Computational Graph Value Attribution for Data Analytics Components
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Solution Overview
Problem
In data analytics systems, particularly those involving machine learning and artificial intelligence, the existing approaches to determining the economic value of components are often uncertain and do not accurately align costs with the marginal benefits, leading to suboptimal resource allocation and reduced buyer demand due to the risk being borne primarily by buyers.
Innovation Solution
A computer-implemented method that represents a data analytics-driven system as a computational graph, calculates usage and utility metrics for each component, and allocates value based on these metrics using a node-specific value decomposition algorithm or machine learning algorithms to determine the expected marginal utility of components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pricing approaches based on system usage or component usage metrics are used, then the pricing structure is simple and easy to implement, but the value attribution is inaccurate and does not reflect the true marginal benefit of components
Solution Approach 1:
The system segments the value attribution process into distinct components: computational graph construction, usage metric calculation, utility metric calculation, and value decomposition. Each component handles a specific aspect of the valuation problem, allowing complex value attribution to be broken down into manageable, modular steps that can be implemented and maintained separately.
Solution Approach 2:
The patent introduces computational graphs as an intermediary structure that models the relationships between system components and their outputs. This intermediary representation enables the system to trace and quantify the contribution of each component to the final output, serving as a bridge between raw usage data and accurate value attribution.
2Ease of operation
If buyers bear the primary risk in component purchases, then sellers can maintain stable pricing, but buyer demand is reduced due to uncertainty about marginal benefits
Solution Approach 1:
The system implements feedback loops where usage metrics and utility metrics are continuously calculated and used to refine value attributions. This feedback mechanism allows the system to learn from actual component performance and adjust pricing and risk allocation accordingly, reducing uncertainty for buyers while maintaining reliability for sellers.
Solution Approach 2:
The patent changes the parameters used for pricing from simple usage metrics to a composite of usage metrics and utility metrics. This parameter transformation enables more accurate reflection of marginal benefits, allowing buyers to make informed decisions about component purchases with reduced risk.
3Measurement precision
If value is determined after system outputs are observed, then accurate business value can be measured, but the timing mismatch creates uncertainty for component selection and pricing negotiations
Solution Approach 1:
The system performs preliminary calculations of usage metrics and utility metrics before final value determination. By pre-calculating component contributions to system outputs, the system enables earlier value assessments that can inform component selection and pricing negotiations without sacrificing measurement accuracy.
Solution Approach 2:
The patent implements a dynamic valuation system where value attributions can be updated in real-time as new data becomes available. This dynamic approach allows the system to adapt to changing conditions and provide timely value assessments that reflect current system performance rather than relying on historical data alone.
Data Source
AI summary
Values are attributed to components of a data analytics-driven system by representing the system as a computational graph. The computational graph embodies a function that takes one or more inputs and produces an output, and each component of the system is represented as a subgraph of the computational graph. A usage metric is calculated for each component of the system by determining whether the output of the function of the system is affected by the component. A utility metric is also calculated for each component of the system. Based on the calculated component usage metrics and utility metrics, respective value are allocated to the system components.