Spending Data Aggregation System for Business Comparison
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Solution Overview
Problem
Business entities face challenges in comparing their spending transactions with similar entities due to inadequate average business spending data, privacy concerns, and the inability to compare by category or attribute, as existing methods either lack categorized data or raise proprietary information issues.
Innovation Solution
A system and method for comparing spending transactions of a target business entity by category or attribute with other entities, using a central server that aggregates and categorizes spending data, allowing selective comparison and generating reports based on aggregated amounts, while maintaining privacy through localized aggregation and secure data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If average business spending data from market researchers is used, then businesses can obtain spending information, but the data lacks category information and does not provide meaningful comparison by attributes
Solution Approach 1:
The patent segments spending data by categorizing transactions into different categories (e.g., office supplies, marketing, utilities) and allows businesses to compare spending by specific categories or combinations of categories. This segmentation enables detailed attribute-based comparison while maintaining manageable data structures through hierarchical categorization.
Solution Approach 2:
The patent adds categorical dimensions to spending data, transforming one-dimensional total spending data into multi-dimensional data structured by category, time period, and other attributes. This dimensional expansion enables comprehensive comparison across multiple criteria simultaneously.
2Ease of operation
If a central database storing spending transactions of many business entities is provided, then businesses can compare spending, but privacy and proprietary information concerns arise
Solution Approach 1:
The patent extracts only the necessary aggregated spending data from individual business transactions, removing sensitive proprietary information while retaining comparison value. Businesses can compare spending patterns without exposing detailed transaction records, achieving privacy protection through selective data extraction and aggregation.
Solution Approach 2:
The patent merges spending data from multiple businesses into aggregated category-level statistics, combining individual data points into collective insights. This merging process protects individual business privacy while enabling meaningful comparisons through aggregated trends and benchmarks.
3Ease of operation
If spending transactions are stored without category information, then data storage is simpler, but businesses cannot compare spending by attributes such as category
Solution Approach 1:
The patent segments spending data by categorizing transactions into hierarchical categories, enabling attribute-based comparison without requiring storage of every individual transaction detail. Category-level aggregation reduces data volume while preserving analytical capability for comparing spending by specific attributes.
Solution Approach 2:
The patent implements partial categorization, storing category information for all transactions but enabling comparison at selective levels of detail. Users can choose to compare by broad categories or drill down to specific sub-categories, obtaining only the necessary level of detail for each comparison need, thus managing data volume efficiently.
4Adaptability or versatility
If businesses compare spending with all business entities, then more comparison data is available, but the results may not be helpful due to lack of similarity
Solution Approach 1:
The patent enables businesses to define selective criteria for comparison groups based on local characteristics such as industry type, company size, geographic location, and spending categories. This local quality approach ensures comparisons are made with relevant similar businesses rather than all businesses, improving measurement precision while maintaining flexibility in defining comparison groups.
Solution Approach 2:
The patent provides dynamic comparison capabilities where businesses can adjust their selective criteria and comparison parameters flexibly. Comparison groups can be dynamically defined and modified based on changing business needs, allowing adaptation to different analytical requirements while maintaining relevance through similarity-based filtering.
Data Source
AI summary
A system and method compares aggregated spending transactions of a target entity with those of a collection of entities. The system and method aggregates spending transactions of the target entity, and compares with the corresponding aggregated spending transactions of the collection of entities.