Record Type Prediction via Normalized Value Segmentation
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Solution Overview
Problem
Current data processing techniques lack effective methods for predicting the type of records based on value fields, especially in large datasets like expense reports, which hinders efficient data analysis and management.
Innovation Solution
A system that normalizes values, divides records into groups based on secondary field values, and generates prediction functions using descriptive statistics or probability density functions, such as kernel density estimation, to determine the type of records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing techniques are used to analyze large datasets, then data analysis can be performed, but the efficiency and accuracy of predicting record types is insufficient
Solution Approach 1:
The patent segments the dataset into multiple groups based on secondary field values (e.g., expense categories, locations, time periods). This segmentation allows the system to analyze each group separately using descriptive statistics and probability density functions, improving prediction accuracy for each segment while maintaining overall processing efficiency through parallelization.
Solution Approach 2:
The patent transforms the raw data by normalizing amount values and calculating descriptive statistics (mean, median, mode) and probability density functions for each group. These parameter transformations convert raw financial data into statistical features that enable accurate record type prediction while reducing the complexity of direct analysis.
2Productivity
If manual classification methods are used for record types, then accuracy can be maintained, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-calculating descriptive statistics and probability density functions for each data group during off-peak times or as part of data ingestion. This preliminary processing creates ready-to-use statistical models that enable rapid prediction of record types during actual data analysis, significantly reducing classification time without sacrificing accuracy.
Solution Approach 2:
The patent replaces manual classification mechanisms with automated statistical prediction functions. Instead of relying on manual review or simple rule-based systems, the invention uses probability density functions and descriptive statistics to automatically predict record types, achieving both high speed and high accuracy in classification.
3Measurement precision
If complex statistical methods are applied to all records, then prediction accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent applies complex statistical methods selectively to segmented groups rather than to all records uniformly. By dividing the dataset into meaningful groups based on secondary fields, the system can apply sophisticated probability density function calculations only where needed, reducing overall computational complexity while maintaining high prediction accuracy for each segment.
Solution Approach 2:
The patent implements local quality by applying different statistical analysis depths to different data groups. Some groups may require full probability density function analysis while others can use simpler descriptive statistics. This localized approach optimizes resource allocation and reduces system complexity while maintaining high accuracy where it matters most.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program queries a database for a subset of a plurality of records in the database. Each record in the plurality of records includes a value for a first field and a second value for a second field. The program further normalizes the first value of the first field of each record in the subset of the plurality of records. The program also divides the subset of the plurality of records into a plurality of groups of records based on the second values of the second field. The program further generates a function for predicting a type of a particular record based on the value of the field of the particular record.