Knowledge Graph Coaching Prompts for Accurate Productivity Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital content systems are inflexible and inefficient, relying solely on single-application data for user productivity assessment, leading to inaccurate predictions due to limited data scope and lack of contextual information.
Innovation Solution
An executive coaching system that generates coaching prompts using a knowledge graph informed by observation layer, world state, and connector data sources, determining a pulse status and generating coaching insights with recommended actions for improving productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems rely solely on single-application data for productivity assessment, then system simplicity is maintained, but measurement precision and reliability of productivity predictions deteriorate
Solution Approach 1:
The patent merges multiple data sources including observation layer data from various applications, world state data from device sensors, and connector data from external sources into a unified knowledge graph. This combination enables comprehensive productivity assessment by integrating diverse data types that individually would be insufficient, thereby resolving the contradiction between maintaining system simplicity and achieving accurate predictions.
Solution Approach 2:
The knowledge graph structure serves multiple functions simultaneously: it stores structured relationships between entities, processes unstructured observational data, integrates sensor data, and provides a unified interface for generating coaching insights. This multi-functionality allows the system to handle diverse data sources without proportionally increasing complexity, addressing the contradiction between measurement precision and device complexity.
2Adaptability or versatility
If existing systems are rigidly fixed to single-application input signals, then system adaptability is limited, but ease of operation is maintained
Solution Approach 1:
The knowledge graph is designed as a universal data structure that can accommodate multiple types of inputs including structured application data, unstructured observation layer data, and sensor data from various device components. This universal structure enables the system to adapt to diverse data sources without requiring separate processing pipelines for each data type, thereby achieving versatility without proportionally increasing architectural complexity.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between diverse data sources and the coaching insight generation process. It standardizes and structures data from multiple origins into a unified format, allowing the system to adapt to various data sources without exposing this complexity to the user interface or final processing stages, thus maintaining ease of operation while achieving adaptability.
3Measurement precision
If existing systems lack contextual environmental data, then data collection simplicity is maintained, but measurement precision of productivity assessment deteriorates
Solution Approach 1:
The patent segments data collection into distinct layers: observation layer data from application interfaces, world state data from device sensors, and connector data from external sources. Each layer is processed and structured independently before being integrated into the knowledge graph. This segmentation allows the system to manage large volumes of diverse data without overwhelming the processing architecture, achieving precise productivity assessment while maintaining manageable data handling through structured segmentation.
4Reliability
If existing systems generate underinformed predictions, then processing speed is maintained, but reliability of productivity predictions deteriorates
Solution Approach 1:
The system performs preliminary structuring and organization of data from multiple sources into the knowledge graph before generating productivity predictions. By pre-processing and structuring the data in advance, the system ensures that when predictions are generated, the necessary contextual information is already organized and accessible, thereby improving prediction reliability without proportionally increasing processing time during the actual prediction generation phase.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing coaching insights using a large language model to process coaching prompts. In some embodiments, the disclosed systems generate a coaching prompt from a knowledge graph encoding data from data sources, such as an observation layer and a world state. The disclosed systems also determine a pulse status of a user account to inform a coaching prompt. Additionally, the disclosed systems provide the coaching prompt to a large language model for generating a coaching insight to improve the pulse status.