Nodal Data Structure for Personalized Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current engineering processes lack a universal standard for data accumulation, translation, and transformation across disparate systems, leading to inefficiencies in data communication and utilization, resulting in fragmented data that hinders personalized responses and recommendations.
Innovation Solution
A computer-implemented method using a machine-learning model to interpret user queries, generate data queries, and present personalized responses by leveraging a nodal data structure with identifiers corresponding to nouns and verbs in a shared knowledge language, facilitating data integration and access across various systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is accumulated across disparate computing infrastructures without a universal standard, then data volume and connectivity increase, but data fragmentation and incompatibility worsen
Solution Approach 1:
The patent implements a universal data standard that enables data from disparate computing infrastructures to be exchanged and utilized across different systems. This standard acts as a common interface that allows multiple data sources to be integrated without requiring system-specific adaptations, thereby maintaining data compatibility while accommodating increased data volume.
Solution Approach 2:
The patent introduces an intermediary layer (the universal data standard) that mediates between disparate data sources and end users. This intermediary translates and harmonizes data from different infrastructures, enabling seamless communication and exchange while preventing data fragmentation from undermining system interoperability.
2Adaptability or versatility
If data is fragmented across disparate systems, then system autonomy is maintained, but personalized responses and recommendations deteriorate
Solution Approach 1:
The universal data standard enables contextual data to be accessed and utilized across systems while preserving system autonomy. Each system can maintain its independence while the standard provides a common language for data exchange, allowing personalized responses to be generated without requiring centralization of data or loss of system independence.
3Device complexity
If traditional data management approaches are used, then system simplicity is maintained, but recommendation quality and personalization deteriorate
Solution Approach 1:
The patent changes the fundamental parameter of data organization by implementing a universal standard with standardized data structures and identifiers. This transformation enables precise matching and retrieval of contextual data, thereby improving recommendation accuracy without requiring overly complex management systems. The standard itself provides the organizational framework that simplifies data access while enabling personalized responses.
Data Source
AI summary
Disclosed herein are methods and systems for providing individualized responses and recommendations based on a shared knowledge language. A method of receiving a user query for a personalized response associated with a profile; interpreting the query by executing a machine-learning model; generating a data query corresponding to the user query by executing the machine-learning model, the data query configured for execution in a computer model comprising one or more nodes having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; receiving a first node of the computer model, wherein the first node is associated with the profile and generated based at least in part on an application accessed by the profile; presenting the personalized response, wherein the personalized response comprises an indication of the first node of the computer model.