公开(公告)号：US20250103822A1

公开(公告)日：2025-03-27

申请号：US18372462

申请日：2023-09-25

Applicant: Adobe Inc.

Inventor： Niranjan Kumbi ,  Sreekanth Reddy ,  Sumit Bhatia ,  Milan Aggarwal ,  Simra Shahid ,  Nikitha Srikanth ,  Camille Girabawe ,  Narayanan Seshadri

IPC: G06F40/35

Abstract: System and methods for generating, validating, and augmenting question-answer pairs using generative AI are provided. An online interaction server accesses a set of digital content available at a set of designated network locations. The online interaction server further trains a pre-trained large language model (LLM) using the set of digital content to obtain a customized LLM. The online interaction server generates a set of question-answer pairs based on the set of digital content using the customized LLM and validates the set of question-answer pairs by determining if an answer in a question-answer pair is derived from the set of digital content. The online interaction server also selects a digital asset to augment an answer in a validated question-answer pair based on a semantic similarity between the validated question-answer pair and the digital asset.

公开(公告)号：US20220394337A1

公开(公告)日：2022-12-08

申请号：US17339700

申请日：2021-06-04

Applicant: Adobe Inc.

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

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

Abstract: 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：US20220245446A1

公开(公告)日：2022-08-04

申请号：US17164111

申请日：2021-02-01

Applicant: ADOBE INC.

Inventor： Saayan Mitra ,  Xiang Chen ,  Akangsha Sunil Bedmutha ,  Viswanathan Swaminathan ,  Omar Rahman ,  Camille Girabawe

IPC: G06N3/08 ,  G06N3/04

Abstract: An improved electronic communication system schedules transmission of electronic communications based on a predicted open time and click time. The open and click times are predicted from a machine learning model that is trained to optimize for both tasks. Additionally, when training the machine learning model, the loss used for adjusting the system to achieve a desired accuracy may be a biased loss determined from a function that penalizes overpredicting the open time. As such, the loss value may be determined by different set of rules depending on whether the predicted time is greater than the actual time or not.

公开(公告)号：US20210342866A1

公开(公告)日：2021-11-04

申请号：US16861756

申请日：2020-04-29

Applicant: Adobe Inc.

Inventor： Camille Girabawe ,  Richard Yang ,  Goutham Srivatsav Arra ,  Akangsha Sunil Bedmutha ,  Omar Rahman ,  Niranjan Kumbi ,  Vaidyanathan Venkatraman

IPC: G06Q30/02 ,  G06F16/28 ,  G06F16/901 ,  G06F16/9535

Abstract: Techniques are disclosed for selecting audience members for a marketing campaign. A list of potential members is accessed, where each member is associated with a corresponding feature vector comprising features. A subset of the features is selected, and used to select a first group from the list for inclusion in the campaign, thereby also defining a second group from the list for exclusion from the campaign. A first similarity among the members in the first group is compared to a second similarity between the members in the first and second groups. If the first similarity is equal to or lower than the second similarity, the subset of features is updated to form a new subset of features, and the selection process of target audience member is repeated, until the first similarity becomes higher than the second similarity. Subsequently, the marketing campaign is launched with the first group of members.

公开(公告)号：US20230409621A1

公开(公告)日：2023-12-21

申请号：US17845437

申请日：2022-06-21

Applicant: Adobe Inc.

Inventor： Xiang Chen ,  Viswanathan Swaminathan ,  Saayan Mitra ,  Camille Girabawe ,  Sreekanth Reddy

IPC: G06F16/35 ,  G06F40/279

CPC classification number: G06F16/35 ,  G06F40/279

Abstract: A topic mapping system generates customized mapping schemas for multiple topic sets. The topic mapping system generates document clusters that represent groups of digital documents. The topic mapping system also generates, for each topic set, a document-topic mapping data object (“DTM data object”) that describes a customized mapping schema of the document clusters to labels in the topic set. The topic mapping system identifies customized groups of documents for responding to multiple requests that have a particular keyword. For each request, the topic mapping system identifies a particular topic set and DTM data object associated with a computing system that provided the request. Based on the keyword, the topic mapping system identifies documents that are categorized according to the customized mapping schema in the DTM data object. The topic mapping system can provide customized groups of documents to respective computing systems that provided the multiple requests.

公开(公告)号：US20220253690A1

公开(公告)日：2022-08-11

申请号：US17171365

申请日：2021-02-09

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Gautam Choudhary ,  Mansi Agarwal ,  Shivansh Bindal ,  Abhishek Pande ,  Camille Girabawe

IPC: G06N3/08 ,  G06N3/04

Abstract: The present disclosure generally relates to techniques for predicting a collective decision made by a group of users on behalf of a requesting entity. A predictive analysis system includes specialized machine-learning architecture that generates a prediction of a collective group decision based on the captured interactions of individual members of the group.