Intermediate Server Matching for Privacy-Preserving User Data Vectors
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Solution Overview
Problem
Existing systems that collect and process behavioral and demographic data on service provider devices introduce vulnerabilities to user privacy and data security, lacking transparency and user control, and expose individuals to potential harm.
Innovation Solution
Generating multidimensional vectors directly on portable terminal devices, incorporating sensors and data processing capabilities, ensures secure data generation and transmission, minimizing exposure to external threats and maintaining user control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If behavioral and demographic data are collected and processed on service provider devices, then personalized recommendations and user engagement are enhanced, but user privacy and data security are compromised
Solution Approach 1:
Instead of collecting raw behavioral and demographic data on service provider devices and then processing it, the patent inverts the approach by having user devices generate multidimensional vectors locally from the raw data first, then transmit only these vectors to the service provider. This inversion ensures that personal information never leaves the user's device in an identifiable form, eliminating privacy vulnerabilities while preserving the ability to generate personalized recommendations through vector-based analysis.
Solution Approach 2:
The patent extracts only the essential features from raw behavioral and demographic data by transforming them into multidimensional vectors that capture user preferences and behaviors without containing personally identifiable information. This extraction process separates the useful analytical content from the sensitive personal data, allowing service providers to access pattern information while users retain control over their personal information.
2Measurement precision
If detailed behavioral data is aggregated and analyzed on remote servers, then personalized recommendations are improved, but data security risks and unauthorized access vulnerabilities increase
Solution Approach 1:
The patent inverts the traditional data flow by performing the data transformation and feature extraction process on user devices rather than on remote servers. Raw behavioral data is converted into multidimensional vectors locally, and only these transformed vectors are transmitted to service providers. This inversion maintains personalization accuracy through sophisticated vector analysis while eliminating security risks associated with storing and processing detailed behavioral data on remote servers.
Solution Approach 2:
The patent introduces multidimensional vectors as an intermediary representation between raw behavioral data and personalized recommendations. These vectors serve as a mediator that preserves the essential information needed for accurate personalization while removing personally identifiable information. The vectors act as a secure interface that allows service providers to access user preference patterns without direct access to sensitive personal data.
3Adaptability or versatility
If user personal information is transmitted to service providers for service delivery, then service personalization is enabled, but exposure to data breaches and malicious access occurs
Solution Approach 1:
The patent extracts only the necessary preference and behavior patterns from user personal information by transforming them into multidimensional vectors. This extraction process removes personally identifiable information while retaining the essential characteristics needed for service personalization. Service providers receive and process only these anonymized vectors, eliminating data breach risks associated with handling sensitive personal information while maintaining the ability to deliver personalized services.
Solution Approach 2:
The patent fundamentally changes the parameter representation from raw personal information to multidimensional vector space. By transforming user data into a different parameter space (vectors with multiple dimensions representing different aspects of user behavior and preferences), the system enables sophisticated personalization through mathematical operations on vectors while ensuring that the original personal information parameters never leave the user's device in an identifiable form.
Data Source
AI summary
Communication equipment and system architecture to facilitate the continuous collection, analysis, and secure transfer of user profile data. An intermediate server connects portable terminals to merchant and service provider servers for improved privacy, security, and optimized data transactions using multidimensional vectors. Portable terminal and server enhancements allow for continuous data collection and communication, enabling the regular generation of multidimensional vectors from current user data. User profile data originate from portable terminals, resulting in data that is regularly and almost continuously changing. The system and method enable the continuous collection and regular updating of user profile data, enhancing efficiency across all applications and systems reliant on such data. Portable terminals are equipped with sensors and processing capabilities, allowing for integrated data collection. This system enables the collection, analysis, and synthesis of user data to produce multidimensional vectors representing various aspects of user behavior, preferences, and activities. These vectors have broad applications, including personalized recommendations, contextual awareness, and targeted advertising. Portable terminals facilitate transactions between users and companies without disclosing personal information until a contract is established. An intermediary server, represented by the management server, facilitates user-profile-based interactions between users and service providers without revealing personal information on the service site. The service provider sends to the intermediate management server Market Persona profile information—which could be in the form of natural language Market Persona information or in the form of a multidimensional Market Persona vector with appropriate dimension tags and provider ID.