Crowd-Matching Algorithm for Expert Audience Targeting

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Solution Overview

Problem

Individuals seeking information or advice often face challenges in distinguishing valuable responses from a diverse and sometimes misleading crowd-sourced audience, as responders may lack expertise or provide irrelevant information, making it difficult to identify reliable answers.

Innovation Solution

A system and method that automatically directs queries to the most suitable potential responders from a crowd-sourced population based on query content, context, timing, location, and preferred resources, using a crowd-matching algorithm to ensure high-quality and relevant answers by selecting the appropriate target audience.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If a public request for help is made to a broader crowd-sourced audience, then the quantity of responses increases, but the quality and reliability of answers deteriorates due to inclusion of unqualified responders

Engineering Contradiction:

Improvequantity of responsesVSAvoidquality of answers

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system segments the crowd-sourced audience into different groups based on their expertise, knowledge, and qualifications. By dividing the broad audience into specialized segments, the system can direct queries to appropriate expert groups while maintaining a large overall response pool, thus preserving both quantity and quality of answers.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary mechanism (the platform with its matching algorithms) that sits between the query and the crowd-sourced audience. This intermediary filters and directs queries to suitable responders based on expertise matching, preventing unqualified individuals from providing low-quality answers while maintaining access to a broad audience.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If queries are directed to a large crowd-sourced population, then the diversity of responses increases, but the complexity of identifying valuable information increases

Engineering Contradiction:

Improvediversity of responsesVSAvoidcomplexity of identifying valuable information

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms where responders are evaluated based on the quality and accuracy of their answers. This feedback is used to refine future matching, allowing the system to maintain diverse response pools while reducing the complexity of identifying valuable information through learned patterns of reliable responders.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces the manual mechanical process of sifting through diverse responses with an automated computational matching system. The platform uses algorithms to automatically match queries with suitable responders based on expertise profiles, eliminating the need for users to manually evaluate diverse responses and reducing identification complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If the system filters responses to ensure high quality, then the reliability of answers improves, but the time required to process and evaluate responses increases

Engineering Contradiction:

Improvereliability of answersVSAvoidtime to process responses

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-evaluating and profiling responders before queries are submitted. Expertise profiles, qualifications, and performance metrics are established in advance, allowing the matching system to quickly identify suitable responders without time-consuming evaluation at query time, thus maintaining reliability while reducing processing time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes parameters by using automated matching criteria based on expertise tags, knowledge areas, and performance metrics rather than manual review. By transforming the selection process into a parameter-based automated system, the platform maintains high reliability through systematic filtering while dramatically reducing the time required to process and evaluate responses.

Inventive Principle:

Principle #35Parameter changes

4Productivity

If the system uses automated matching algorithms, then the productivity of information delivery improves, but the device complexity of the system increases

Engineering Contradiction:

Improvespeed of information deliveryVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system achieves universality by designing a multi-functional platform that handles query reception, responder profiling, automated matching, response delivery, and feedback collection within a single integrated system. This universal approach improves productivity by eliminating the need for multiple separate systems while managing complexity through unified architecture and standardized processes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10915973B2System and method providing expert audience targeting

Publication Date: 2021.02.09 TRANSFORM SR BRANDS LLC

AI summary

A system and method for directing queries to the most suitable potential responders of an audience selected from a crowd-sourced population from which to request information, based on information such as query content, query context, timing, location, preferred supporting resource(s), and source of the query.