Trusted AI Store for Secure Personalized Topology Generation
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Solution Overview
Problem
Current cloud-based AI services face challenges such as high processing and storage demands, unpredictable performance due to simultaneous requests or denial of service attacks, and unavailability when user devices are offline. Additionally, users seek personalized AI solutions but risk data privacy issues with untrustworthy vendors.
Innovation Solution
A trusted AI store provides a secure environment for AI topology construction, offering support for vendors and clients with secure output personalization. This involves a multi-tiered AI topology with certified access to private data, secure partitioning, and digital rights management to ensure data privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based AI services are used to meet high processing and storage demands, then AI service capability is improved, but system availability deteriorates when user devices are offline
Solution Approach 1:
The patent segments the AI system into multiple components: cloud-based AI services for high-capacity processing, edge devices for local inference, and mobile devices for user interaction. This segmentation allows the system to maintain availability by executing critical AI functions locally on edge devices while leveraging cloud services when available, thus resolving the contradiction between cloud dependency and system availability.
Solution Approach 2:
The patent implements preliminary action by pre-training AI models in the cloud and deploying them to edge devices before offline operations are needed. This advance preparation ensures that essential AI capabilities are already present on local devices, enabling them to function independently when cloud services are unavailable, thereby maintaining system availability without sacrificing processing capability.
2Adaptability or versatility
If cloud AI services handle massive simultaneous requests, then service coverage is improved, but service predictability deteriorates due to unpredictable delays
Solution Approach 1:
The patent divides the AI service architecture into cloud-based training services and edge-based inference services. This segmentation allows the cloud to handle model updates and heavy computational tasks while edge devices handle real-time inference requests locally, eliminating the unpredictable delays associated with cloud round-trips during high-load periods and ensuring predictable service delivery.
Solution Approach 2:
The patent introduces edge devices as intermediaries between users and cloud services. These edge devices cache AI models and handle inference requests locally, acting as a buffer that absorbs traffic spikes and prevents direct overload of cloud services, thereby maintaining both broad service coverage and predictable response times even during massive simultaneous request scenarios.
3Adaptability or versatility
If users access third-party vendor AI solutions for personalization, then AI service customization is improved, but data security deteriorates due to potential vendor malice
Solution Approach 1:
The patent implements preliminary action by establishing a trust certification framework before any data exchange occurs. Vendors must obtain trust certifications through verified security audits and compliance checks before being authorized to access user data. This advance verification ensures that only trusted vendors can provide personalized services, enabling customization while mitigating security risks from potentially malicious actors.
Solution Approach 2:
The patent introduces a trust certification authority as an intermediary between users and third-party vendors. This intermediary verifies vendor credentials, enforces security protocols, and mediates data access permissions. By inserting this trusted intermediary layer, the system enables vendors to provide customized AI services while preventing unauthorized or malicious data access, thus resolving the contradiction between service customization and data security.
Data Source
AI summary
Host services in an artificial intelligence (AI) infrastructure supporting a visual creation interface of multi-node AI based topologies in segments and partitions to service various overall AI objectives across multiple host, creator, user and third party devices and systems. Training of AI nodes, testing, automatic topology assessment and adjustment is provided. Secure interaction between topology partitions supports privacy concerns. Influencers and celebrities provide personal data to AI topologies to automatically generate topology node configurations, training data and datasets that can be swapped into any topology for those addressing host's digital rights management compensation and restrictions. Host services provide platforms for sharing created AI topologies, AI generations based on such AI topologies, and celebrity and influencer elements thereof. User device circuitry bisection supports secure topology operations and communications with other topology partitions, and allows for compartmentalization of privacy concerns. AI generated output can be generally produced can be prepared post generation or output with such form which supports personalization through population with personal data elements and types. Drag and drop visually topology building is enhances with software interfacing through software development kit usage. Host services screens uploaded data to identify third party ownership issues and provides a service for gathering authorizations, allocating associated sharing, and applying digital rights restrictions.