Multi-Tenant Dataset Stratification for Reliable SSP Resolution
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Solution Overview
Problem
In multi-tenant systems, evaluating subscription data for compliance with accounting standards like ASC 606 and IFRS 15 is time-consuming and unreliable due to the complexity and size of datasets, particularly in determining Standalone Selling Price (SSP), which often requires manual trial and error and is infeasible using current computing processes.
Innovation Solution
A stratification system within a multi-tenant system that efficiently stratifies datasets based on different attributes, using candidate stratification mechanisms tested via an AI algorithm like a genetic algorithm, to resolve conforming SSPs by normalizing and testing different keys, selecting appropriate stratification mechanisms, and removing outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial and error is used to evaluate subscription data for compliance, then flexibility in handling complex accounting standards is maintained, but the process becomes time-consuming and unreliable
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with an automated computer-based system that applies accounting standards (ASC 606 and IFRS 15) algorithmically. The system automatically determines Standalone Selling Prices by evaluating subscription data against defined criteria, eliminating manual trial and error while maintaining compliance reliability through consistent rule-based processing.
Solution Approach 2:
The system enables self-service evaluation by automatically processing subscription data without requiring manual intervention. The automated process independently evaluates compliance, determines SSPs, and generates results, allowing the system to serve itself in performing the evaluation function that previously required human analysts.
2Extent of automation
If current computing processes are used to determine SSP, then automation is achieved, but the processes are infeasible for complex datasets requiring sophisticated analysis
Solution Approach 1:
The patent segments the complex dataset into manageable components by stratifying transactions based on key characteristics such as product type, customer segment, or geographic region. This segmentation allows the automated system to process complex data by breaking it into smaller strata that can be evaluated independently, then aggregated to determine overall SSPs while maintaining sophisticated analysis capability.
Solution Approach 2:
The system adds the dimension of stratification to the analysis, organizing data not just by individual transactions but by grouped strata with defined characteristics. This dimensional change enables the automated process to handle complexity by introducing hierarchical organization, allowing sophisticated analysis through multiple levels of data aggregation and evaluation.
3Productivity
If datasets are not stratified, then the original dataset structure is preserved, but evaluation of conforming SSPs becomes inefficient due to mixed event data
Solution Approach 1:
The patent applies segmentation by stratifying the dataset into distinct groups based on shared characteristics. Each stratum contains homogeneous data suitable for specific SSP evaluation, improving productivity by allowing targeted analysis. The system manages the resulting structural complexity through automated stratification logic that organizes data according to predefined criteria relevant to accounting standard compliance.
Data Source
AI summary
A multi-tenant system includes hardware processors that ingest a dataset including transaction records, each of the transaction records including a field value and attribute values of different attributes. The hardware processors test candidate stratification mechanisms on the dataset, each candidate stratification mechanism stratifying the dataset according to a different attribute. The hardware processors determine a test value from field values within each stratification of each candidate stratification mechanism. The hardware processors evaluate each candidate stratification mechanism based on metrics following implementation of the candidate stratification mechanism on the dataset. The one or more metrics indicate a proportion of conforming field values that are within a threshold range of a corresponding test value. The hardware processors select a particular stratification mechanism from the candidate stratification mechanisms based on the one or more metrics, stratify the dataset according to the particular stratification mechanism, and resolve conforming values from the stratified dataset.