公开(公告)号：US20220394337A1

公开(公告)日：2022-12-08

申请号：US17339700

申请日：2021-06-04

申请人： Adobe Inc.

发明人： Liana Vagharshakian ,  Atanu R. Sinha ,  Camille Girabawe ,  Gautam Choudhary ,  Omar Rahman ,  Scott Trafton ,  Vivek Sinha

IPC分类号： H04N21/466 ,  H04N21/45 ,  H04N21/258

摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

公开(公告)号：US20210110411A1

公开(公告)日：2021-04-15

申请号：US16600099

申请日：2019-10-11

申请人： Adobe Inc.

发明人： Yali Pollak ,  Vivek Sinha ,  Ajay Awatramani

IPC分类号： G06Q30/02 ,  G06N20/00 ,  G06N7/00

摘要： A method, in which one or more processing devices perform operations, includes executing a content-extraction agent that extracts activity data describing interactions with online resources by one or more user devices associated with a target entity. The method includes organizing the activity data into an input descriptive data structure associated with the target entity. The method includes computing a probability of the target entity belonging to a decision-making group by applying, to the input descriptive data structure, a role-classification model that is trained to determine probabilities that entities belong to the decision-making group. The method further includes transmitting an indication of the probability to a content provider, where transmitting the indication of the probability causes the content provider to customize interactive content to the target entity prior to a transmission of the interactive content to the one or more user devices.