Itinerary Estimation via Expense Data Chronological Classification
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Solution Overview
Problem
Existing systems for managing and estimating business trip expenses and personal itineraries are inefficient, requiring manual input and classification of expense data, which can be time-consuming and prone to errors.
Innovation Solution
An itinerary estimation device that classifies expense data into groups based on chronological order and location information, using OCR to extract relevant details, and estimates itineraries by analyzing the continuity of date and location data from multiple documents, with processes for correction, elimination, and supplementation to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input and classification of expense data is used, then user control and data accuracy can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system automatically extracts date and location information from expense data and performs chronological classification without requiring manual user input. The itinerary estimation device processes expense data autonomously, reducing time consumption while maintaining accuracy through automated classification algorithms.
Solution Approach 2:
The patent replaces manual mechanical classification operations with automated information processing systems. Optical character recognition (OCR) technology converts image data into structured information, and algorithms automatically classify expense data chronologically, eliminating the need for manual data entry and classification.
2Measurement precision
If manual classification of expense data is performed, then data processing accuracy can be maintained, but device complexity and operational difficulty increase
Solution Approach 1:
The system segments expense data processing into distinct automated stages: OCR extraction of date and location information, chronological sorting of extracted data, and itinerary generation from classified groups. This segmentation simplifies the overall system by breaking down complex manual classification into manageable automated components.
Solution Approach 2:
The patent introduces an intermediary classification unit that acts as a bridge between raw expense data and final itinerary output. This unit automatically performs chronological classification and generates structured expense data groups, simplifying the interface between data input and itinerary estimation without requiring complex manual intervention.
3Productivity
If automated OCR extraction is used, then processing speed and productivity increase, but measurement precision and data reliability may decrease
Solution Approach 1:
The system incorporates feedback mechanisms where extracted date and location information is validated against chronological consistency and logical relationships. The classification unit verifies that extracted data forms coherent temporal sequences, and the estimation unit cross-checks extracted information against known itinerary patterns, improving extraction reliability while maintaining high processing speed.
Solution Approach 2:
The patent performs preliminary validation and correction of extracted information before final itinerary generation. The classification unit pre-processes extracted data by identifying and correcting obvious errors, filling in missing information based on contextual clues, and ensuring chronological consistency before passing data to the estimation unit, thereby maintaining both speed and accuracy.
Data Source
AI summary
An itinerary estimation device includes a classification unit and an estimation unit. The classification unit classifies multiple pieces of expense data including information related to expenses into one or more expense data groups, according to a chronological order of date information extracted from each of the multiple pieces of expense data. The estimation unit estimates an itinerary that includes at least dates on the basis of information including date information extracted from each piece of classified expense data included in the expense data group.