AI Topology Generation for Custom Output
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI technologies are limited by their single-node design, requiring high costs and expertise for custom implementations, making them inaccessible to most companies and users.
Innovation Solution
An AI-based auto-topology generating infrastructure that captures user interactions and source data to automatically create multiple topology frames, enabling automated generation of AI outputs without extensive user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-node AI models are used, then implementation simplicity is maintained, but customization capability and functional completeness deteriorate
Solution Approach 1:
The patent segments the AI system into multiple specialized nodes (text generation node, image generation node, video generation node, audio generation node) instead of using a single monolithic model. Each node handles specific generative tasks, allowing the system to maintain simplicity at the individual node level while achieving comprehensive customization capability at the system level through modular architecture.
2Adaptability or versatility
If custom AI designs are created, then functional completeness is improved, but cost and skill requirements increase
Solution Approach 1:
The patent creates a universal AI topology framework that can generate multiple types of content (text, images, video, audio) through a standardized set of nodes and connections. This multi-functional architecture allows any user to access comprehensive generative capabilities without needing to build custom models from scratch, as the system provides pre-built functional modules that can be combined for various purposes.
Solution Approach 2:
The patent employs pre-trained AI models as reusable components within the topology system. Instead of requiring users to train their own models, the system copies and combines existing pre-trained nodes (text generation, image generation, etc.) to achieve custom functional designs. This approach maintains functional completeness while dramatically reducing the skill and cost requirements, as users simply need to configure connections between pre-existing models rather than creating new ones from scratch.
3Productivity
If multiple topology frames are generated automatically, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent implements an automated topology generation system that self-configures based on user input. When a user provides a prompt or selects a template, the system automatically generates appropriate topology frames, selects relevant nodes, establishes connections, and configures parameters without requiring manual intervention. This self-service capability dramatically improves productivity while managing system complexity through intelligent automation algorithms that handle the complexity internally rather than exposing it to users.
Data Source
AI summary
Based on user work product and underlying source data information, automatic generation of artificial intelligence (AI) topology defining operations for future AI generations each mimicking the approach and format of the user work product. Hosting services supports user interaction to gather work product examples and source data information, and also supports automatic identification of: (i) frames or elements of user work product; (ii) primary AI and support processing nodes to service content generation for each frame (or element); (iii) cross frame topology influence linkages; (iv) pattern data associated with each frame; and (v) AI and support processing based topologies for each frame. AI and support processing nodes being selected from a plurality of available nodes provided by the hosting service. Insufficient performance of such available nodes drives custom, automatic training of AI nodes and/or creation of support processing nodes with and without auto programming assist. During operations, user interactions trigger regeneration of all or certain frames and may inject temporary or permanent topology changes. User's underlying source data information and process of creating user's work product without AI assist can be monitored with gathered data from monitoring being used in the automatic generation of frame topologies and also used in future AI based generations according to such topologies to deliver generated output in the format of the user's work product and meeting the expectations of the user.