Trait-Based Family Database for Health Risk Analysis
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Solution Overview
Problem
Existing social networking and genealogical technologies do not effectively facilitate extended family connections or provide useful analysis of data related to family traits, leading to difficulties in communicating and understanding familial characteristics and health risks.
Innovation Solution
A method that involves identifying family members, recording and updating their information in a database, sharing access to the database, analyzing the data, and providing summaries of analyzed information to family members, including potential medical risks and preventative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social networking technologies are used to link friends and family, then basic connections are established, but extended family connections cannot be made easily and elaborate analysis of family data is not provided
Solution Approach 1:
The system segments family connections into different levels (immediate family, extended family, distant relatives) and organizes them in a hierarchical structure. This allows users to navigate and manage extended family connections systematically, improving ease of operation while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces an intermediary analysis layer that automatically processes family data, identifies relationships, and provides insights. This intermediary system bridges the gap between basic social networking and comprehensive family connection, making extended family linking easier and more effective.
2Loss of information
If genealogical databases are used to track ancestry, then ancestral information is stored, but living descendants cannot easily make connections or communicate with each other
Solution Approach 1:
The system merges genealogical database functionality with social networking capabilities, creating an integrated platform that both stores ancestral information and facilitates living descendant connections. This combination eliminates the barrier between record-keeping and active communication.
Solution Approach 2:
The system provides feedback mechanisms that automatically notify living descendants when new family members are added to the database or when relationship information is updated. This keeps descendants informed and engaged, facilitating continuous communication and connection.
3Quantity of substance
If gene mapping is used to explore genetic information, then genetic data is collected, but practical applications for determining individual health propensities are too limited
Solution Approach 1:
The system transforms raw genetic data into meaningful health parameters and risk assessments. By changing the representation of genetic information from raw sequences to interpreted health propensities, the system makes the data versatile for practical health applications while maintaining comprehensive genetic coverage.
Solution Approach 2:
An intermediary analysis layer processes genetic data and translates it into practical health insights, bridging the gap between data collection and application. This intermediary system enables versatile health applications by automatically interpreting genetic information in context.
4Loss of information
If existing gene databases are used, then genetic information is stored, but the information is not adequately analyzed or provided in useful forms relating to pre-existing conditions
Solution Approach 1:
The system implements feedback loops that continuously analyze genetic data and provide updated health risk assessments to users. This automated feedback process transforms static data storage into dynamic, useful information delivery, reducing the complexity burden on users while maintaining high information utility.
Solution Approach 2:
The system performs self-service analysis by automatically processing and interpreting genetic data without requiring users to manually analyze complex datasets. The system serves itself by generating meaningful health insights from raw data, eliminating the complexity barrier while maximizing information utility.
Data Source
AI summary
In some examples a method of diagnosing family traits is describe. the method may include, identifying family members and recording information regarding the identified family members in a first database. In some examples, the identified family members may include at least some living persons from different immediate families. sharing access to the first database with living identified family members. The method may further include updating the recorded information regarding the identified family members and analyzing the updated information regarding the identified family members. In some example, the updated information may include at least one trait of at least some of the identified family members. Some examples of the method may further include providing a summary of the analyzed information.