AI Support Network for Targeted Advisor Guidance
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Solution Overview
Problem
The complexity and multiplicity of data in automated analysis systems make it challenging to accurately transmit relevant data to users, often leading to inaccuracies and inefficiencies due to the unique needs of each user, exacerbated by the vast volume of available data.
Innovation Solution
A system utilizing a server configured to receive biological extractions from users, generate diagnostic outputs through machine-learning processes, and identify influencers by matching user attributes with influencer attributes to provide targeted behavior modification requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated analysis systems process vast volumes of data, then the quantity of available information increases, but the accuracy of transmitting relevant data to users deteriorates due to complexity and multiplicity
Solution Approach 1:
The patent segments the vast volume of data into distinct categories and types (e.g., diagnostic data, behavioral data, biological extraction data). The system divides data processing into multiple specialized modules that handle different data types separately, making it easier to identify and transmit only the relevant portions to specific users based on their needs.
Solution Approach 2:
The system applies local quality by tailoring data transmission to individual user needs and characteristics. Each user receives customized data subsets based on their specific requirements, role, and context rather than a uniform data distribution. This ensures high accuracy in data transmission by delivering precisely the right information to the right person.
2Loss of information
If data is transmitted to all users, then completeness of information distribution increases, but inefficiency and waste increase due to unique individual needs of each user
Solution Approach 1:
The system dynamically adjusts data transmission based on real-time user profiles, needs, and contexts. Rather than static one-size-fits-all distribution, the system continuously adapts which data is transmitted to which user, optimizing both completeness and efficiency by delivering the right information to the right person at the right time.
Solution Approach 2:
The patent introduces an intermediary intelligent system that acts as a mediator between the data source and users. This intermediary analyzes user needs, filters appropriate data, and delivers customized information subsets, thereby maintaining information completeness while eliminating waste from transmitting irrelevant data to users who don't need it.
3Area of stationary object
If transmissions are made to incorrect professionals, then coverage of data distribution increases, but system accuracy deteriorates due to inaccuracies and time waste in correction
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user profiles, credentials, and needs before data transmission occurs. This preliminary matching process ensures that data is routed to the correct professionals from the outset, preventing misdirected transmissions and eliminating the need for corrective actions, thereby maintaining both wide coverage and high accuracy.
Solution Approach 2:
The patent implements feedback mechanisms that monitor data transmission accuracy and user responses. When the system detects potential misrouting or transmission errors, feedback loops enable corrective actions to be taken, ensuring that data reaches the appropriate professionals and maintaining system reliability even as coverage expands.
Data Source
AI summary
A system for an artificial intelligence support network for informed advisor guidance includes a diagnostic engine operating on a computing device and configured to receive a biological extraction related to a user, said biological extraction comprising a self-assessment of the user, and generate a diagnostic output as a function of the self-assessment of the user The system includes an advisor module. The advisor module is configured to select an informed advisor as a function of the diagnostic output, generate an advisory output as a function of the diagnostic output, said advisory output identifying the current condition of the user, and transmit the advisory output to a client device associated with the selected informed advisor.