Vector Embedding System for Privacy-Preserving Content Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative AI systems struggle to effectively utilize first-party or proprietary data for digital content generation due to security concerns and the challenge of making extracted data intelligible, often resulting in hallucinations or context-unaware content.
Innovation Solution
The system employs vectorized embeddings of first-party data and machine learning to identify similar digital content items, using a first ML model to compare user prompts with vector databases, and selectively inputs these to generative ML models to generate sourced or unsourced digital content items based on contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative AI models are trained on first-party or proprietary data, then the generated content becomes more accurate and contextually relevant, but data privacy and security concerns are violated
Solution Approach 1:
The patent introduces an intermediary vector database that stores embeddings of first-party data without the actual data itself. The generative AI model queries this vector database for relevant information rather than being trained on the raw data, thus maintaining accuracy while preserving privacy. The vector database acts as a mediator between the private data and the AI model.
Solution Approach 2:
The patent extracts only the essential information from first-party data by converting it into vector embeddings and storing these in a vector database. This extraction allows the generative AI model to access and utilize the information without having access to or training on the original sensitive data, thereby resolving the privacy-accuracy contradiction.
2Productivity
If search engines are used to extract data from first-party data, then data retrieval is possible, but the extracted data is not easily intelligible to users
Solution Approach 1:
The patent transforms the first-party data into vector embeddings, which are mathematical representations that capture the semantic meaning of the data. This parameter transformation makes the data both retrievable through vector similarity search and intelligible when processed by the generative AI model, which can present the information in user-friendly formats.
Solution Approach 2:
The patent replaces traditional search engine mechanisms with a vector-based embedding system. Instead of relying on keyword matching and indexing that produces raw data extracts, the system uses vector similarity search to retrieve semantically relevant information, which is then processed by the generative AI model to produce intelligible responses.
3Productivity
If generative AI models are trained on improper or unrelated data, then training can proceed, but the generated content results in hallucinations or context-unaware output
Solution Approach 1:
The patent implements a feedback mechanism where the system queries the vector database based on the user's prompt and uses the retrieved relevant information to guide the generative AI model's output. This feedback loop ensures that the model generates accurate, context-aware content by continuously referencing the actual first-party data stored in the vector database, preventing hallucinations.
Solution Approach 2:
The patent performs preliminary action by pre-processing and storing first-party data as vector embeddings in the vector database before the generative AI model needs to generate content. This preliminary preparation ensures that when content generation is needed, the model can quickly retrieve and utilize relevant information from the database, ensuring high-quality output without requiring retraining.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent digital content generation using first-party data. For example, a system may extract features from items of first-party data to generate a vector table of vectorized embeddings. A user prompt may be vectorized and input to a machine learning (ML) model to generate a result vector that is a similar to the prompt vector in a latent space representation of the vector database. The system may compare the result vector to the prompt vector, and based on a result of the comparison, output either a sourced digital content item or an unsourced digital content item. The sourced digital content item may be generated by generative ML model(s) based on the result vector, and the unsourced digital content item may be generated by generative ML model(s) based on a custom prompt derived from the prompt vector.