Multimodal Entity Aggregation via Embedding Indexing
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Solution Overview
Problem
Current methods for extracting value from unstructured data, such as images, video, audio, and text, are hindered by the need for specialized machine learning and data engineering talent, expensive infrastructure, and insufficient data for training models, making it difficult for businesses to leverage this data effectively.
Innovation Solution
A system that aggregates and evaluates multimodal, time-varying entities by using machine learning embedding models to create numeric vectors, allowing for the formation of entities from diverse data sources and enabling proximity searches, thereby making it accessible for average engineers and SQL users to deploy production use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning models are used to extract value from Natural Data, then data-value extraction capability is improved, but infrastructure cost and complexity increase
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms Natural Data into structured representations before downstream analysis. This intermediary layer handles the complexity of deep learning model management, allowing businesses to leverage advanced AI capabilities without directly managing the underlying infrastructure complexity.
Solution Approach 2:
The patent creates simplified copies or representations of complex data processing capabilities. By generating embeddings and structured data representations, the system provides accessible interfaces that replicate sophisticated ML functionality without requiring users to implement or manage the full deep learning infrastructure.
2Productivity
If deep learning models are deployed for Natural Data processing, then data analysis capability is improved, but operational cost increases
Solution Approach 1:
The patent performs preliminary processing of Natural Data by generating embeddings and structured representations in advance. This preprocessing step transforms unstructured data into optimized formats that reduce computational requirements for subsequent analysis, thereby lowering operational costs while maintaining high analytical capability.
3Productivity
If specialized ML and data engineering talent is hired, then model training capability is improved, but hiring cost and time increase
Solution Approach 1:
The patent enables organizations to self-serve advanced ML capabilities through automated embedding generation and structured data processing pipelines. By providing pre-built infrastructure and tools that require minimal specialized knowledge to operate, the system allows businesses to deploy production use cases without needing to hire expensive ML and data engineering talent.
4Reliability
If sufficient data is collected for model training, then model performance is improved, but data collection time and storage requirements increase
Solution Approach 1:
The patent transforms the parameters of raw Natural Data into embedding space representations. This parameter transformation converts high-dimensional unstructured data into optimized vector representations that capture essential features while reducing storage requirements and enabling faster processing, thus improving model performance without proportionally increasing data collection and storage overhead.
Data Source
AI summary
A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.