公开(公告)号：US20240386315A1

公开(公告)日：2024-11-21

申请号：US18318524

申请日：2023-05-16

Applicant: ADOBE INC.

Inventor： Thomas BOUCHER ,  Tanay ANAND ,  Stephane LECERCLE ,  Saurabh GARG ,  Pranjal PRASOON ,  Nikaash PURI ,  Mukul LAMBA ,  Milan AGGARWAL ,  Jayakumar SUBRAMANIAN ,  Francoise CORVAISIER ,  David MENDEZ ACUNA ,  Camel AISSANI ,  Balaji KRISHNAMURTHY

IPC: G06N20/00 ,  G06N5/022

Abstract: Methods and systems are provided for a transformer model for journey simulation and prediction. In embodiments described herein, training data is obtained from stored journeys. The training data for each journey indicates customer interactions with each event in the sequence of events of the journey. A machine learning model is trained using the training data to simulate customer interaction with an input journey. The trained machine learning model then generates a simulation of customer interaction with an input journey and the results of the simulation are displayed.

公开(公告)号：US20250086448A1

公开(公告)日：2025-03-13

申请号：US18367020

申请日：2023-09-12

Applicant: Adobe Inc.

Inventor： Tanay ANAND ,  Siddarth RAMESH ,  Shripad Vilasrao DESHMUKH ,  Jayakumar SUBRAMANIAN

IPC: G06N3/08 ,  G06N3/0475

Abstract: Systems and methods provide a generative recommendation model that leverages verbalizations generated from sequential data. In accordance with some aspects, sequential data for a trajectory comprising a plurality of steps is accessed, in which the sequential data comprises a tuple for each step of the trajectory. Verbalized sequential data is generated from the sequential data, in which the verbalized sequential data for each step of the trajectory comprises one or more natural language sentences generated from the tuple for the step. A generative model is trained on the verbalized sequential data to provide a trained generative model that generates a recommended action given a prompt specifying a current state.

公开(公告)号：US20250124620A1

公开(公告)日：2025-04-17

申请号：US18380059

申请日：2023-10-13

Applicant: Adobe Inc.

Inventor： Tarun ARORA ,  Tanay ANAND ,  Siddarth RAMESH ,  Shripad DESHMUKH ,  Pranjal PRASOON ,  Piyush DEWNANI ,  Md anis ALAM ,  Jayakumar SUBRAMANIAN ,  Gaurav SATIJA ,  Diwakar Reddy YERRAGUNTA ,  Deepthi AMIRTHAGADESWARAN ,  Balaji KRISHNAMURTHY ,  Avinash KATIYAR

IPC: G06T11/20 ,  G06F40/40

Abstract: Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.

公开(公告)号：US20230143777A1

公开(公告)日：2023-05-11

申请号：US17454445

申请日：2021-11-10

Applicant: ADOBE INC.

Inventor： Pinkesh BADJATIYA ,  Tanay ANAND ,  Simra SHAHID ,  Nikaash PURI ,  Milan AGGARWAL ,  S Sejal NAIDU ,  Sharat Chandra RACHA

IPC: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

CPC classification number: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

Abstract: A method of finding online relevant conversing posts, comprises receiving, by a web server serving an online forum, a query post from an inquirer using the online forum, computing a contextual similarity score between each conversing post of a set of conversing posts with a query post, wherein the contextual similarity score is computed between the body of each of conversing posts and of the query post, wherein N1 conversing posts with a highest contextual similarity score are selected; computing a fine grained similarity score between the subject of the query post and of each of the N1 conversing posts, wherein N2 conversing posts with a highest fine grained similarity score are selected; and boosting the fine grained similarity score of the N2 conversing posts based on relevance metrics, wherein N3 highest ranked conversing posts are selected as a list of conversing posts most relevant to the query post.