Trip Purpose Prediction Model for Faster Tax Trip Labeling

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Solution Overview

Problem

Manually labeling trip purposes for tax deductions is a time-consuming and error-prone process, especially for individuals with numerous trips, leading to potential loss of significant tax savings.

Innovation Solution

A method using a topic prediction model to automatically recommend trip purposes based on travel data, involving pre-processing, building a topic model, and training an ensemble classifier to predict trip purposes, which are then presented to users for review and confirmation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual labeling of trip purposes is performed, then accuracy of trip purpose classification can be maintained, but time consumption and labor effort increase significantly

Engineering Contradiction:

Improvetrip purpose classification accuracyVSAvoidtime for labeling trips

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-labeling of trip purposes through machine learning models that analyze trip data patterns, location information, and temporal characteristics to autonomously classify trips without requiring manual user input for each trip

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual labeling process with an automated electronic system using ensemble classifiers, topic models, and natural language processing to perform trip purpose classification, eliminating the need for manual human intervention in the labeling process

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual labeling of numerous trips is performed, then complete trip purpose tracking can be achieved, but error rate increases due to human fatigue and mistakes

Engineering Contradiction:

Improvetrip purpose tracking completenessVSAvoidtrip purpose classification accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The system incorporates feedback mechanisms where user corrections and confirmations of predicted trip purposes are used to retrain and improve the machine learning models, creating a continuous improvement loop that increases both reliability and accuracy over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary automated classification system that acts as a bridge between raw trip data and final trip purpose labels, reducing the cognitive load on users and minimizing errors by pre-processing and structuring information before human review

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If automated prediction models are used, then time efficiency and productivity improve, but system complexity increases

Engineering Contradiction:

Improvetrip labeling speedVSAvoidprediction system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the trip purpose prediction system into distinct modular components including topic model generation, feature extraction modules, ensemble classifier integration, and prediction output stages, allowing each component to be developed, tested, and maintained independently

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal ensemble classifier architectures and reusable feature extraction functions that can handle multiple types of trip data and purposes, reducing overall system complexity by avoiding the need for separate specialized models for each trip type

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If comprehensive trip data analysis is performed, then prediction accuracy improves, but computational resources and processing time increase

Engineering Contradiction:

Improvetrip purpose prediction accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements partial action by analyzing only the most relevant features and data points for each trip prediction rather than processing all available data, using techniques like feature selection and prioritization to achieve good accuracy with reduced computational overhead

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary actions by pre-computing trip features, pre-processing trip data, and generating topic models in advance before actual prediction is needed, reducing the computational burden during real-time prediction operations

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12524712B2Method for predicting trip purposes based on input topics utilizing a purpose prediction model

Publication Date: 2026.01.13 INTUIT INC

AI summary

Certain aspects of the present disclosure provide techniques for recommending trip purposes to users of an application. Embodiments include receiving labeled travel data from the application running on a remote device including a plurality of trip purposes. Embodiments include building a topic model representing words associated with a plurality of topics. Embodiments include training a topic prediction model, using the plurality of topics and one or more features derived from each of the plurality of trip records, to output a topic based on an input trip record. Embodiments include training a purpose prediction model, using the topic model and the plurality of trip purposes, to output a trip purpose based on an input topic. The trip purpose may be recommended to a user via a user interface of the application running on the remote device.