Assignment Evaluation System Using Cohort-Based Machine Learning

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Solution Overview

Problem

Existing methods struggle to effectively evaluate assignments due to nebulous data representation, making it difficult for automated processes to detect and quantify deficiencies.

Innovation Solution

An apparatus and method utilizing a processor and memory to receive assignment data, inner circle data, and determine a cohort using a supervised machine-learning model to generate actions based on the cohort and assignment data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated processes are used to evaluate assignments, then productivity is improved, but measurement precision deteriorates due to nebulous data representation

Engineering Contradiction:

Improveassignment evaluation efficiencyVSAvoiddata evaluation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary processing layer that transforms nebulous assignment data into structured, quantifiable representations. This intermediary system uses natural language processing and data normalization techniques to bridge the gap between ambiguous input data and automated evaluation requirements, enabling both high productivity and accurate measurement simultaneously

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system applies parameter changes by transforming qualitative, nebulous data parameters into quantitative, structured parameters suitable for automated processing. Through parameter transformation and standardization, the system maintains measurement precision while enabling efficient automated evaluation at scale

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If supervised machine-learning models are used to generate actions, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improveevaluation accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex evaluation system into distinct functional modules: data reception module, cohort determination module, action generation module, and token certificate module. Each module performs a specific function with well-defined interfaces, reducing overall system complexity while maintaining high measurement precision through specialized processing in each segment

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The supervised machine-learning model serves multiple functions within the system: it determines cohorts based on inner circle data, evaluates assignment performance, and generates actionable recommendations. This multi-functionality reduces the need for separate specialized systems, thereby managing device complexity while maintaining comprehensive evaluation precision

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11823100B1Apparatus and method for evaluating assignments

Publication Date: 2023.11.21 GRAVYSTACK INC

AI summary

An apparatus and method for evaluating assignments. The processor receives assignment data generated by an inner circle member and inner circle data relating to the inner circle member. The processor determines a cohort for the inner circle member based on the inner circle data. The processor generates an action for the assignment based on the assignment and the cohort.