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公开(公告)号：US10270746B2
公开(公告)日：2019-04-23
申请号：US15415684
申请日：2017-01-25
Applicant: Facebook, Inc.
Inventor： Li Zhou , William Bullock , Anh Phuong Bui
Abstract: Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.
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公开(公告)号：US20190080366A1
公开(公告)日：2019-03-14
申请号：US15699522
申请日：2017-09-08
Applicant: Facebook, Inc.
Inventor： Li Zhou , Aleksey Sergeyevich Fadeev , William Bullock , Wei Liu
Abstract: A method is disclosed for attributing conversions among multiple members of a socially connected influence group, such as a household. Data from advertising impressions, including views and clicks, is maintained by an online system. When a conversion is made, the social network of the user creating the conversion event is analyzed. An influence group, defined as a group comprising the users and group of socially connected users whom influence the purchasing decisions of the first user, is created. Conversion data is analyzed for the first user and the other members of the influence group. This data is weighted to determine the propensities of successful conversions among all members of the influence group.
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公开(公告)号：US10949434B1
公开(公告)日：2021-03-16
申请号：US16000747
申请日：2018-06-05
Applicant: Facebook, Inc.
Inventor： Sanjay Kanaka Sai Tirupattur Saravanan , Bradley H Smallwood , Frederick R. Leach , William Bullock
IPC: G06F7/20 , G06N5/02 , G06N20/00 , G06F16/2458 , G06F16/2457
Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating an identity resolution model from a ground truth data set to accurately match users across one or more digital content providers to perform analyses of user activities across the one or more digital content providers. For example, the systems described herein can generate a ground truth data set of known users and utilize the ground truth data set to generate an identity resolution model for one or more digital content providers based on predicted user identities. Furthermore, in one or more embodiments, the systems utilize the identity resolution model to accurately resolve and match user identities between one or more digital content providers and assign universal identifiers to the user identities. Moreover, the disclosed systems can utilize the universal identifiers to provide analytical insights of user actions between the one or more digital content providers.
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公开(公告)号：US20200036802A1
公开(公告)日：2020-01-30
申请号：US16049712
申请日：2018-07-30
Applicant: Facebook, Inc.
Inventor： William Bullock
Abstract: In one embodiment, a method includes receiving one or more communication network addresses and one or more geographic locations of each network address, determining one or more location-related features based on each network address, generating one or more predicted locations of the network address, each predicted location corresponding to one of the first geographic locations of the network address, and each predicted location being associated with a time stamp representing an age of the predicted location, determining, based on the location-related features and the time stamps, a weighting factor representing a probability that at least one of the predicted locations of the network address corresponds to a true location of the network address, and determining, for each of the predicted locations, a weight based on at least the weighting factor, wherein the weight represents a probability that the predicted location corresponds to the true location of the network address.
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公开(公告)号：US20180150883A1
公开(公告)日：2018-05-31
申请号：US15365849
申请日：2016-11-30
Applicant: Facebook, Inc.
Inventor： Joseph Poj Davin , William Bullock , Erjie Ang
CPC classification number: G06Q30/0269 , G06N20/00
Abstract: An online system provides content items to target users who are identified to have high incremental likelihood of performing conversion actions when presented with content items. The incremental likelihood represents the difference between the response likelihood of performing conversion actions when a content item is presented to a user, and the baseline likelihood when a content item is not presented to the user. The baseline and response likelihood for a user are predicted by one or more machine-learned models. By targeting the content to users that are likely to have a high incremental likelihood, the online system provides content items to users whose conversion actions are more likely to be impacted by the presentation of content items, rather than users that may just be of interest for performing the action.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11030650B2
公开(公告)日：2021-06-08
申请号：US15979332
申请日：2018-05-14
Applicant: Facebook, Inc.
