Machine Learning Domain Targets for Personalized User Scheduling

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Solution Overview

Problem

Users face challenges in effectively managing their finite resources to prioritize and balance multiple life domains, as existing systems lack the ability to automatically set targets and schedules that align with their goals and status within these domains.

Innovation Solution

A system and method utilizing machine learning models to generate domain-specific targets and schedules based on user data, incorporating target-setting and scheduling algorithms to optimize resource allocation across selected domains, and provide personalized motivational feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If users manually manage their schedules and priorities across multiple life domains, then they can maintain control over their time, but they experience difficulty balancing multiple life realms and cannot effectively exploit maximum value due to finite time resources

Engineering Contradiction:

Improvevalue exploitation efficiencyVSAvoidtime management difficulty

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables automated self-service by using machine learning models to automatically generate schedules and set domain targets without requiring manual user intervention. The scheduling model takes user inputs and autonomously creates optimized schedules, while the target-setting model automatically establishes domain-specific goals, allowing users to exploit value efficiently without manual time management overhead

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system changes parameters by dynamically adjusting domain targets and schedule allocations based on user status and domain-specific data. The machine learning models continuously optimize time distribution across domains by modifying schedule parameters and target parameters, enabling adaptive value exploitation as user circumstances change

Inventive Principle:

Principle #35Parameter changes

2Productivity

If the system generates automated schedules and targets using machine learning models, then value exploitation efficiency is improved, but the system complexity increases due to multiple trained models and data processing requirements

Engineering Contradiction:

Improveschedule generation efficiencyVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments functionality into two distinct machine learning models: a target-setting model that generates domain-specific targets and a scheduling model that creates time allocations. This segmentation allows each model to specialize in its function, improving overall efficiency while organizing system complexity into manageable, independent components that can be trained and maintained separately

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If the system processes domain-specific data and user status to generate personalized schedules, then schedule relevance and motivation are enhanced, but data processing requirements and computational resources increase

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system applies local quality by processing only domain-specific data relevant to each user's selected life domains rather than analyzing all possible user data. The target-setting model focuses computations on specific domain parameters, and the scheduling model processes only the domain targets and user status data necessary for schedule generation, reducing overall computational overhead while maintaining high personalization

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250322370A1Methods and systems for exploiting value in certain domains

Publication Date: 2025.10.16 FLOURISH WORLDWIDE LLC

AI summary

Aspects relate to methods and systems for exploiting value within certain domains. An exemplary method includes interrogating, using a remote device, a user for scheduling data and at least a domain, wherein the at least a domain includes at least one domain and no more than a predetermined maximum number of domains, receiving, using the remote device, the at least a domain from the user, interrogating, using the remote device, the user for domain-specific data associated with the at least a domain, receiving, using the remote device, the domain-specific data from the user, generating, using a computing device, a domain target for the at least a domain as a function of the domain-specific data, generating, using the computing device, a user schedule as a function of the domain target and the scheduling data, and displaying, using the remote device, the user schedule and the domain target to the user.