Automated Purchase Categorization Bot Using Merchant Data Analysis
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Solution Overview
Problem
Consumers face difficulties in analyzing their spending habits due to the lack of detailed categorization of purchases from merchants that sell multiple categories of items, as transaction information often lacks category descriptions, resulting in purchases being grouped under an 'Other' category, which is not helpful in understanding spending patterns.
Innovation Solution
A bot is used to automatically categorize consumer purchases across multiple merchants by capturing electronic transaction data, including category codes, descriptions, or identifiers, and grouping them into specific categories, providing detailed categorization and percentage analysis of spending by category.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transaction information is provided only with basic merchant details without category descriptions, then the data collection process remains simple, but the categorization precision deteriorates causing purchases to be grouped under generic 'Other' categories
Solution Approach 1:
The patent introduces a bot as an intermediary component that sits between the transaction data source and the consumer. This bot automatically captures transaction information, extracts category details using machine learning models, and processes the data to generate categorized spending reports. The intermediary handles the complexity of categorization internally, providing precise category information to consumers without requiring them to directly manage the complex data processing infrastructure.
Solution Approach 2:
The patent replaces manual categorization methods with automated machine learning models. Instead of requiring consumers to manually review and categorize each transaction, or relying on simple rule-based systems, the invention uses trained machine learning models to automatically analyze transaction data and assign accurate category labels. This substitution of mechanical/manual processes with intelligent automation resolves the contradiction by achieving high categorization precision while keeping the consumer-facing system simple.
2Measurement precision
If manual review of transaction data is performed to achieve accurate categorization, then categorization precision improves, but the time consumption and effort increase significantly
Solution Approach 1:
The patent implements a self-service system where the bot autonomously performs the entire categorization process without requiring consumer intervention. The system automatically captures transaction data, applies machine learning models for category classification, and generates spending analysis reports. Consumers simply receive ready-to-analyze categorized data, eliminating the time and effort previously required for manual review while maintaining high categorization precision through intelligent automation.
Solution Approach 2:
The patent performs categorization actions in advance, automatically processing transaction data as it becomes available rather than waiting for consumers to manually review it later. The bot continuously captures and categorizes transactions in real-time or near-real-time, preparing the spending analysis data beforehand so consumers can immediately access accurate category information without investing time in the analysis process.
3Loss of information
If detailed item-level categorization is implemented for all purchases, then the understanding of spending habits improves, but the complexity of data processing and storage increases
Solution Approach 1:
The patent segments the complex task of transaction analysis into distinct components: transaction data capture, category extraction using machine learning, category validation, and report generation. The bot divides the processing workload into manageable segments, handling each aspect separately. This segmentation allows the system to maintain detailed item-level categorization information while managing complexity through modular processing steps, where each segment handles a specific aspect of the data pipeline independently.
4Productivity
If automated categorization systems are implemented, then productivity in analyzing spending habits improves, but the initial setup and model training complexity increases
Solution Approach 1:
The patent employs machine learning models with adjustable parameters that can be trained on different datasets and customized for specific consumer needs. The system allows for parameter optimization and model fine-tuning to improve categorization accuracy for different spending patterns and merchant types. By making the model parameters adjustable and trainability a core feature, the system achieves high productivity in spending analysis while managing complexity through configurable parameters rather than fixed, hard-coded logic.
Data Source
AI summary
A bot automatically compiles transaction information for a consumer from merchants that sell products in various categories and merchants that sell products in only a single category. The bot categorizes the consumer's purchases from the merchants and determines percentages of consumer spending in each category. The bot may obtain a category for purchases from merchants selling a single category of items based on a merchant identifier such as a merchant code. For other merchants, the bot may obtain category information from the merchant or from an analysis of a category code, or a product or service description or identifier. The categories and percentages of the consumer's purchases may be provided by the bot to the consumer or to a third party. The bot may provide special offers and promotions to a consumer based on the compiled category information and percentage spending by the consumer by category.