Cohort Time Resource Allocation Analysis
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Solution Overview
Problem
Businesses face challenges in accurately allocating resources over time to achieve specified outcomes due to the complexity of managing limited resources and the difficulty in analyzing transactional information to determine trends in defined populations, leading to overspending or underutilization of resources.
Innovation Solution
A method and system that analyze resource allocation by identifying Unit of Analysis Identifiers, grouping them by Type, forming Cohort time segments based on Start Time, adjusting and standardizing Variable Values, calculating eligibility scores, and generating Output Expressions to provide usable estimates for resource allocation decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If businesses use traditional calendar time analysis for resource allocation, then resource allocation decisions can be made using available data, but the analysis fails to account for population heterogeneity and experience time variations leading to suboptimal allocation
Solution Approach 1:
The patent segments the population into distinct cohorts based on their entry time or experience start time. Each cohort is analyzed separately to capture heterogeneity in experience and resource needs. This segmentation allows for more precise resource allocation by accounting for different stages of experience rather than treating the entire population uniformly across calendar time periods.
Solution Approach 2:
The patent introduces a new dimension of analysis by transforming traditional calendar time analysis into experience time analysis. Instead of measuring resource allocation solely by calendar dates, the system adds the dimension of individual experience duration and cohort membership, enabling more accurate predictions of resource needs based on actual population experience rather than chronological time alone.
2Loss of information
If businesses collect and store detailed transactional information for analysis, then more comprehensive data is available for decision-making, but the task of organizing and analyzing this information becomes extensive and troublesome
Solution Approach 1:
The patent extracts key relevant features from detailed transactional information and stores them in a standardized format suitable for analysis. Rather than attempting to organize and analyze all raw transactional data, the system extracts essential elements such as entry time, experience duration, and cohort identifiers, making the data manageable while preserving the information needed for accurate resource allocation decisions.
Solution Approach 2:
The patent introduces an intermediary data structure that sits between raw transactional information and analysis tools. This intermediary format organizes data into standardized cohorts with pre-calculated experience metrics, serving as a bridge that simplifies the analysis process while maintaining fidelity to the original transactional data. This intermediary layer eliminates the need for complex ad-hoc organization of raw data for each analysis task.
3Productivity
If businesses allocate resources based on calendar time periods, then resource allocation aligns with financial reporting periods, but the allocation does not account for population experience heterogeneity leading to overspending or underutilization
Solution Approach 1:
The patent performs preliminary organization of transactional data into standardized cohorts and experience time segments in advance. By pre-processing the data into an analysis-ready format with calculated experience metrics and cohort assignments, the system eliminates the need for extensive data organization at the time of analysis. This preliminary action enables rapid, efficient resource allocation decisions that account for population heterogeneity without time-consuming data preparation.
Data Source
AI summary
The present invention is a method for improving resource allocation comprising the steps of identifying at least one criteria; Identifying sets of information wherein each set of information includes a unique unit of analysis (UOA-ID), a calendar/clock time (CCT), a CATVAR and a VAR Value; grouping each UOA-ID into an appropriate specific population (Type); identifying a Start Time wherein each UOA-ID has met said at least one criteria; forming at least one prospective or retrospective Cohort time segment for each UOA-ID based on their Start Time; placing the UOA-ID into the appropriate time segment; calculating an eligibility score for each UOA-ID for each time segment; calculating an Eligible Adjusted Variable Value; and generating at least one Output Expression.