ML-Based Relationship Association for Service Needs Assessment

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Solution Overview

Problem

Current community outreach and social servicing platforms fail to accurately determine customer needs due to service fragmentation and geographic isolation, providing superficial and inadequate assessments and limited access to customer information, especially for low-income and high-risk groups.

Innovation Solution

The implementation of machine learning-based relationship associations to assess user needs by processing electronic service requests, transmitting questionnaires, determining relationship values, and generating personalized lists of service providers based on user responses and demographic information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional social service platforms use conventional assessment methods, then the system complexity remains low, but the measurement precision of customer needs assessment deteriorates

Engineering Contradiction:

Improvecustomer needs assessment accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces conventional mechanical assessment methods with machine learning algorithms that automatically analyze questionnaire responses, social media data, and demographic information to determine customer needs. This substitution of intelligent algorithms for manual assessment processes significantly improves measurement precision while managing system complexity through automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of assessment by incorporating multiple data sources (questionnaire responses, social media activity, demographic data) and using machine learning models that process these varied parameters to generate comprehensive customer need profiles, thereby improving assessment accuracy beyond traditional single-source methods.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If machine learning algorithms process multiple questionnaire responses to determine relationships, then the measurement precision of needs assessment improves, but the loss of time in processing increases

Engineering Contradiction:

Improveneeds assessment accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing questionnaire responses, social media data, and demographic information in structured formats before actual assessment. This preliminary organization of data enables faster machine learning processing when assessment is needed, reducing the time loss while maintaining high measurement precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning algorithms continuously process and analyze customer data in real-time as questionnaire responses and social media information become available, rather than batch processing. This continuous useful action maintains high assessment accuracy while minimizing processing delays by immediately generating insights from incoming data.

Inventive Principle:

Principle #20Continuity of useful action

3Adaptability or versatility

If the system uses comprehensive data analysis to generate service provider recommendations, then the adaptability to individual customer needs improves, but the device complexity increases

Engineering Contradiction:

Improveservice personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces machine learning algorithms as intermediaries that process comprehensive customer data and translate it into personalized service provider recommendations. These intermediary algorithms manage the complexity by automatically synthesizing multiple data sources (questionnaire responses, social media, demographics) and generating tailored recommendations without requiring direct complex system configuration for each customer.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system achieves universality by using a single machine learning-based platform that handles diverse customer needs across different service domains (healthcare, social services, education). This multi-functional approach provides high adaptability to individual needs while managing complexity through a unified system architecture rather than separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20220342914A1Database access system using machine learning-based relationship association

Publication Date: 2022.10.27 C3S INC

AI summary

A system includes a processor configured to perform operations. The operations include receiving responses to at least first and second questions from a user device over a network; and using a machine-learning algorithm to determine a relationship between the first and second questions based on the received responses; assign to the first and second questions, a relationship value between 0 and 1 based on the determined relationship, wherein the relationship value represents a likelihood the response to the first question is associated with the response to the second question; designate the first or second question as a critical question when a determined relationship value is greater than 0.5; and determine a need of the user based on the response of the user to the critical question. Operations further include generating and providing to the use device a list of service providers based on the determined need of the user.