公开(公告)号：US20240054149A1

公开(公告)日：2024-02-15

申请号：US17819502

申请日：2022-08-12

Applicant: Salesforce, Inc.

Inventor： Rakesh Ganapathi Karanth ,  Parth Vaishnav ,  Chris Robison ,  Russ Halvorson

IPC: G06F16/28 ,  G06F9/46 ,  G06F16/27

CPC classification number: G06F16/284 ,  G06F9/466 ,  G06F16/273

Abstract: A contextual processing engine architecture. The architecture utilizes data objects retrieved from a database to form a new transactional item data structure as input into a contextual processing engine. The transactional data structure includes a prior context pointer to point to historical context. The historical context can be null for new transactions or one or more basis transaction item data structures for contextual transactions. The processing engine processes the input using process functions lists and aggregates the results for output.

公开(公告)号：US11977761B2

公开(公告)日：2024-05-07

申请号：US16798008

申请日：2020-02-21

Applicant: Salesforce, Inc.

Inventor： Kaushal Bansal ,  Rakesh Ganapathi Karanth ,  Vaibhav Tendulkar ,  Venkata Muralidhar Tejomurtula

IPC: G06F3/06

CPC classification number: G06F3/0644 ,  G06F3/0604 ,  G06F3/0665 ,  G06F3/067

Abstract: Examples include maintaining a virtual pool of containers; receiving a request from a client for one of a plurality of services to performed; when the request includes client code, determining whether the request belongs to regular or priority queue based on two models; adding the request to an appropriate shard in the queue; getting the request from the selected one of the plurality of queues and assigning a container for the request from the virtual pool of containers, the client code to be executed in the container; and after the client code is executed in the container, deleting the container from the virtual pool.

公开(公告)号：US11799901B2

公开(公告)日：2023-10-24

申请号：US16750892

申请日：2020-01-23

Applicant: Salesforce, Inc.

Inventor： Kaushal Bansal ,  Vaibhav Tendulkar ,  Rakesh Ganapathi Karanth ,  Fangchen Richard Sun

IPC: H04L29/06 ,  H04L9/40 ,  G06N5/04 ,  G06N20/00 ,  H04L67/306 ,  G06N20/20

CPC classification number: H04L63/1458 ,  G06N5/04 ,  G06N20/00

Abstract: Examples include a method of predictive rate limiting for performing services requested by a client in a cloud computing system. The method includes receiving a request from a client for one of a plurality of services to be performed, the client belonging to an organization; and determining a current threshold for the organization by applying a real time data model and a historical data model, the real time data model generating a first threshold at least in part by determining a number of requests received from the organization over a first preceding period of time; the historical data model generating a second threshold, the historical data model being generated by applying a machine learning model to historical data stored during processing of previous requests for the plurality of services from the organization over a second preceding period of time, the current threshold being the average of the first threshold and the second threshold. The method further includes performing the requested service when the current threshold is not exceeded; and denying the request when the current threshold is exceeded.

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：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.