Personalized Inflation Modeling via Activity Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated financial planning systems lack the ability to develop personalized financial strategies that adequately mitigate risks such as inflation, particularly for long-term goals like retirement, as they fail to account for individual spending habits and future inflation risks.
Innovation Solution
A provider computing system that receives user data, including spending information and activity sensor data, to determine a predicted spending profile and develop a personal inflation liability model, generating an optimal investment portfolio to mitigate future inflation risks by strategically allocating assets to sectors expected to undergo larger inflation rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional automated financial planning systems are used, then basic financial advice can be provided, but the systems cannot develop personalized financial strategies that adequately mitigate inflation risks
Solution Approach 1:
The patent segments the financial planning process into distinct modules: spending pattern analysis module, inflation liability modeling module, and portfolio optimization module. Each module processes specific aspects of financial data independently and combines results to create a comprehensive personalized strategy, enabling both personalization and reliable risk mitigation.
Solution Approach 2:
The system performs preliminary actions by analyzing historical spending patterns and predicting future spending requirements before inflation actually impacts the portfolio. This advance analysis allows the system to pre-position assets in inflation-protective sectors and adjust allocations proactively rather than reactively.
2Measurement precision
If activity sensor data is integrated into financial modeling, then more accurate predicted spending profiles can be generated, but the system complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data types (transaction data, sensor data, demographic data) through a single integrated architecture. The processing circuit is designed to universally accept and process various input formats, converting them into standardized spending profile parameters, thereby managing complexity while maintaining precision.
Solution Approach 2:
The system introduces an intermediary processing layer that acts as a mediator between raw sensor data and the financial modeling engine. This intermediary layer aggregates, validates, and transforms sensor data into meaningful spending pattern indicators, simplifying the integration process while preserving measurement precision.
3Reliability
If personalized inflation liability models are developed for each user, then future inflation risks can be accurately identified, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating inflation liability parameters and storing them in a lookup database. When a user's spending profile is established, the system quickly retrieves pre-computed inflation factors and applies them to generate the personalized model, significantly reducing computation time while maintaining reliability.
Solution Approach 2:
The patent employs parameter changes by using standardized inflation assumptions and modeling parameters that can be adjusted in discrete steps. This allows the system to generate personalized models by modifying key parameters rather than performing complete recalculations, improving productivity while preserving assessment reliability.
Data Source
AI summary
A system for personalized inflation modeling and mitigation includes a network interface, a database, and a processing circuit. The processing circuit is structured to receive user data comprising spending information, a current investment portfolio of a user, and information obtained from an activity sensor. The processing circuit is further structured to: determine a predicted spending profile indicating a prediction of future spending of the user for each spending category of a set, develop a personal inflation liability model based on the determined predicted spending profile and predicted inflation information, where the personal inflation liability model indicates a personalized future inflation risk associated with at least one of the spending categories, generate an optimal investment portfolio configured to mitigate the personalized future inflation risk based on the current investment portfolio and the personal inflation liability model, and modify the current investment portfolio based on the optimal investment portfolio.