Mediated Representations for Personalized Device Control
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Solution Overview
Problem
Current technologies fail to effectively create personalized and dynamic representations of physical geographical spaces using data from broadcasting devices, limiting the ability to tailor user experiences and optimize services based on real-time user interactions and preferences.
Innovation Solution
A system and method that utilize mobile devices to gather and process signals from broadcasting devices, inferring user preferences and movements to generate mediated representations of environments, which can include or exclude objects and features based on user input, machine learning, and external data feeds, allowing for real-time adjustments and personalized experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mediated representations are created using broadcasting device signals, then personalized user experiences are enabled, but device complexity increases
Solution Approach 1:
The patent introduces a mediation layer that sits between broadcasting devices and triggered devices. This mediator collects signals from multiple broadcasting devices, processes them through machine learning models, and generates personalized representations for triggered devices. The mediator handles the complexity of data aggregation, pattern recognition, and representation generation, while triggered devices receive ready-to-use personalized representations without needing to implement complex processing themselves.
Solution Approach 2:
The system divides the overall functionality into separate components: broadcasting devices that emit signals, a mediation layer that processes signals and generates representations, and triggered devices that consume representations. This segmentation allows each component to be optimized independently - broadcasting devices focus on signal transmission, the mediator handles complex processing, and triggered devices focus on rendering and user interaction.
2Productivity
If real-time signal processing is performed to create dynamic representations, then user experience quality improves, but computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-computing patterns from collected signals and storing them in the mediated representation. Machine learning models are trained offline on aggregated signal data to identify patterns of interest. When a triggered device needs a representation, the system retrieves and renders pre-computed patterns rather than performing full real-time analysis, significantly reducing computational energy requirements while maintaining responsiveness.
Solution Approach 2:
Triggered devices autonomously determine their own representation needs based on their context and user preferences. The system allows triggered devices to self-select which patterns from the mediated representation are relevant to their current situation, reducing the need for continuous system-wide processing and enabling energy-efficient, on-demand representation generation.
3Measurement precision
If user preferences are inferred through machine learning, then representation accuracy improves, but data processing time increases
Solution Approach 1:
The system performs preliminary machine learning processing by continuously training models on aggregated signal data from multiple triggered devices. User preferences are inferred in advance through offline pattern recognition, and these pre-computed preference profiles are stored for rapid retrieval. When a triggered device needs personalized representations, the system quickly matches the device's context against pre-computed preference patterns rather than performing full real-time preference inference.
Solution Approach 2:
The system implements feedback loops where user interactions with representations are continuously monitored and fed back into the machine learning models. This feedback refines preference inference accuracy over time by adjusting models based on actual user behavior patterns. The feedback mechanism allows the system to progressively improve accuracy while reducing processing time, as the models become more efficient at predicting user preferences based on learned patterns.
Data Source
AI summary
System and methods are provided to create representations of geographic areas. Such representations enable users to search for items and services of interest and to quickly locate and utilize such items and services. Representations are created using user preferences thus reducing the amount of information presented to a user, i.e., user preferences control the contents of a representation. Control APIs contained within a representation may be used to control devices represented in a representation or to manufacture them using 3-D printing technologies. Methods to learn user preferences via his movements and other actions and impose a user's preferences upon an environment are shown. Some details of the invention are described by applying the invention to problems in retail marketing and figures depicting an implementation illustrate certain aspects of the invention.