Dynamic Client Solution Generation via Machine Learning and NLG
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Solution Overview
Problem
Current techniques for creating static client solutions are inefficient, requiring extensive manual effort and time, leading to delayed delivery and lost business opportunities due to their inability to support interactive input and timely adaptation to client needs.
Innovation Solution
A system utilizing machine learning and natural language generation models to process historical project and client data, generating digitized dynamic client solutions that can be interactively customized and updated in real-time, reducing production time from days to hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual creation of static client solutions is used, then customization quality is maintained, but production time increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical creation processes with an automated AI-based system that uses machine learning models and natural language generation to create client solutions, eliminating the need for manual drafting while maintaining customization quality
Solution Approach 2:
The system enables self-service through automated generation where the AI model independently creates client solutions based on input data, reducing dependency on manual intervention and accelerating production without sacrificing customization
2Adaptability or versatility
If static client solutions are created manually, then detailed customization is achieved, but adaptability to real-time needs is reduced
Solution Approach 1:
The patent transitions from static solutions to dynamic, interactive client solutions that can adapt in real-time based on user input and changing needs, enabling the system to respond dynamically rather than requiring manual re-creation for each change
Solution Approach 2:
The system incorporates feedback mechanisms where client input is processed by the AI model to generate updated solutions, creating a feedback loop that enables continuous adaptation and real-time delivery without manual intervention
3Extent of automation
If manual creation processes are used, then control over solution quality is maintained, but labor dependency increases and scalability is limited
Solution Approach 1:
The patent substitutes manual creation processes with an automated AI system that handles solution generation, allowing for high-level automation while the system manages its own feasibility through built-in validation and generation capabilities
Data Source
AI summary
A device may receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and may process the historical project data and the client data, with machine learning models, to generate recommendations for the problem and confidence scores for the recommendations. The device may process the recommendations and the confidence scores, with an NLG model, to generate a solution to the problem and content for the solution, and may generate a digitized dynamic client solution to the problem based on the solution and the content. The device may provide the digitized dynamic client solution to a user device, and may receive feedback on the digitized dynamic client solution from the user device. The device may generate a final digitized dynamic client solution based on the feedback, and may perform actions based on the final digitized dynamic client solution.