Micro Data Engine for Utility-Based Consumption
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Solution Overview
Problem
Current data procurement models for research institutions are inflexible and costly, requiring large upfront investments for entire data sets that may not align with changing research priorities, leading to wasted resources and inefficiencies in data usage.
Innovation Solution
A micro data engine system that allows for the selection and transmission of specific data subsets based on client needs, calculating data consumption in micro data units, and providing transparent pricing, thereby reducing unnecessary data transmission and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If researchers procure large volumes of data sets to ensure coverage for research studies, then data availability and completeness are improved, but data storage costs and processing time increase
Solution Approach 1:
The patent segments data sets into discrete micro data units that can be individually selected and procured based on specific research needs. This allows researchers to obtain only the necessary data portions rather than entire data sets, reducing storage costs and processing time while maintaining data availability for targeted research questions.
2Adaptability or versatility
If researchers purchase entire data sets to ensure future research flexibility, then adaptability to changing research priorities is improved, but upfront costs and resource waste increase
Solution Approach 1:
The patent implements a dynamic data procurement model where researchers can flexibly purchase additional micro data units as research priorities evolve. This dynamic approach replaces static upfront purchases of entire data sets, allowing institutions to adapt to changing research directions while paying only for actually used data, thereby reducing resource waste.
3Ease of operation
If data sets are provided in fixed formats with predetermined cohorts, then data procurement simplicity is improved, but adaptability to modified research questions deteriorates
Solution Approach 1:
By segmenting data into standardized micro data units with consistent metadata structures, the system maintains procurement simplicity through uniform interfaces while enabling flexible combination of units to address modified research questions. The standardized segmentation allows researchers to easily assemble customized data configurations without complex procurement processes.
Data Source
AI summary
Systems and method for utility consumption of data are enclosed. The system may include at least one memory and at least one processor. The at least one memory may store a plurality of data sets and one or more non-transitory computer-executable instructions. The at least one processor, in response to executing the one or more instructions, may implement a method or execute a micro data engine configured to implement a method. The method may include receiving a data request with data requirements from a client. The method may include arranging a product data set including a selection of the plurality of data sets based on the data requirements. The method may include calculating the number of micro data units in the product data set. The method may include transmitting the product data set to the client. The method may include transmitting an invoice to the client.