Smart Card Data Analysis System for Dynamic Decision Support
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Solution Overview
Problem
Location-specific mobile applications struggle to provide relevant and meaningful information without pre-defined rules, as they rely on static rules that do not align with dynamic changes in user activities and cognitive states, limiting their usefulness in real-life scenarios.
Innovation Solution
A data analysis system comprising a processor, latent learner, inference deducer, and probability determiner that arranges data in a multi-dimensional structure based on target activities, employs proactive-retroactive learning to predict outcomes, and determines the probability of success, generating and ranking choices for smart cards to aid individual decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined rules are used to provide information in mobile applications, then the information can be structured and presented, but the information becomes static and does not align with dynamic changes in user activities and cognitive states
Solution Approach 1:
The patent implements dynamic rule generation that adapts to changing user activities and cognitive states. Instead of static pre-defined rules, the system continuously updates rules based on real-time user data, making the information presentation dynamic and context-aware while managing complexity through automated learning algorithms.
Solution Approach 2:
The system employs machine learning models that automatically learn and generate rules without human intervention. The algorithms self-adjust to user behavior patterns, eliminating the need for manual rule creation and updating, thereby reducing long-term system complexity while improving adaptability.
2Loss of information
If pre-defined rules are used to structure information, then information can be presented in a meaningful manner, but the rules provide information that is generally not aligned to cognitive process
Solution Approach 1:
The system incorporates feedback loops where user interactions and cognitive state data continuously inform rule adjustments. The machine learning models analyze user responses and behavioral patterns to refine information structuring, ensuring alignment with cognitive processes while maintaining information relevance through iterative optimization.
Solution Approach 2:
The patent dynamically changes parameters of information presentation based on detected cognitive states and user activities. By adjusting information structure, timing, and content parameters in real-time, the system maintains both information relevance and alignment with cognitive processes without relying on fixed pre-defined rules.
3Device complexity
If location-specific mobile applications provide information based on pre-defined rules, then the applications can function with simple architecture, but the applications have very limited use for decision-making in real-life conditions
Solution Approach 1:
The system performs preliminary analysis of user data and pre-generates potential decision recommendations before users need them. By anticipating user needs and pre-processing information based on historical patterns, the application maintains simple architecture while providing reliable, context-aware decision support when required.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw user data and information presentation. These models translate complex behavioral data into structured, actionable insights, enabling reliable decision-making support while keeping the overall application architecture relatively simple through automated intermediate processing.
Data Source
AI summary
A data analysis system includes processor to: arrange data in a multi-dimensional structure based on a target activity defined by a smart card; perform analysis on the data to predict an outcome of the target activity of activities; determine a probability of success of the outcome that has been predicted; determine, based on the outcome and probability of success, choices associated with the activities; determine patterns and changes in the data pertaining to the activities detected by an access device with access to the smart card; perform transformative and scheduling, exposed through an application programming interface for the data; schedule to arrange the choices and the probability of success of the outcome for the access device; cue the choices and the probably of success; and transmit the plurality of choices and the probability of success of the outcome to the access device for rendering on the smart card.