Metadata Process Mapping for Missing Contract Evidence
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Solution Overview
Problem
Proving a negative in contractual agreements is difficult due to the lack of definitive evidence, especially when corporate data is no longer accessible, leading to disputes over payments and profits.
Innovation Solution
A method involving accessing data elements within a data store, determining metadata values, and storing them to form predictive models based on metadata from multiple sources, allowing for improved data analysis and evidence tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If corporate data is stored and made accessible, then evidence for contractual outcomes can be reviewed, but when the company goes bankrupt or data is lost, the data becomes unavailable for proof
Solution Approach 1:
The system performs preliminary actions by continuously capturing and storing metadata about data elements, their access patterns, and relationships before the company goes bankrupt or data is lost. This metadata is preserved independently and can be used to reconstruct evidence even when original corporate data becomes unavailable.
Solution Approach 2:
The system creates copies of critical information by storing metadata about data elements, access patterns, and relationships in a separate, preserved data structure. This metadata copy serves as evidence of what happened with the original data, allowing reconstruction of contractual outcomes without needing the original corporate records.
2Measurement precision
If definitive documents are required to prove contractual outcomes, then legal evidence is strengthened, but proving a negative becomes extremely difficult when documents are unavailable
Solution Approach 1:
The system introduces metadata as an intermediary that mediates between the original data elements and the need for proof. This metadata captures information about data access, relationships, and patterns that serves as sufficient evidence without requiring the original definitive documents, thereby resolving the difficulty of proving negatives.
Solution Approach 2:
The system provides feedback by continuously monitoring and recording metadata about data elements and their access patterns. This feedback loop creates a permanent record of what happened with the data, enabling definitive proof of contractual outcomes even when original documents are lost or unavailable.
3Productivity
If comprehensive data is stored for analysis, then predictive modeling capability is improved, but data management complexity increases
Solution Approach 1:
The system extracts only the essential metadata information from comprehensive data sets, separating the critical patterns and relationships from the bulk data. This extracted metadata is what is actually stored and used for predictive modeling, reducing data management complexity while maintaining modeling capability.
Solution Approach 2:
The system segments data management by organizing metadata into distinct categories (data element metadata, access pattern metadata, relationship metadata) with separate storage and management mechanisms. This segmentation simplifies the overall data management complexity while enabling comprehensive predictive analysis.
Data Source
AI summary
A method is disclosed for analysing a data set to determine a first processes. Common elements within the data are identified and associated with the first processes. The common elements are mapped within the first processes to provide an estimated process flow for the first process. Another process is evaluated to determine an absence of one or more common elements common to the estimated process flow. A map is then provided of the process flow indicating events and documents forming the similar processes.