Ambient Data Characterization via Crowd-Sourced Parameters
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Solution Overview
Problem
Current technologies lack the ability to effectively crowd-source and crowd-share ambient image and sound data using crowd-sourced parameters, limiting the functionality of personal companions in providing real-time information and guidance to users.
Innovation Solution
Implementing a system where multiple individuals carry sensors that persistently receive and characterize ambient data using crowd-sourced parameters, with the characterized data being shared among users, enabling personal companions to provide information and interact with users in a conversational manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple sensors persistently capture ambient data from many individuals, then the quantity and diversity of available information increases, but the complexity of processing and managing this data increases
Solution Approach 1:
The system employs automated algorithms and machine learning models that self-organize and process the ambient data without requiring manual intervention. The crowd-sourced parameters automatically tag and categorize data as it is collected, allowing the system to handle increasing data quantities through self-service processing rather than manual management.
Solution Approach 2:
The patent introduces an intermediary processing layer that uses crowd-sourced parameters as a mediator between raw ambient data and the personal companion applications. This intermediary layer pre-processes and tags data using collectively contributed parameters, reducing the complexity burden on individual devices and enabling scalable data management.
2Adaptability or versatility
If crowd-sourced parameters are used to characterize ambient data, then the adaptability and relevance of information to individual users improves, but the time required to collect and aggregate parameters from the crowd increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing crowd-sourced parameters in advance before they are needed for data characterization. Users contribute parameters during their normal usage, and these parameters are stored and ready for immediate application when ambient data needs characterization, eliminating delays.
Solution Approach 2:
The patent implements dynamic parameter updates where crowd-sourced parameters are continuously refined and updated based on new contributions from the crowd. The system transitions from static parameter sets to dynamically changing parameters that adapt over time, allowing the system to maintain high adaptability while reducing the effective collection time through iterative improvement.
3Measurement precision
If personal companions actively solicit information from users, then the quality and accuracy of user-provided data improves, but the ease of operation and user convenience decreases
Solution Approach 1:
Instead of having personal companions actively solicit information from users, the system inverts the approach by having sensors passively capture ambient data and then using crowd-sourced parameters to automatically extract meaningful information. This inversion eliminates the need for active user participation while maintaining high data quality through automated analysis.
Solution Approach 2:
The system enables self-service data collection where the ambient sensors and crowd-sourced parameter system automatically perform the work of data gathering and characterization without requiring user effort. The personal companion serves itself by autonomously processing ambient data using collectively contributed parameters, maintaining both high data quality and user convenience.
4Loss of information
If ambient data is persistently recorded and shared among many users, then the usefulness and value of the personal companion system increases, but the loss of user privacy and control over personal information increases
Solution Approach 1:
The system extracts only the essential and anonymized features from ambient data using crowd-sourced parameters, separating useful information from personally identifiable details. By taking out only the necessary characteristics needed for personal companion functionality while leaving out sensitive personal information, the system maintains information usefulness while reducing privacy risks.
Solution Approach 2:
The patent introduces privacy-preserving intermediaries that process and anonymize data before sharing it with the crowd-sourced parameter system. These intermediary layers act as mediators that protect user privacy by removing or masking personally identifiable information while retaining the essential characteristics needed for data characterization and personal companion functionality.
Data Source
AI summary
Personal companions crowd-source and/or crowd-share characterizations, and optionally raw data, from real-world, virtual and/or mixed-reality experiences. Characterizations can advantageously be stored in one or more self-evolving, structured databases, and can be organized according to objects, actions, events and thoughts. Characterizations can be weighted differently for different users, and “forgotten” over time, especially in favor of maintaining higher level characterizations. Personal companions can be used to obtain additional information, and conduct interpersonal, commercial, or other interactions or transactions.