Transaction Classification with Reliability Codes

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Solution Overview

Problem

Existing accounting systems either require manual transaction classification, which can be inaccurate due to human error, or rely on automatic classification that fails to correct mistakes and adapt to new entries.

Innovation Solution

A computer-implemented method for categorizing transactions using a set of reliability codes and transaction categories, which automatically associates categories based on user-defined rules and crowd-sourced data, displaying transactions with reliability codes to indicate accuracy and allowing users to review and correct classifications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual classification of transactions is used, then accuracy of transaction classification is improved, but time consumption and labor requirements increase

Engineering Contradiction:

Improveaccuracy of transaction classificationVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments transaction classification into different reliability levels (high reliability automatic classification, medium reliability requiring review, low reliability requiring manual classification). This segmentation allows the system to maintain high accuracy for automatic classifications while efficiently handling only the necessary manual reviews for uncertain transactions, thus reducing overall time consumption.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a feedback mechanism where users can review and correct automatic classifications, and these corrections are fed back into the system to improve future automatic classifications. This feedback loop maintains high accuracy while reducing the need for complete manual review of all transactions.

Inventive Principle:

Principle #23Feedback

2Productivity

If automatic classification systems are used, then productivity and speed of transaction processing are improved, but reliability and accuracy of classification deteriorate

Engineering Contradiction:

Improvespeed of transaction processingVSAvoidreliability of transaction classification

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent makes the classification system dynamic by adjusting the level of automation based on transaction characteristics and reliability scores. High reliability transactions are automatically classified, while low reliability transactions trigger manual review. This dynamic approach maintains high productivity for automatic classifications while ensuring reliability through selective manual verification.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms where users can correct misclassifications, and these corrections are used to train and improve the automatic classification algorithms. This feedback loop continuously enhances the reliability of automatic classification while maintaining high processing speed.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If automatic classification systems are used, then ease of operation is improved, but adaptability to new and varying transactions deteriorates

Engineering Contradiction:

Improveease of transaction classificationVSAvoidadaptability to new transactions

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent implements self-service through automatic classification that handles the majority of transactions without human intervention. The system automatically learns from user corrections and improves its classification capabilities over time, maintaining ease of operation while developing adaptability to new transaction types through continuous learning from user feedback.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adapts to new transactions by monitoring user corrections and adjusting its classification algorithms accordingly. This dynamic learning process maintains ease of operation for automatic classification while improving adaptability to novel transaction types through continuous refinement based on user input.

Inventive Principle:

Principle #15Dynamics

4Measurement precision

If manual review of all transactions is performed, then accuracy of classification is improved, but device complexity and system complexity increase

Engineering Contradiction:

Improveaccuracy of transaction classificationVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the review process by classifying transactions into different reliability categories. Only transactions with low reliability scores require manual review, while high reliability transactions are processed automatically. This segmentation dramatically reduces the complexity of manual review processes while maintaining high overall accuracy.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS8725586B2Accounting system and management methods of transaction classifications that is simple, accurate and self-adapting

Publication Date: 2014.05.13 LEVIN DOUGLAS

AI summary

A computer implemented method for categorizing transactions for a user comprising receiving a plurality of transactions, each transaction including a transaction party, a date associated with the transaction, and a transaction amount, providing a set of transaction categories, automatically associating one of the transaction categories with at least one of the received transactions from the set of transaction categories, providing a set of reliability codes comprising at least two reliability codes, selecting a reliability code from the set of reliability codes for at least one of the automatically selected transaction categories, and displaying at least one of the received transactions with its selected reliability code.