LLM-Based Performance Benchmarking for Personalized Improvement Roadmaps

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Solution Overview

Problem

Existing performance improvement systems lack individualized approaches tailored to user-specific strengths and weaknesses, relying on passive and unreliable industry benchmarks, failing to provide dynamic and effective personal performance enhancement.

Innovation Solution

A computer-implemented method using large language models to determine contextualized and personalized scores, integrating individual and industry benchmarks, generating a trackable objective roadmap for continuous improvement, leveraging AI to analyze user performance data and generate actionable insights.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional performance review systems are used with industry benchmarks, then performance evaluation can be standardized, but the system fails to provide individualized approaches tailored to user-specific strengths and weaknesses

Engineering Contradiction:

Improveindividualized performance improvementVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments performance evaluation into multiple dimensions including contextualized metadata analysis, personal parameter comparison, and multi-benchmark integration (individual, team, industry). This segmentation allows the system to handle complexity through modular processing of different performance aspects separately while providing comprehensive individualized feedback.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically changes evaluation parameters by adjusting weights and thresholds based on user-specific metadata, historical performance data, and contextual factors. This enables the same performance review system to adapt to different users' strengths, weaknesses, and career stages without requiring entirely separate systems.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If passive industry benchmarks are used, then performance standards can be established, but the system fails to provide dynamic and effective personal performance enhancement

Engineering Contradiction:

Improveperformance evaluation accuracyVSAvoiddynamic personalization

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements continuous feedback loops where performance data is collected, analyzed against multiple benchmarks, and used to generate personalized improvement recommendations. This feedback mechanism ensures that the evaluation remains reliable while adapting to individual user needs through iterative refinement of performance insights.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The performance review system transitions from static industry benchmarks to dynamic evaluation that continuously adapts to individual user profiles, historical performance trends, and changing contextual factors. This dynamic approach maintains reliability through data-driven analysis while providing personalized enhancement pathways.

Inventive Principle:

Principle #15Dynamics

3Productivity

If generic performance improvement plans are implemented, then resource allocation can be simplified, but the system fails to address user-specific strengths and weaknesses effectively

Engineering Contradiction:

Improveperformance improvement effectivenessVSAvoidtime for personalized analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual, time-consuming personalized analysis with AI-driven automated processing of performance data. Machine learning algorithms analyze contextualized metadata, personal parameters, and benchmark comparisons to generate customized improvement plans, dramatically reducing the time investment required while maintaining or enhancing improvement effectiveness.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables users to receive personalized performance analysis and improvement recommendations through automated processing of their own performance data. The AI system self-adjusts evaluation parameters and generates tailored insights without requiring extensive manual intervention, making personalized performance enhancement scalable and time-efficient.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250335858A1Ai-enhanced intelligent workflow for improved personal performance

Publication Date: 2025.10.30 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method for determining, using a first large language model, a contextualized score based on contextualized metadata describing a user and at least one additional individual. The method may further include determining, using a second large language model, a personalized score by comparing personal parameters describing the user against historical parameters. Based on an aggregation of the contextualized score and the personalized score, the method may determine an individual benchmark. The method may further include determining an industry benchmark based on historical industry benchmarks. The method may further include generating an objective roadmap for the user based on the individual benchmark and the industry benchmark, where the roadmap includes first actions for improvement that are generated by measuring a first distance between a first status, the individual benchmark, and the industry benchmark.