Inventor： William Bullock , Li Zhou
Abstract: An online system receives a request from an online system user to present a content item associated with an action that may be performed on a third party website not associated with the user. The online system identifies a set of third party websites on which the action may be performed based on information provided by content publishers associated with the websites describing performances of the action on the websites. The online system predicts a likelihood a viewing user of the online system presented with the content item will perform the action on each third party website based on the information provided by the content publishers and selects a website associated with a highest predicted likelihood the viewing user will perform the action on the website. The online system generates the content item including a link to the selected website and provides the content item for presentation.
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公开(公告)号：US10728225B1
公开(公告)日：2020-07-28
申请号：US16293654
申请日：2019-03-06
Applicant: Facebook, Inc.
Inventor： Li Zhou , William Bullock , Anh Phuong Bui
Abstract: Embodiments include one or more client devices accessible by users, an online system, and one or more partner systems such that the online system is able to identify a user of the online system across different devices and browsers based on the user activity that occurs external to the online system. A user performs user actions (e.g. purchase a product) on a web page of a partner system and may provide personally identifiable information (PII) to the partner system. The partner system provides the hashed PII and user actions performed by the user to the online system. The online system identifies a user profile on the online system by matching personal information in the user profile to the hashed PII. The online system generates a confidence score indicating a likelihood that the identified user of the online system is the individual that performed the external user action.
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公开(公告)号：US20200043046A1
公开(公告)日：2020-02-06
申请号：US16054367
申请日：2018-08-03
Applicant: Facebook, Inc.
Inventor： Tsuwei Chen , Qizhen Ruan , Roy Koonammave Jose , Scott J. Bratsman , William Bullock , Aude Hofleitner , Yoav Shapira , Mostafa Keikha
Abstract: In one embodiment, a method includes analyzing social graph information associated with users of a social-networking system, developing feature vectors describing elements of social graph information, and applying the feature vectors to determine the relevance of elements of social graph information to the location of special relevance. The method further includes receiving at least one data point from a user's networked device, applying the feature vectors to the at least one data point to determine the relevance of the at least one data point to the location of special relevance, and assigning weight to each data point based on the determined relevance of each data point to the location of special relevance. Finally, the method includes processing the at least one data point according to its assigned weight and forming a prediction, to a particular degree of certainty, indicating the user's location of special relevance.
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公开(公告)号：US20190069030A1
公开(公告)日：2019-02-28
申请号：US15686489
申请日：2017-08-25
Applicant: Facebook, Inc.
Inventor： Saul Peretz Jackman , William Bullock
IPC: H04N21/466 , G06Q30/02 , G06Q50/00 , G06N99/00
Abstract: An online system determines an effect of presenting a content item in causing users to perform a specific action associated with the content item without excluding presentation of content item from certain users. The online system presents the content item to various users and identifies a set of users not presented with the content item. Based on probabilities of presenting the content item to users of the set and to users to whom the content item was presented, the online system weights users of the set so a distribution of probabilities of being presented with the content item for the set matches a distribution of probabilities of the content item being presented to the users who were presented with the content item. The online system determines a metric based on the weights and occurrences of the specific action by users of the set and users presented with the content item.
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公开(公告)号：US20180332140A1
公开(公告)日：2018-11-15
申请号：US15592108
申请日：2017-05-10
Applicant: Facebook, Inc.
Inventor： William Bullock , Liang Xu , Li Zhou
CPC classification number: H04L67/327 , G06F17/277 , G06F17/30702 , G06K9/00677 , G06Q30/0269 , H04L67/306
Abstract: An online system predicts household features of a user, e.g., household size and demographic composition, based on image data of the user, e.g., profile photos, photos posted by the user and photos posted by other users socially connected with the user, and textual data in the user's profile that suggests relationships among individuals shown in the image data of the user. The online system applies one or more models trained using deep learning techniques to generate the predictions. For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user's profile data and tags associated with the photos. The online system uses the predictions to build more information about the user and his/her household in the online system, and provide improved and targeted content delivery to the user and the user's household.
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