公开(公告)号：US20230042318A1

公开(公告)日：2023-02-09

申请号：US17971312

申请日：2022-10-21

Applicant: Salesforce, Inc.

Inventor： Nikhil Naik ,  Ali Madani ,  Nitish Shirish Keskar

IPC: G16H50/20 ,  G06K9/62 ,  G06N5/04 ,  G16H10/20 ,  G06N20/00

Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of non-overlapping image tiles. Bags of tiles are created through sampling of the image tiles. For each H&E stain image, the system generates a feature vector from a bag of tiles sampled from the partitioned image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. With the trained models, the analytics system predicts a hormone receptor status by applying a prediction model to the feature vector for a test H&E stain image.

公开(公告)号：US11948665B2

公开(公告)日：2024-04-02

申请号：US17001068

申请日：2020-08-24

Applicant: Salesforce, Inc.

Inventor： Ali Madani ,  Bryan McCann ,  Nikhil Naik

IPC: G16B40/30 ,  G06F30/20 ,  G06F111/08 ,  G16B5/20 ,  G16B15/20 ,  G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G16B50/10

CPC classification number: G16B40/30 ,  G06F30/20 ,  G16B5/20 ,  G16B15/20 ,  G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G16B50/10 ,  G06F2111/08

Abstract: The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.

公开(公告)号：US11810298B2

公开(公告)日：2023-11-07

申请号：US17971312

申请日：2022-10-21

Applicant: Salesforce, Inc.

Inventor： Nikhil Naik ,  Ali Madani ,  Nitish Shirish Keskar

IPC: G06T7/00 ,  G16H50/20 ,  G06N5/04 ,  G16H10/20 ,  G06N20/00 ,  G06F18/21 ,  G06F18/214 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06F18/217 ,  G06F18/2148 ,  G06N5/04 ,  G06N20/00 ,  G06V20/69 ,  G16H10/20 ,  G16H50/20 ,  G06V2201/03

Abstract: An analytics system uses one or more machine-learned models to predict a hormone receptor status from a H&E stain image. The system partitions H&E stain images each into a plurality of non-overlapping image tiles. Bags of tiles are created through sampling of the image tiles. For each H&E stain image, the system generates a feature vector from a bag of tiles sampled from the partitioned image tiles. The analytics system trains one or more machine-learned models with training H&E stain images having a positive or negative receptor status. With the trained models, the analytics system predicts a hormone receptor status by applying a prediction model to the feature vector for a test H&E stain image.

公开(公告)号：US12229655B2

公开(公告)日：2025-02-18

申请号：US17353691

申请日：2021-06-21

Applicant: Salesforce, Inc.

Inventor： Ali Madani ,  Alvin Guo Wei Chan

IPC: G06N3/045 ,  G06N3/088

Abstract: Embodiments described herein provide methods and systems for generating data samples with enhanced attribute values. Some embodiments of the disclosure disclose a deep neural network framework with an encoder, a decoder, and a latent space therebetween, that is configured to extrapolate beyond the attributes of samples in a training distribution to generate data samples with enhanced attribute values by learning the latent space using a combination of contrastive objective, smoothing objective, cycle consistency objective, and a reconstruction loss.

公开(公告)号：US20240203532A1

公开(公告)日：2024-06-20

申请号：US18589215

申请日：2024-02-27

Applicant: Salesforce, Inc.

Inventor： Ali Madani ,  Bryan McCann ,  Nikhil Naik

IPC: G16B40/30 ,  G06F30/20 ,  G06F111/08 ,  G16B5/20 ,  G16B15/20 ,  G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G16B50/10

CPC classification number: G16B40/30 ,  G06F30/20 ,  G16B5/20 ,  G16B15/20 ,  G16B25/10 ,  G16B30/00 ,  G16B40/20 ,  G16B50/10 ,  G06F2111/08

Abstract: The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.