to systems, methods, and apparatus, including non-transitory computer-readable media, for performing quota resolution on a cloud computing platform. A system can receive user account data from one or more user accounts representing a first user. The system can generate a plurality of features from the user account data characterizing interactions between the first user and the computing platform. From at least the plurality of features, the system can generate a score at least partially representing a predicted likelihood that the additional computing resources allocated to the first user account will be used in violation of one or more predetermined abusive usage parameters during a predetermined future time period.
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公开(公告)号:US20230206255A1
公开(公告)日:2023-06-29
申请号:US17646142
申请日:2021-12-27
Applicant: Google LLC
Inventor: Rui Zhong , Xu Gao , Colleen Conway Walsh , Aditya Padala , Hirak Mondal , Dayu Yuan , Ngoc Thuy Le , Zi Yang , Pradhat Kiran Bharathidhasan , Sarath Balasubramaniam Ramachandran
CPC classification number: G06Q30/0201 , G06F40/30 , G06N20/00
Abstract: A method for predicting a customer trust target metric includes receiving, from a business, a customer trust target metric definition defining a customer trust target metric customized by the business. The method also includes obtaining sentiment data representative of one or more interactions between a customer and the business. The sentiment data includes textual feedback data and non-textual metadata. The method also includes determining, using a natural language processing model, a sentiment score of the sentiment data. Further, the method includes predicting, using the sentiment score and the customer trust target metric definition, a respective customer trust target metric for a respective one of the one or more interactions between the customer and the business. The method also includes sending, to the business, the predicted respective customer trust target metric.
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公开(公告)号:US20220129318A1
公开(公告)日:2022-04-28
申请号:US17382973
申请日:2021-07-22
Applicant: Google LLC
Inventor: Kun Qiu , Seiji Charles Armstrong , Theodore R. Pindred , Rui Zhong , Maxwell Corbin , Allan Martucci , Aditya Padala , Dayu Yuan , Ngoc Thuy Le
IPC: G06F9/50
Abstract: The disclosure is directed to systems, methods, and apparatus, including non-transitory computer-readable media, for performing quota resolution on a cloud computing platform. A system can receive user account data from one or more user accounts representing a first user. The system can generate a plurality of features from the user account data characterizing interactions between the first user and the computing platform. From at least the plurality of features, the system can generate a score at least partially representing a predicted likelihood that the additional computing resources allocated to the first user account will be used in violation of one or more predetermined abusive usage parameters during a predetermined future time period.
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公开(公告)号:US10460229B1
公开(公告)日:2019-10-29
申请号:US15464053
申请日:2017-03-20
Applicant: Google LLC
Inventor: Dayu Yuan , Ryan P. Doherty , Colin Hearne Evans , Julian David Christian Richardson , Eric E. Altendorf
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for disambiguating word sense. One of the methods includes maintaining a respective word sense numeric representation of each of a plurality of word senses of a particular word; receiving a request to determine the word sense of the particular word when included in a particular text sequence, the particular text sequence comprising one or more context words and the particular word; determining a context numeric representation of the context words in the particular text sequence; and selecting a word sense of the plurality of word senses having a word sense numeric representation that is closest to the context numeric representation as the word sense of the particular word when included in the particular text sequence.
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