Dynamic Product Offering Adjustment via Circadian Rhythm Forecasting
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Solution Overview
Problem
Current business operations and product offerings are not dynamically adjusted to align with the changing micro Circadian Rhythms (MCRs) of consumers, leading to inefficiencies in meeting the fluctuating needs and wants of individuals and groups, as existing solutions fail to respond effectively to the dynamic changes in consumer states.
Innovation Solution
A method and system that analyzes CR data to identify and forecast MCR patterns, correlating them with possible future states to determine the likelihood of transitions, allowing businesses to dynamically adjust their operations and offerings through a notification system that uses forecasting engines, influencer values, and statistical distributions like Poisson and Skellam distributions to predict and respond to consumer states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If businesses use fixed product offerings and operations, then operational simplicity is maintained, but responsiveness to changing consumer needs deteriorates
Solution Approach 1:
The patent implements dynamic product offerings that automatically adjust based on real-time consumer CR state data. The system transitions from fixed operational schedules to dynamic adjustments driven by forecasted consumer states, allowing businesses to adapt offerings to match actual consumer needs without manual intervention.
Solution Approach 2:
The system incorporates feedback loops where consumer CR state data is continuously collected, analyzed, and used to adjust product offerings. The forecasting engine processes CR state transitions and feeds recommendations back to the offering adjustment mechanism, creating a closed-loop system that continuously optimizes responsiveness.
2Productivity
If businesses implement real-time adjustment systems, then responsiveness to consumer needs improves, but system complexity increases
Solution Approach 1:
The system enables self-service automation where the forecasting engine and offering adjustment mechanism operate autonomously based on CR state data. The system automatically identifies optimal offering adjustments without requiring complex manual coordination, reducing operational overhead while maintaining high responsiveness.
Solution Approach 2:
The forecasting engine performs preliminary analysis of CR state transitions to predict future consumer needs before they fully manifest. By anticipating state changes and preparing offering adjustments in advance, the system achieves high operational efficiency without requiring complex real-time decision-making infrastructure.
3Measurement precision
If businesses collect and analyze CR data, then consumer state prediction accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the critical CR state parameters needed for forecasting from the complete CR data set. By identifying and focusing on the most relevant state transitions and influencers, the system achieves high prediction accuracy without processing unnecessary data, reducing computational load while maintaining precision.
Data Source
AI summary
A first Micro Circadian Rhythm (MCR) pattern is identified in a Circadian Rhythm (CR) data of a user. Using the first MCR pattern, a second MCR pattern is predicted during a forecast period. The second MCR pattern is correlated with a set of possible future CR states. A first model of a distribution of a confidence value corresponding to the present CR state of the user is constructed. A second model of a distribution of a confidence value corresponding to a selected future CR state from the set of possible future CR states of the user is constructed. The first model and the second model are correlated to determine a likelihood of the selected future CR state being reachable from the present CR state for the user. When the likelihood exceeding a threshold, an application is caused to adjust a process.