公开(公告)号：US12106199B2

公开(公告)日：2024-10-01

申请号：US18304284

申请日：2023-04-20

Applicant: Salesforce, Inc.

Inventor： Rakesh Ganapathi Karanth ,  Arun Kumar Jagota ,  Kaushal Bansal ,  Amrita Dasgupta

IPC: G06N20/20 ,  G06N7/01

CPC classification number: G06N20/20 ,  G06N7/01

Abstract: An online system performs predictions for real-time tasks and near real-time tasks based on available network bandwidth. A client device receives a regression based machine learning model. Responsive to receiving a task, the client device determines an available network bandwidth for the client device. If the available network bandwidth is below a threshold, the client device uses the regression based machine learning model to perform the task. If the client device determines that the network bandwidth is above the threshold, the client device extracts features of the task, serializes the extracted features, and transmits the serialized features to an online system, causing the online system to use a different machine learning model to perform the task based on the serialized features.

公开(公告)号：US11886461B2

公开(公告)日：2024-01-30

申请号：US16528175

申请日：2019-07-31

Applicant: Salesforce, Inc.

Inventor： Arun Kumar Jagota ,  Stanislav Georgiev

IPC: G06N20/00 ,  G06N7/01 ,  G06F16/25 ,  G06F16/2455

CPC classification number: G06F16/258 ,  G06F16/2456 ,  G06N7/01 ,  G06N20/00

Abstract: A system tokenizes raw values and corresponding standardized values into raw token sequences and corresponding standardized token sequences. A machine-learning model learns standardization from token insertions and token substitutions that modify the raw token sequences to match the corresponding standardized token sequences. The system tokenizes an input value into an input token sequence. The machine-learning model determines a probability of inserting an insertion token after an insertion markable token in the input token sequence. If the probability of inserting the insertion token satisfies a threshold, the system inserts the insertion token after the insertion markable token in the input token sequence. The machine-learning model determines a probability of substituting a substitution token for a substitutable token in the input token sequence. If the probability of substituting the substitution token satisfies another threshold, the system substitutes the substitution token for the substitutable token in the input token sequence.

公开(公告)号：US20240020479A1

公开(公告)日：2024-01-18

申请号：US17952155

申请日：2022-09-23

Applicant: Salesforce, Inc.

Inventor： Akash Singh ,  Rajdeep Dua ,  Arun Kumar Jagota

IPC: G06F40/295 ,  G06F40/284 ,  G06N3/063 ,  G06N3/08

CPC classification number: G06F40/295 ,  G06F40/284 ,  G06N3/063 ,  G06N3/084

Abstract: A cloud platform trains a machine-learned entity matching model that generates predictions on whether a pair of electronic records refer to a same entity. In one embodiment, the entity matching model is configured as a transformer architecture. In one instance, the entity matching model is trained using a combination of a first loss and a second loss. The first loss indicates a difference between an entity matching prediction for a training instance and a respective match label for the training instance. The second loss indicates a difference between a set of named-entity recognition (NER) predictions for the training instance and the set of NER labels for the tokens of the training instance.

公开(公告)号：US20230259831A1

公开(公告)日：2023-08-17

申请号：US18304284

申请日：2023-04-20

Applicant: salesforce, Inc.

Inventor： Rakesh Ganapathi Karanth ,  Arun Kumar Jagota ,  Kaushal Bansal ,  Amrita Dasgupta

IPC: G06N20/20 ,  G06N7/01

CPC classification number: G06N20/20 ,  G06N7/01

Abstract: An online system performs predictions for real-time tasks and near real-time tasks based on available network bandwidth. A client device receives a regression based machine learning model. Responsive to receiving a task, the client device determines an available network bandwidth for the client device. If the available network bandwidth is below a threshold, the client device uses the regression based machine learning model to perform the task. If the client device determines that the network bandwidth is above the threshold, the client device extracts features of the task, serializes the extracted features, and transmits the serialized features to an online system, causing the online system to use a different machine learning model to perform the task based on the serialized features.