AI Advisory System for Data Relevance Filtering

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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, leading to inaccuracies and frustration.

Innovation Solution

An artificial intelligence advisory system that uses a computing device to receive user inputs, evaluate the content to identify conversation themes, and match these themes to a repository response using a machine-learning process, selecting and modifying textual outputs for transmission to the user.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If automated analysis systems process large volumes of data, then the quantity of information available increases, but the accuracy of transmitting relevant data to users deteriorates

Engineering Contradiction:

Improvevolume of dataVSAvoidaccuracy of data transmission

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The system segments the large volume of data into distinct categories and types (e.g., health data, financial data, social media data) and further divides it by relevance levels. The advisory module breaks down complex data sets into manageable portions that can be individually evaluated for relevance to specific user needs, preventing information overload and maintaining transmission accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies different processing and filtering qualities to different portions of data based on their relevance and importance. Critical data receives higher priority processing and more rigorous validation, while less critical data undergoes lighter processing. This localized quality approach ensures that the most important data is transmitted with highest accuracy while managing overall system resources efficiently.

Inventive Principle:

Principle #3Local quality

2Loss of information

If the system transmits all available data to users, then completeness of information improves, but user frustration and system inefficiency worsen

Engineering Contradiction:

Improvecompleteness of informationVSAvoiduser experience

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The system implements partial action by transmitting only the subset of data that is relevant to each user's specific needs, preferences, and context. The advisory module evaluates user profiles, current conditions, and data relevance scores to determine which portions of the complete data set should be transmitted. This prevents information overload while ensuring that all necessary information is included.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The advisory module acts as an intermediary between the complete data repository and the user. It filters, prioritizes, and formats data before transmission, translating the raw complete data set into a user-friendly format that maintains completeness of essential information while eliminating redundant or irrelevant content that would cause frustration.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If manual evaluation of data relevance is performed, then accuracy of data selection improves, but processing time and system complexity worsen

Engineering Contradiction:

Improveaccuracy of data selectionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and pre-evaluating data before it reaches the user. The advisory module pre-categorizes data, pre-calculates relevance scores based on user profiles, and pre-filters potential transmissions. This preliminary evaluation maintains high accuracy of data selection while reducing the processing time required at the moment of user interaction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual mechanical evaluation with automated electronic processing. The advisory module uses algorithms and machine learning models to automatically evaluate data relevance, substitute human judgment with computational analysis that can process vast amounts of data rapidly while maintaining consistent accuracy standards across all transmissions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250045309A1Artificial intelligence advisory systems and methods for vibrant constitutional guidance

Publication Date: 2025.02.06 KPN INNOVATIONS LLC

AI summary

An artificial intelligence advisory system for vibrant constitutional guidance, the system comprising a computing device; an advisory module operating on the computing device, configured to receive at least a user input from a user client device; evaluate the content of the at least a user input to identify a theme of conversation; and locate an advisor client device operated by an informed advisor utilizing the identified theme of conversation; and an artificial intelligence advisor operating on the computing device, wherein the artificial intelligence advisor is configured to match the theme of conversation to a repository response utilizing a first machine-learning process, wherein the repository response contains a plurality of textual outputs related to the theme of conversation; select a textual output contained within the repository response; modify the textual output in response to the at least a user input; transmit the modified textual output to the user client device.