Granular Data Usage Classification and Cost Allocation
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Solution Overview
Problem
Current systems lack the ability to effectively monitor, classify, and allocate costs for data usage transactions, leading to difficulties in managing and optimizing data usage, particularly in distinguishing between personal and business usage, which results in increased costs and limited visibility for enterprises.
Innovation Solution
A system and method that analyze data transactions to classify usage into granular categories, using pattern recognition and resource consumption analysis, allowing for cost allocation and reporting based on detailed usage events, enabling effective management and optimization of data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data usage monitoring is implemented at aggregate level only, then system complexity is reduced, but measurement precision and cost allocation accuracy deteriorate
Solution Approach 1:
The patent segments data usage monitoring into multiple levels: aggregate level for high-level oversight and transaction level for detailed measurement. This segmentation allows the system to maintain low complexity at the aggregate level while achieving high precision at the transaction level through selective detailed tracking of individual data events, applications, and users.
Solution Approach 2:
The patent adds a temporal dimension to data usage monitoring by implementing both real-time transaction-level tracking and periodic aggregate reporting. This multi-dimensional approach allows simultaneous view of detailed individual transactions and summarized overall usage patterns, resolving the contradiction between complexity and precision.
2Measurement precision
If transaction level data tracking is implemented, then cost allocation accuracy is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent applies local quality by implementing detailed transaction-level tracking only where needed for cost allocation purposes, rather than uniformly across all data usage. The system selectively monitors transactions requiring cost attribution while using aggregate monitoring for other purposes, optimizing the balance between accuracy and complexity.
Solution Approach 2:
The patent introduces an intermediary data processing layer that sits between raw data usage events and cost allocation systems. This intermediary layer aggregates, filters, and processes transaction data before passing it to cost allocation functions, reducing the complexity burden on both the monitoring and cost management systems while maintaining accuracy.
3Productivity
If detailed usage classification is implemented, then productivity assessment capability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements preliminary action by pre-defining usage categories, classification rules, and measurement criteria before data collection begins. This preparation work includes establishing taxonomies for application types, user roles, and usage patterns, which simplifies the actual detection and measuring process while enabling comprehensive productivity assessment.
Solution Approach 2:
The patent enables self-service by implementing automated classification systems that use metadata, application identifiers, and usage patterns to automatically categorize data transactions without manual intervention. This automation reduces the difficulty of detecting and measuring usage while maintaining detailed classification for productivity assessment.
Data Source
AI summary
A system and method that allows for information relating to data and communication resource usage to be gathered and analyzed such that particular data transactions and usage can be classified based on purpose and/or type. Further, the system and method provide reporting based on amount of usage and/or purpose or type of usage so that associated costs and usage can be calculated applied and allocated to particular accounts, divisions, groups or individuals within and outside of a company or entity. Further, the system may restrict data usage of devices to data usage that can be allocated to particular accounts based on purpose, source, destination or other.