Rank-Ordered Instruction Set Generation via Segmented Ranking
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Solution Overview
Problem
Machine-learning methods face challenges in optimizing instructions for users from large and varied data sets, leading to inefficiencies in sophistication and efficiency, particularly when dealing with complex user objectives.
Innovation Solution
A system and method for generating rank-ordered instruction sets using a ranking process, involving a computing device that receives user objectives, determines a rank-ordered objective set, identifies an instruction set using machine-learning, and generates a ranked list of instructions, iteratively improving solutions based on user actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learning methods are used to analyze large quantities of data and generate instructions for users, then the sophistication of instruction optimization improves, but the efficiency and complexity of the system deteriorates
Solution Approach 1:
The patent segments the instruction generation process into multiple independent ranking processes. Each ranking process handles specific aspects of instruction optimization, allowing the system to manage complexity by dividing the overall task into smaller, more manageable components that can be executed separately and combined.
Solution Approach 2:
The system implements dynamic ranking processes that adaptively adjust instruction rankings based on user objectives and data. The ranking processes are designed to be flexible and configurable, allowing the system to optimize instructions dynamically without requiring complete system reconfiguration, thus managing complexity while maintaining sophistication.
2Adaptability or versatility
If machine-learning methods are used to analyze large quantities of data and generate instructions for users, then the sophistication of instruction optimization improves, but the computational resources and time required deteriorates
Solution Approach 1:
By dividing the instruction optimization into separate ranking processes, the patent enables parallel execution of different ranking tasks. This segmentation allows computational resources to be distributed across multiple independent processes, improving overall computational efficiency while maintaining the sophistication of the optimization.
Solution Approach 2:
The patent implements preliminary ranking processes that prepare and pre-process instructions before final generation. By performing preliminary sorting and filtering of user objectives and data early in the process, the system reduces the computational burden on subsequent processes, improving overall productivity without sacrificing optimization quality.
Data Source
AI summary
A system for generating rank-ordered instruction sets includes at least a computing device, wherein the at least a computing device is configured to generate a first rank-ordered list of instructions, wherein generating further comprises receiving a plurality of user objectives, determine, using a first ranking process and a plurality of objectives, a rank-ordered objective set, identify, using a first machine-learning process and ranked-ordered goal set, an instruction set including a plurality of instructions, wherein the plurality of instructions includes an instruction for addressing each objective of the plurality of objectives, generate, using a second ranking process and a first plurality of instructions, the first ranked-ordered list of instructions for addressing the rank-ordered objective set. provide the rank-ordered instruction set to a user device, receive, from the user device, a plurality of user data, and generate, using the plurality of user data, a second rank-ordered list of instructions.