Contextual Spending Correlation Analysis System
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Solution Overview
Problem
Current technologies fail to effectively leverage minute-by-minute data to predict and manage user spending, as individuals are often unaware of how environmental and relational contexts influence their spending habits, leading to challenges in budgeting and money management.
Innovation Solution
A system and method that collect user spending context data, including location, relationship, and biometric information, to determine correlations between these variables and spending patterns, generating predictions and notifications to help users control their spending by identifying actions to reduce projected expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If minute-by-minute data is collected and analyzed to predict spending, then spending prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the minute-by-minute data into contextual categories (location context, relationship context, activity context) and processes each segment separately using specific correlation models. This segmentation reduces the complexity of analyzing all data points uniformly while maintaining prediction accuracy by applying targeted analysis to each context type.
Solution Approach 2:
The system transforms raw minute-by-minute data into meaningful contextual parameters (e.g., converting location data to place types, converting social interactions to relationship contexts). This parameter transformation simplifies the data structure and enables more efficient processing while preserving the predictive value of the original data.
2Reliability
If contextual data variables are analyzed to understand spending influences, then spending control effectiveness is improved, but computational requirements increase
Solution Approach 1:
The patent pre-establishes spending correlation models and contextual frameworks before actual spending events occur. By pre-processing the data structures and correlation relationships, the system reduces the computational burden during real-time spending analysis, lowering energy consumption while maintaining effective spending control.
Solution Approach 2:
The system automatically identifies and analyzes relevant contextual variables without requiring manual intervention or extensive computational resources. The contextual spending correlation model self-adapts to user spending patterns, reducing the need for energy-intensive reprocessing of data.
3Loss of information
If comprehensive contextual parameters are tracked, then understanding of spending behavior is improved, but information processing load increases
Solution Approach 1:
The patent extracts only the most relevant contextual parameters from the comprehensive data set for each spending event. By identifying and extracting key influencers (such as location type, relationship context, or activity category) while discarding redundant information, the system maintains deep understanding of spending behavior without overwhelming processing loads.
Solution Approach 2:
The system organizes comprehensive contextual parameters into hierarchical dimensions (e.g., location dimension, social dimension, temporal dimension). This dimensional organization allows the system to process information more efficiently by analyzing patterns across dimensions rather than treating all parameters as a flat, complex data set.
Data Source
AI summary
Systems and methods receive contextual data related to spending as well as spending data. Correlations are determined to explain variables that influence spending amounts or rates. Systems and methods can collect user spending context data associated with a user, generate spending correlations between variables of the user spending context data, determine a next event based on at least one of the location data and the relationship data, and generate a next event value for the next event having at least a subset of the variables.