Merchant Industry Mapping via Ambient Noise Analysis
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Solution Overview
Problem
In the payment industry, merchant-industry mapping is inaccurate for small or non-aggregated merchants due to data inaccuracy, which hinders effective data analytics and service growth.
Innovation Solution
A method and system utilizing ambient noise recorded by merchant terminals to determine the type of merchant and map it to an industry, employing machine learning and artificial intelligence algorithms to process audio signals and update databases for accurate merchant-industry mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection methods are used for merchant information, then existing database structures can be maintained, but merchant-industry mapping accuracy deteriorates for small/non-aggregated merchants
Solution Approach 1:
The patent replaces traditional mechanical/data-entry-based merchant classification systems with acoustic field-based detection. Merchant terminals capture ambient noise patterns, and server systems process these audio signals to automatically determine merchant types and industries, eliminating the need for manual data entry and improving accuracy for small merchants who traditionally lack reliable classification data.
Solution Approach 2:
The patent introduces ambient noise as an intermediary indicator to indirectly determine merchant industry classification. Instead of directly collecting inaccurate merchant self-declared data, the system uses ambient sound patterns (music, conversations, machinery noises) as a mediator to infer merchant type, providing more reliable classification for small businesses.
2Measurement precision
If manual verification processes are used for merchant data, then data accuracy can be maintained for large merchants, but processing time and operational complexity increase
Solution Approach 1:
The patent enables merchants to self-identify their industry classification through ambient noise patterns captured at their premises. The system automatically processes the audio data and assigns merchant types without requiring manual verification or intervention, significantly reducing processing time while maintaining high accuracy through acoustic pattern recognition.
Solution Approach 2:
The system performs preliminary classification of merchant types based on ambient noise patterns captured during payment transactions. This preliminary action occurs automatically in the background, pre-classifying merchants before any manual verification would be needed, thus eliminating time-consuming manual processes while maintaining accuracy.
3Reliability
If existing database structures are used for merchant information, then system compatibility is maintained, but the ability to detect and correct data anomalies is limited
Solution Approach 1:
The patent implements a feedback mechanism where ambient noise data continuously updates merchant classification information in the database. The system processes new acoustic patterns and adjusts existing merchant records or creates new classifications, providing ongoing feedback that improves data reliability and enables automatic detection of anomalies in merchant industry mapping.
Data Source
AI summary
Embodiments provide a method and a system for conducting merchant-industry mapping based on ambient noise. The method includes receiving a payment transaction request and an audio signal comprising ambient noise in surrounding of a merchant terminal of a merchant. The ambient noise is recorded by the merchant terminal and includes a plurality of sounds. The method includes determining whether the merchant is an aggregated merchant or a non-aggregated merchant. The method includes processing the ambient noise to determine a type of the merchant upon determining that the merchant is the non-aggregated merchant. The method includes mapping the merchant to an industry from a plurality of industries available in the server system based on the type of the merchant. The method further includes storing the mapping of the industry and the merchant in a database associated with the server system.