Genetic-Based Recommendations Through Pangenetic Attribute Learning
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Solution Overview
Problem
Existing internet search engines lack the ability to provide personalized and accurate search results based on an individual's genetic and epigenetic attributes, leading to suboptimal recommendations and increased dissatisfaction in product choices.
Innovation Solution
Incorporating pangenetic attributes, such as genetic and epigenetic data, into web search engines to personalize results by linking them to webpage metadata, using methods like passive and active collaborative filtering to determine correlations between pangenetic attributes and user behavior, and integrating these attributes into search algorithms to enhance relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search algorithms are used, then the system is simple and easy to operate, but the search results lack personalization and accuracy
Solution Approach 1:
The patent segments the user profile into multiple distinct attributes including genetic attributes, epigenetic attributes, demographic attributes, and behavioral attributes. This segmentation allows the search system to process and weight different types of personalization data independently, improving search result accuracy without creating an unmanageably complex monolithic system.
Solution Approach 2:
The patent introduces genetic and epigenetic attributes as new dimensions for personalization beyond traditional demographic and behavioral data. This dimensional expansion enables more precise search results by adding biological inheritance-based personalization layers, resolving the contradiction between accuracy and complexity through structured multi-dimensional attribute handling.
2Measurement precision
If pangenetic attributes are integrated into search algorithms, then search result relevance improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing user pangenetic attributes and search query parameters before actual search execution. User profiles including genetic and epigenetic data are pre-analyzed and stored in optimized formats, allowing the search algorithm to perform faster comparisons and reduce real-time computational energy consumption while maintaining high result relevance.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the weight and importance of different pangenetic attributes based on the specific search context and user preferences. This selective parameter optimization ensures that only relevant genetic and epigenetic parameters are fully processed for each query, reducing unnecessary computational energy consumption while preserving search result relevance.
3Measurement precision
If collaborative filtering methods are used, then recommendation accuracy improves, but data processing time and system complexity increase
Solution Approach 1:
The patent uses copying by creating simplified representative models of user pangenetic profiles and item characteristics. Instead of processing complete raw genetic and epigenetic datasets for each recommendation calculation, the system uses pre-generated copy representations that capture essential personalization patterns, thereby improving recommendation accuracy while significantly reducing data processing time.
4Adaptability or versatility
If personalized search based on genetic attributes is implemented, then user satisfaction increases, but privacy concerns and data security requirements worsen
Solution Approach 1:
The patent introduces intermediary mechanisms including encrypted data storage, anonymized attribute processing, and controlled access protocols that mediate between personalized search requirements and privacy protection. Genetic and epigenetic attributes are processed through security intermediaries that prevent direct exposure of sensitive biological data while still enabling personalized search results, thus increasing user satisfaction without compromising privacy.
Data Source
AI summary
An embodiment may involve storing, by a computing device and in a database, a set of pangenetic attributes of a set of individuals, wherein the pangenetic attributes of the set are respectively and statistically associated with products; based on the statistical associations between the pangenetic attributes and the products, determining, by the computing device, product recommendations for a second set of individuals; receiving, by the computing device and from the second set of individuals, a plurality of measures of satisfaction with the product recommendations; based on the plurality of measures of satisfaction, learning, by the computing device, an association between a subset of the pangenetic attributes and a particular product; and storing, by the computing device and in the database, the learned association, wherein the learned association provides a basis for subsequent recommendations of the particular product when a subsequent individual exhibits the subset of the pangenetic attributes.