Ambient Personal Agent Privacy Gating for Contextual Service Matching
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Solution Overview
Problem
Ambient computing environments pose a risk of exposing private user information without prior privacy or security arrangements, compromising user privacy.
Innovation Solution
A method involving a personal intelligent agent that analyzes user preferences, associates them with context indicators, determines exposable preferences, and broadcasts anonymized indicators over the ambient computing environment, while monitoring for satisfaction of those preferences without prior interaction between parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user preferences are disclosed in ambient computing environment, then service personalization is improved, but user privacy is compromised
Solution Approach 1:
The patent segments user preferences into exposable and non-exposable categories based on privacy levels. The intelligent agent divides preference data into discrete units that can be selectively disclosed, allowing service personalization through exposable preferences while protecting non-exposable ones, thus resolving the contradiction between personalization and privacy protection.
Solution Approach 2:
The patent changes the parameter of preference disclosure from binary (disclose/not disclose) to graded (multiple privacy levels). By implementing a graded privacy scheme where preferences have different exposability levels, the system enables hyper-personalization by matching context with appropriate privacy levels, allowing service providers to access preferences at acceptable privacy levels while maintaining user privacy control.
2Object-affected harmful factors
If prior security protocols are implemented, then user privacy is protected, but system complexity and interaction overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-classifying user preferences into exposable and non-exposable categories before any interaction occurs. The intelligent agent performs this classification in advance, so that when service providers need preferences, they can directly use the pre-organized exposable preferences without engaging in complex security negotiations, thus protecting privacy while avoiding protocol complexity.
Solution Approach 2:
The patent implements self-service where the intelligent agent autonomously manages preference disclosure without requiring prior security arrangements between user and service providers. The agent independently determines which preferences to expose based on context matching, eliminating the need for complex inter-party security protocols while maintaining privacy protection.
3Productivity
If all user preferences are exposed, then service matching efficiency is improved, but information security is compromised
Solution Approach 1:
The patent applies local quality by allowing different preferences to have different disclosure properties based on their sensitivity. Instead of uniformly exposing or hiding all preferences, the system selectively exposes only those preferences that are appropriate for the given context, improving service matching efficiency for non-sensitive preferences while maintaining information security for sensitive ones.
Solution Approach 2:
The intelligent agent acts as an intermediary between user preferences and service providers. It filters and transforms raw preference data into context-appropriate exposable preferences, enabling efficient service matching by providing relevant preferences to providers while blocking sensitive information, thus resolving the contradiction between matching efficiency and information security.
Data Source
AI summary
A method of operating a personal intelligent agent in an ambient computing environment, comprising receiving input; analyzing input to derive a user personal preference; associating the personal preference with a first context indicator; determining whether the personal preference is exposable; responsive to determining that the personal preference is exposable, storing the preference with the associated context indicator; detecting when the agent enters a detectable context and responsively creating a second context indicator; determining if there is a match between the second and the first context indicator; retrieving the exposable personal preference associated with the context indicator; creating an anonymous preference indicator comprising the exposable personal preference with the matched context; emitting the preference indicator over the ambient computing environment; and monitoring the ambient computing environment to detect any broadcast message indicating ability to satisfy the preference shown in the preference indicator.