公开(公告)号：US11657225B2

公开(公告)日：2023-05-23

申请号：US17348257

申请日：2021-06-15

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F40/284 ,  G06N20/00

CPC classification number: G06F40/284 ,  G06N20/00

Abstract: Systems and methods for generating a tuned summary using a word generation model. An example method includes receiving, at a decoder of the word generation model, a training data learned subspace representation of training data. The method also includes identifying tunable linguistic characteristics of the word generation model and training the decoder to output a training tuned summary of the training data learned subspace representation based on at least one of the tunable linguistic characteristics. The method further includes receiving an input text and a target characteristic token, and generating, by the trained decoder of the word generation model, each word of a tuned summary of the input text from a learned subspace representation and from feedback about preceding words of the tuned summary, wherein the tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US10796095B2

公开(公告)日：2020-10-06

申请号：US15945996

申请日：2018-04-05

Applicant: Adobe Inc.

Inventor： Niyati Himanshu Chhaya ,  Tanya Goyal ,  Projjal Chanda ,  Kushal Chawla ,  Jaya Singh

IPC: G06F40/30 ,  G06N5/04 ,  G06N20/00 ,  G06F40/211 ,  G06F40/284

Abstract: Techniques are disclosed for predicting a tone of a text communication using psycholinguistic features of the text communication. In some examples, a method may include generating a feature vector for a text communication using features, including psycholinguistic features, extracted from the text communication, and predicting a tone of the text communication based on the feature vector. The tone is predicted by a trained prediction module that is trained using psycholinguistic features of text communications in a training set used to train the trained prediction module. The predicted tone is at least one of a predicted measure of frustration, a predicted measure of formality, and a predicted measure of politeness.

公开(公告)号：US10755088B2

公开(公告)日：2020-08-25

申请号：US15868531

申请日：2018-01-11

Applicant: Adobe Inc.

Inventor： Kushal Chawla ,  Vaishnav Pawan Madandas ,  Moumita Sinha ,  Gaurush Hiranandani ,  Aditya Jain

IPC: G06K9/00 ,  G06T19/00 ,  G06Q30/02 ,  G06Q30/06 ,  G06N20/00

Abstract: Systems and methods are disclosed herein for determining user behavior in an augmented reality environment. An augmented reality application executing on a computing system receives a video depicting a face of a person. The video includes a video frame. The augmented reality application augments the video frame with an image of an item selected via input from a user device associated with a user. The augmented reality application determines, from the video frame, a score representing an action unit. The action unit represents a muscle on the face of the person depicted by the video frame and the score represents an intensity of the action unit. The augmented reality application calculates, from a predictive model and based on the score, an indicator of intent of the person depicted by the video.

公开(公告)号：US20210312129A1

公开(公告)日：2021-10-07

申请号：US17348257

申请日：2021-06-15

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F40/284 ,  G06N20/00

Abstract: Certain embodiments involve tuning summaries of input text to a target characteristic using a word generation model. For example, a method for generating a tuned summary using a word generation model includes generating a learned subspace representation of input text and a target characteristic token associated with the input text by applying an encoder to the input text and the target characteristic token. The method also includes generating, by a decoder, each word of a tuned summary of the input text from the learned subspace representation and from a feedback about preceding words of the tuned summary. The tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US11023685B2

公开(公告)日：2021-06-01

申请号：US16412868

申请日：2019-05-15

Applicant: Adobe Inc.

Inventor： Sopan Khosla ,  Kushal Chawla ,  Niyati Himanshu Chhaya

IPC: G06F40/30 ,  G06N3/08

Abstract: Certain embodiments involve facilitating natural language processing through enriched distributional word representations. For instance, a computing system receives an initial word distribution having initial word vectors that represent, within a multidimensional vector space, words in a vocabulary. The computing system also receives a human-reaction lexicon indicating human-reaction values respectively associated with words in the vocabulary. The computing system creates an enriched word distribution by modifying one or more of the initial word vectors such that the distance between the pair of initial word vectors representing a pair of words is decreased based on a human-reaction similarity between the pair of words.

