ML-Assisted Project Initiation Documents with Adaptive LLM Questions
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Solution Overview
Problem
Conventional project initiation processes involving multiple stakeholders are time-consuming due to the need for extensive human feedback and data collection through meetings, which can be inefficient and labor-intensive.
Innovation Solution
An ML-assisted system that uses a persona database and Large Language Models (LLMs) to generate an interactive user interface for project initiation, dynamically presenting contextually relevant questions based on user input, and updating the interface with unanswered questions based on LLM assessments to streamline data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional workflow tools are used to collect stakeholder input through meetings, then comprehensive stakeholder feedback can be obtained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service by having the LLM automatically generate questions, assess responses, and identify unanswered questions without requiring manual intervention from project managers or stakeholders. The system serves itself by autonomously managing the data collection process and adapting questions based on previous responses.
Solution Approach 2:
The patent replaces the mechanical system of manual meeting scheduling, question drafting, and response tracking with an automated LLM-based system. The LLM generates contextually relevant questions, assesses stakeholder responses, and dynamically updates the question list, eliminating the need for manual coordination and reducing time investment.
2Adaptability or versatility
If multiple stakeholders are involved in project initiation meetings, then diverse perspectives can be captured, but the complexity and number of interactions increase
Solution Approach 1:
The LLM serves multiple functions simultaneously: generating questions, assessing responses, identifying unanswered questions, and adapting questions to specific stakeholders. This multi-functional approach consolidates what would otherwise require multiple separate processes and tools into a single unified system, reducing overall complexity while maintaining comprehensive stakeholder coverage.
Solution Approach 2:
The system implements feedback loops where the LLM assesses each stakeholder's responses and uses this information to dynamically generate follow-up questions. This feedback mechanism ensures that diverse perspectives are captured effectively while automatically managing the complexity of multi-stakeholder interactions through intelligent question adaptation.
3Reliability
If manual review and feedback incorporation is performed, then quality control can be maintained, but productivity decreases
Solution Approach 1:
The LLM performs self-service by automatically reviewing stakeholder responses, generating appropriate questions, and identifying gaps in the data collection process. This eliminates the need for manual quality control while maintaining high standards, as the LLM continuously monitors and adjusts the collection process to ensure comprehensive and accurate information gathering.
Solution Approach 2:
The system maintains continuous operation by automatically generating and assessing questions without interruption. The LLM continuously monitors responses, generates follow-up questions, and updates the collection process in real-time, ensuring that quality control is maintained while productivity is enhanced through uninterrupted automated processing.
Data Source
AI summary
Provided are computer-implemented systems and methods for generating an ML-assisted project initiation user interface, comprising: providing, at a memory, a persona database comprising a plurality of personas and a corresponding plurality of questions; sending, at a network device, an ML-assisted project initiation user interface to a user device; receiving, at a network device, document content from a user at the user device; generating, at a processor in communication with the memory and the network device, an assessment of the document content; sending, using the network device, the assessment to a Large Language Model (LLM) system; receiving, using the network device, an assessment response from the LLM system corresponding to the assessment; updating, at the processor, the ML-assisted project initiation user interface with at least one unanswered question based on the assessment response.