Context-Specific LLM Browser Buttons for Faster AI Workflows
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Solution Overview
Problem
Conventional systems for accessing artificial intelligence models are inefficient, operationally inflexible, and inaccurate due to localized access, excessive navigation, and reliance on incomplete user prompts.
Innovation Solution
A floating widget approach that generates adaptable large language model (LLM) function buttons within a web browser, dynamically modifying based on webpage content to provide context-specific functionalities and autonomous workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems provide localized access to AI models within a singular webpage, then the system structure is simple, but the navigation efficiency deteriorates requiring excessive device interactions
Solution Approach 1:
The patent introduces a floating widget that operates in a separate dimensional space from the webpage content. This widget persists across different webpages and provides AI model access independently of the webpage's structural layout, allowing users to access AI functions without navigating through the webpage's navigation hierarchy.
Solution Approach 2:
The floating widget serves as an intermediary between the user and the AI model. Instead of requiring users to navigate through the webpage to access AI functions, the widget acts as a persistent mediator that provides direct access to AI capabilities regardless of the current webpage context.
2Measurement precision
If conventional systems require users to navigate to specific web addresses to interact with AI models, then the access control is precise, but the operational flexibility deteriorates
Solution Approach 1:
The floating widget provides universal access to AI model functions across multiple different webpages and contexts. The same widget instance can be used to access AI capabilities whether the user is viewing a news article, a documentation page, or any other webpage, making the system multi-functional and context-independent.
Solution Approach 2:
The system dynamically adapts to different webpage contexts while maintaining consistent AI access. The floating widget adjusts its behavior based on the current webpage content and user interactions, providing relevant AI functions for each context without requiring users to navigate to specific locations.
3Device complexity
If conventional systems rely on user prompts for AI responses, then the system is simple to implement, but the response accuracy deteriorates due to incomplete data
Solution Approach 1:
The system performs preliminary actions by automatically analyzing the current webpage content and extracting relevant information before the user needs to query the AI model. This pre-processing of context information ensures that the AI receives complete and accurate data about the webpage, improving response accuracy without requiring complex user prompt engineering.
Solution Approach 2:
The system incorporates feedback loops where the AI model analyzes the webpage content, generates responses, and the user can provide feedback on the accuracy and relevance. This feedback mechanism allows the system to continuously improve its understanding of the content and enhance response accuracy over time.
Data Source
AI summary
This disclosure describes systems that generate a selection of large language model (LLM) function buttons in a floating widget within a web browser of a client device. The disclosed systems can generate or otherwise select the LLM function buttons to include based on context of a webpage within the web browser. Responsive to detecting an indication of an interaction with an LLM function button, the disclosed systems can generate a side panel within the web browser according to the LLM function button. In some embodiments, the disclosed systems can display and utilize a customized LLM function button responsive to detecting a certain webpage or content within the webpage. Further, in some embodiments, the disclosed systems can generate an LLM function button to perform a customized workflow responsive to detecting a certain webpage or content within the webpage.