In-Browser AI Pipeline Execution for Private On-Device Processing
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Solution Overview
Problem
Delivering AI at scale across various platforms is complex due to high computing costs and compatibility issues in server-based environments, which increase with the complexity and usage of AI, affecting user experience and data security.
Innovation Solution
Implementing on-device, in-browser AI processing using a client web browser that enables users to create and execute AI pipelines, segmenting content, generating data features, and executing AI modules to generate insights, with a client-side framework for improved performance and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If AI models run on servers in cloud environments, then AI processing capability is provided, but computing costs increase significantly
Solution Approach 1:
The patent extracts AI processing capability from centralized server environments and brings it to individual client devices. By loading AI models and executing them locally in the browser or on the device, the system eliminates the need for expensive cloud computing resources while maintaining AI functionality.
Solution Approach 2:
The patent implements local copies of AI models on client devices rather than relying on remote server instances. This copying approach enables AI processing without continuous subscription to costly cloud computing services, reducing ongoing computing costs.
2Ease of operation
If more AI skills and models are deployed to improve user experience, then user experience improves, but the complexity of AI delivery increases
Solution Approach 1:
The patent creates a universal AI delivery framework that can handle multiple AI skills and models through a single standardized interface. This framework abstracts the complexity of deploying and managing various AI models, allowing developers to add AI functionality without increasing operational complexity.
Solution Approach 2:
The patent introduces an intermediary framework layer between the application and AI models. This framework manages model loading, execution, and coordination, shielding the complexity of AI delivery from the application developer and simplifying the overall system.
3Reliability
If AI processing is done on client devices, then data privacy improves, but compatibility issues and performance challenges arise
Solution Approach 1:
The patent dynamically adjusts AI model parameters such as precision, batch size, and computational intensity based on the capabilities of the client device. This allows the same AI framework to adapt to different hardware configurations while maintaining both privacy and performance.
Solution Approach 2:
The patent implements a dynamic AI execution environment that can adapt to varying device capabilities in real-time. The system monitors device performance and adjusts model complexity and execution parameters accordingly, ensuring compatibility across different platforms while maintaining optimal performance.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for on-device, in-browser AI processing. In examples, a selection of an AI pipeline is received. Content associated with the AI pipeline is also received. The content is segmented into multiple data segments and a set of data features is generated for the data segments. AI modules associated with the AI pipeline are loaded to create the AI pipeline. The set of data features is provided to the AI pipeline. The AI pipeline is executed to generate insights for the set of data features. The insights are then provided to a user.