ML Tax Expense Recommendation System
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Solution Overview
Problem
Tax preparation software for self-employed individuals generates lists of tax deductible expenses based on industry-specific rules, failing to account for individual-specific details like income, marital status, and life events, leading to incomplete recommendations and resource-intensive maintenance.
Innovation Solution
A machine learning model is trained using datasets containing user interactions, industry information, and contextual features to provide personalized recommendations for tax deductible expenses, with feedback loops for continuous model improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rules-based approach is used to generate tax deductible expense lists, then the system is easy to implement initially, but it fails to account for individual-specific details and requires resource-intensive maintenance
Solution Approach 1:
The patent replaces the mechanical rules-based system with a machine learning model that automatically learns and adapts to individual user characteristics. The ML model processes multiple user features (income, industry, life events) simultaneously to generate personalized recommendations, eliminating the need for complex rule maintenance while improving adaptability.
Solution Approach 2:
The system transitions from static rules to dynamic parameter-based recommendations. The ML model takes multiple input parameters (user features) and generates optimized recommendations based on learned patterns from training data, allowing the system to adapt to individual circumstances without manual rule updates.
2Device complexity
If rules-based approach is used, then the system structure is simple, but it cannot generate comprehensive recommendations for all user situations
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it processes diverse user features, identifies relevant expense categories, and generates personalized recommendations. This single multi-functional model replaces numerous specialized rules, improving recommendation completeness while managing complexity through unified architecture.
Solution Approach 2:
The system uses training data from past user interactions to create a model that copies successful recommendation patterns. By learning from historical data across multiple users, the model captures comprehensive expense patterns that would be difficult to encode in rules, improving reliability without proportionally increasing complexity.
3Measurement precision
If multiple models are used to handle different user details, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges multiple recommendation functions into a single machine learning model that processes all user features simultaneously. This unified model learns interactions between different features (income, industry, life events) and generates comprehensive recommendations, achieving high accuracy while avoiding the complexity of managing multiple separate models.
Data Source
AI summary
A method for automatically recommending to a user of a software application one or more categories of a plurality of different categories of tax deductible expenses includes providing input data to a trained machine learning model and receiving output from the trained machine learning model based on the input data. The output includes a recommendation for the user that includes (i) one or more categories of the plurality of different categories of tax deductible expenses; and (ii) a plurality of examples of tax deductible expenses for each of the one or more categories. The method includes receiving feedback from the user on the recommendation and generating updated training data for the trained machine learning model based on the feedback.