Token Exchange System for Dynamic Goal Simulation

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Solution Overview

Problem

Conventional systems fail to effectively simulate user behavior and account status while progressing towards a goal due to unpredictable user activities and static resource allocations, leading to limited impact from mitigation techniques and difficulties in updating resource allocations.

Innovation Solution

The use of token conversions and machine learning simulations to dynamically adjust token conversion schedules based on observed user behavior, allowing for bidirectional conversions between account balance and tokens, and leveraging automated agents for account management to enhance goal achievement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional static resource allocations are used in online accounts, then account management is simple, but the system fails to adapt to changing user circumstances and provides clear boundaries for resource allocation

Engineering Contradiction:

Improveadaptability to user circumstancesVSAvoidaccount management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic token conversion schedules that automatically adjust based on user behavior patterns and goal progression. The system transitions from static resource allocation to dynamic allocation where token conversion rates, frequencies, and amounts are continuously optimized based on observed user activities and circumstances changes

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes key parameters of resource allocation including token conversion rates, conversion frequencies, and allocation amounts based on user behavior data. Machine learning models analyze user patterns and automatically adjust these parameters to optimize goal achievement while adapting to changing user circumstances

Inventive Principle:

Principle #35Parameter changes

2Reliability

If conventional mitigation techniques are applied to unpredictable user behavior, then some guidance is provided, but the impact is limited due to diverse user activities

Engineering Contradiction:

Improvegoal achievement reliabilityVSAvoidresponse to diverse user behaviors

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements continuous feedback loops where user behaviors are monitored, analyzed, and used to adjust token conversion schedules. The system observes user activities, compares actual progression against goal targets, and automatically modifies conversion parameters to improve goal achievement reliability across diverse user behavior patterns

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions by pre-calculating optimal token conversion schedules based on historical user behavior patterns and goal requirements. Machine learning models predict future user activities and proactively adjust conversion parameters before deviations occur, enhancing goal achievement reliability

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If detailed tracking of user behavior is implemented to improve goal progression, then better insights are gained, but computing resources increase

Engineering Contradiction:

Improveuser behavior insightsVSAvoidcomputing resource consumption

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the most relevant user behavior features and metrics needed for goal progression analysis. Instead of processing all user data, the system identifies and extracts key behavioral patterns, conversion frequencies, and goal-related activities that have the highest impact on optimization decisions

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system implements partial tracking by monitoring a subset of user behaviors that are most critical for goal achievement. The machine learning models focus on analyzing specific behavior categories rather than all possible user activities, reducing computing resource requirements while maintaining effective goal progression insights

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12093932B1Simulations using a token exchange system

Publication Date: 2024.09.17 UNITED SERVICES AUTOMOBILE ASSOCIATION (USAA)

AI summary

Aspects of the present disclosure are directed to techniques to simulate conditions while progressing towards a goal state using token conversions. Tokens can be assets associated with a defined goal and can be implemented to segment accounts for a defined purpose and to improve user planning. An account can be segmented by converting a portion of the user's account balance to tokens to progress towards the goal. In some implementations, a simulation can generate a target token conversion schedule that achieves a target number of tokens at a target date. However, user behavior can change the observed token amounts and cause deviations from the schedule. The simulations can alter the token conversion schedule and arrive at a new target date for the target number of tokens. In some implementations, automated agents and machine learning components can be used to implement the token tracking and simulations.