公开(公告)号：US20200242197A1

公开(公告)日：2020-07-30

申请号：US16262655

申请日：2019-01-30

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F17/27 ,  G06N20/00

Abstract: Certain embodiments involve tuning summaries of input text to a target characteristic using a word generation model. For example, a method for generating a tuned summary using a word generation model includes generating a learned subspace representation of input text and a target characteristic token associated with the input text by applying an encoder to the input text and the target characteristic token. The method also includes generating, by a decoder, each word of a tuned summary of the input text from the learned subspace representation and from a feedback about preceding words of the tuned summary. The tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US20190311035A1

公开(公告)日：2019-10-10

申请号：US15945996

申请日：2018-04-05

Applicant: Adobe Inc.

Inventor： Niyati Himanshu Chhaya ,  Tanya Goyal ,  Projjal Chanda ,  Kushal Chawla ,  Jaya Singh

IPC: G06F17/27 ,  G06N5/04 ,  G06N99/00

Abstract: Techniques are disclosed for predicting a tone of a text communication using psycholinguistic features of the text communication. In some examples, a method may include generating a feature vector for a text communication using features, including psycholinguistic features, extracted from the text communication, and predicting a tone of the text communication based on the feature vector. The tone is predicted by a trained prediction module that is trained using psycholinguistic features of text communications in a training set used to train the trained prediction module. The predicted tone is at least one of a predicted measure of frustration, a predicted measure of formality, and a predicted measure of politeness.

公开(公告)号：US11886480B2

公开(公告)日：2024-01-30

申请号：US17822837

申请日：2022-08-29

Applicant: Adobe Inc.

Inventor： Kushal Chawla ,  Niyati Himanshu Chhaya ,  Sopan Khosla

IPC: G06F16/35 ,  G06N3/08 ,  G06N3/04 ,  G06F40/279 ,  G06F40/10 ,  G06N20/00

CPC classification number: G06F16/35 ,  G06F40/10 ,  G06F40/279 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00

Abstract: Certain embodiments involve using a gated convolutional encoder-decoder framework for applying affective characteristic labels to input text. For example, a method for identifying an affect label of text with a gated convolutional encoder-decoder model includes receiving, at a supervised classification engine, extracted linguistic features of an input text and a latent representation of an input text. The method also includes predicting, by the supervised classification engine, an affect characterization of the input text using the extracted linguistic features and the latent representation. Predicting the affect characterization includes normalizing and concatenating a linguistic feature representation generated from the extracted linguistic features with the latent representation to generate an appended latent representation. The method also includes identifying, by a gated convolutional encoder-decoder model, an affect label of the input text using the predicted affect characterization.

公开(公告)号：US11449537B2

公开(公告)日：2022-09-20

申请号：US16224501

申请日：2018-12-18

Applicant: Adobe Inc.

Inventor： Kushal Chawla ,  Niyati Himanshu Chhaya ,  Sopan Khosla

IPC: G06F16/35 ,  G06N3/08 ,  G06N3/04 ,  G06F40/279

Abstract: Certain embodiments involve using a gated convolutional encoder-decoder framework for applying affective characteristic labels to input text. For example, a method for identifying an affect label of text with a gated convolutional encoder-decoder model includes receiving, at an encoder, input text. The method also includes encoding the input text to generate a latent representation of the input text. Additionally, the method includes receiving, at a supervised classification engine, extracted linguistic features of the input text and the latent representation of the input text. Further, the method includes predicting an affect characterization of the input text using the extracted linguistic features and the latent representation. Furthermore, the method includes identifying an affect label of the input text using the predicted affect characterization. The gated convolutional encoder-decoder model is jointly trained using a weighted auto-encoder loss associated with a reconstruction engine and a weighted classification loss associated with the supervised classification engine.

公开(公告)号：US11080745B2

公开(公告)日：2021-08-03

申请号：US15435869

申请日：2017-02-17

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Kushal Chawla ,  Yash Shrivastava ,  Dhruv Singal ,  Atanu Ranjan Sinha ,  Deepak Pai

IPC: G06Q30/02

Abstract: Forecasting a potential audience size and an unduplicated audience size for a digital campaign includes receiving an audience segment input and a time period input. The audience segment input is converted into multiple atomic target specifications. For each of the multiple atomic target specifications, a potential audience size is determined during the time period input by selecting a time series model based on a frequency of attribute values from the atomic target specification and combining the selected time series model with a frequent item set model. The potential audience size for each of atomic target specifications is aggregated over the time period input into a total potential audience size. The total potential audience size is output. The time series model and the frequent item set model are obtained using data from a historic bid request database.