Protocol Database Interface Using Generative AI and Multi-Channel Input
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Solution Overview
Problem
Traditional protocol database systems restrict access to process owners and their delegates, leading to knowledge gaps, inefficiencies, and increased vulnerability due to limited information dissemination.
Innovation Solution
A multi-channel cognitive interaction platform utilizing a generative machine learning model to analyze and generate outputs such as new rules, calendar data, and action requests, integrating them automatically into the protocol database, thereby enhancing accessibility and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access to protocol database is restricted to process owners and their delegates, then security and control are maintained, but information dissemination is limited leading to knowledge gaps and increased vulnerability
Solution Approach 1:
The patent introduces a cognitive interaction platform as an intermediary between the protocol database and users. This platform uses machine learning models to process natural language queries and generate accurate responses about protocol information, allowing broader access without direct database exposure. The intermediary maintains security while enabling information dissemination through automated query processing and response generation.
2Device complexity
If traditional protocol database systems are used with restricted access, then system complexity is kept simple, but users find the protocol tedious and time-consuming to access
Solution Approach 1:
The patent replaces the mechanical interaction model (manual navigation of database interfaces) with an intelligent system using machine learning models. Users can query protocol information using natural language instead of navigating complex database structures. The system automatically processes queries, retrieves relevant information, and generates comprehensive responses, dramatically reducing access time while maintaining manageable system complexity through modular architecture.
3Extent of automation
If manual processes are used for protocol updates and rule creation, then system automation is minimal, but computational resources and time are significantly reduced through automated processing
Solution Approach 1:
The patent implements self-service capabilities where the system automatically generates new rules, detects dependencies, and updates protocol databases without extensive manual intervention. The machine learning models process incoming information, identify patterns, and create structured outputs autonomously. This automation reduces computational overhead by optimizing query processing and using efficient algorithms for dependency detection and rule generation, achieving high automation with reasonable resource consumption.
Data Source
AI summary
Systems, computer program products, and methods are described herein for protocol database generative interfacing via a multi-channel cognitive interaction platform. The present disclosure includes training a machine learning model, wherein the machine learning model comprises a generative machine learning model, and wherein the generative machine learning model is trained on entries of a protocol database, receiving, into a multi-channel cognitive interaction platform, an input of at least one of text, voice, and an image, detecting, using an aggregation engine, changes in the protocol database, detecting, using a relationship engine, dependencies in the protocol database comprising dependencies between the at least one protocol, and generating a generated output using the machine learning model.