US 12,608,668 B1Granted

Automatically determining workstreams from common actions in online applications

Assignee8flow Inc.

InventorsBoaz Hecht, Josh Russ, Yev Goldin

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Abstract

An embodiment uses a computer-implemented approach to identify and automate workstreams from common actions in online applications. A browser extension collects and processes user event metadata, which is used to train machine learning models for workflow automation. The extension reads relevant fields from web services and delivers prompts with high-level actions (HLAs) based on user or organizational details. These HLAs are appended to prompts and sent to a large language model (LLM) via an API call. The LLM responds with a solution and a sequence of actions, which the browser extension executes automatically. Users retain control to pause, accelerate, or modify these actions. This method ensures secure, end-to-end workflow automation across various computing devices, enabling efficient and autonomous resolution of tasks while maintaining user oversight. The browser extension processes metadata, not enterprise data, ensuring security and privacy.

Claims24

Dependent claims

One or more non-transitory computer-readable storage media storing a workflow automation application comprising one or more sequences of instructions which, when executed using one or more processors, the one or more processors being communicatively coupled to one or more network interfaces that are communicatively coupled to one or more internetworks and capable of network communication with a program hosted on an agent computer that also hosts a browser, cause the one or more processors to execute:

receiving browser tab event data indicating a change to a new or different browser tab associated with navigation of the browser to a web service;
in response to receiving the browser tab event data indicating the change, extracting web service data from a web page served to the browser from the web service;
retrieving, from digital data storage, a first prompt text for a first prompt to a large language model (LLM) and a first list of actions from a vocabulary of common user actions;
programmatically calling the LLM and providing a first engineered prompt comprising the first prompt text, the first list of actions, and the web service data;
receiving a response to the first engineered prompt from the LLM, the response comprising a second list of actions;
and
posting a plurality of requests to the web service, each request among the plurality of requests specifying to execute a different successive action in the second list of actions.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/076,692Filed 11 Mar 2025 · us provisional application
Priority: 23 Oct 2024
Published: US 2026/0111815 A123 Apr 2026
Granted: 21 Apr 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 codes

G06Q 10/06316
Resource planning, allocation, distributing or scheduling for enterprises or organisations: sequencing of tasks or work
G06F 9/547
Interprogram communication: remote procedure calls [RPC]; Web services
G06F 9/548
Remote procedure calls [RPC]; Web services: object oriented; Remote method invocation [RMI]
G06Q
Information and communication technology [ict] specially adapted for administrative, commercial, financial, managerial