Group Schedule Optimization Using ML Domain Targeting
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Solution Overview
Problem
Members of a group often struggle within specific domains, leading to underperformance in shared domains, necessitating a method to optimize value for both the group and its members.
Innovation Solution
A computing device processes domain data to extract critical domain data, generates recommendations using machine learning models, and presents them through a graphical user interface with customizable views, allowing for targeted domain-specific goals and schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain data is processed manually to set targets and schedules, then customization and accuracy are improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent replaces manual mechanical processes of domain data analysis and target setting with machine learning models and automated computing systems. The system automatically processes domain data, extracts critical information, generates targets and schedules, and optimizes recommendations without human intervention, thereby maintaining high accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-service by allowing the automated platform to independently process domain data, generate recommendations, and update targets without requiring manual input or review. The machine learning models autonomously analyze data patterns, identify critical domains, and produce optimized schedules, making the system self-sufficient and eliminating time-consuming manual operations.
2Reliability
If comprehensive domain data is analyzed for all group members, then recommendation quality is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the critical subset of domain data necessary for generating recommendations, rather than processing all available data. The system identifies and isolates key performance indicators and critical domain parameters, removing unnecessary data elements. This extraction approach maintains recommendation quality by focusing on essential information while significantly reducing computational complexity and processing requirements.
Solution Approach 2:
The system applies local quality by tailoring the analysis depth and data processing intensity to specific domains and individual group members based on their unique characteristics and performance needs. Rather than uniformly processing all data for all members, the system adapts its computational resources to focus on critical areas, maintaining high recommendation quality where needed while reducing overall system complexity.
3Loss of information
If multiple views and display elements are provided in the graphical interface, then information completeness is improved, but interface complexity and user navigation difficulty increase
Solution Approach 1:
The graphical user interface implements dynamics by allowing users to dynamically switch between multiple views and display elements based on their specific information needs. The system provides a flexible, adaptive interface where users can toggle between comprehensive detailed views and simplified overview displays, enabling complete information access while maintaining ease of operation through on-demand complexity adjustment.
Solution Approach 2:
The patent segments the graphical interface into multiple independent views and display elements, each presenting specific types of information. Users can selectively activate or deactivate different view segments based on their current needs, allowing complete information to be organized into manageable, non-overwhelming segments that improve usability while maintaining information completeness.
Data Source
AI summary
Aspects of the present disclosure generally relates to a method including receiving group data and identifying at least a domain target for the at least a domain as a function of the domain-specific data. Also, the method may include generating a plurality of candidate schedules. Further, the method may include selecting one or more recommendations from the plurality of candidate schedules. Moreover, the method may include presenting, at a remote device, the one or more recommendations to a group, and tracking, by the computing device, a group's progress with regard to the optimal group schedule.