LLM Coaching Prompts Using Knowledge Graphs for Pulse Status
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Solution Overview
Problem
Existing digital content systems are inflexible and inefficient, relying solely on single-application data for productivity predictions, leading to inaccurate user account productivity assessments due to limited data scope.
Innovation Solution
Generate a coaching insight from a coaching prompt to include language informed by a knowledge graph that encodes data from unique data sources, such as observation layer, world state, and connectors, to provide coaching insights that improve user account productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems rely solely on single-application data for productivity predictions, then the system complexity remains low, but the measurement precision of productivity assessments deteriorates
Solution Approach 1:
The patent combines multiple data sources including observation layer data, world state data, connector data, and user interaction data into a unified analysis framework. This merging of diverse data streams enables comprehensive productivity predictions by integrating environmental context, application state, cross-application behaviors, and explicit user feedback, thereby resolving the contradiction between maintaining simple system architecture and achieving high measurement precision.
Solution Approach 2:
The system implements a universal data collection framework that gathers information from multiple sources serving different functions: observation layer for environmental context, world state for application state, connectors for cross-application data, and user interactions for explicit feedback. This multi-functional approach allows a single system to handle diverse data types and prediction requirements, improving productivity assessment accuracy without proportionally increasing complexity.
2Adaptability or versatility
If existing systems are fixed to single-application input signals, then the ease of operation is maintained, but the adaptability to environmental and contextual data deteriorates
Solution Approach 1:
The patent implements a dynamic data collection architecture where the system adaptively gathers information from multiple sources based on current context and requirements. The observation layer dynamically monitors environmental changes, the world state updates application context, connectors adaptively retrieve data from other applications, and user feedback mechanisms adjust based on interactions. This dynamic approach enables the system to adapt to varying data sources and contexts while maintaining operational simplicity through automated data integration.
3Reliability
If existing systems monitor only user interactions within a single application, then the device complexity remains low, but the reliability of productivity predictions deteriorates
Solution Approach 1:
The patent incorporates multiple feedback mechanisms to improve prediction reliability: user feedback through explicit inputs and reactions provides direct validation of productivity assessments, while system feedback from aggregated multi-source data enables continuous refinement of predictions. The observation layer, world state, connectors, and user interactions collectively provide feedback loops that validate and adjust productivity measurements, ensuring reliable predictions despite the increased complexity of integrating multiple data sources.
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.