Smart Camera Assistant for Personalized Highlight Detection
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Solution Overview
Problem
Existing smart camera systems struggle to accurately determine points of interest and generate meaningful highlight files that align with user preferences and interests.
Innovation Solution
The assistant system employs machine-learning algorithms to analyze sensory data from client systems, determining points of interest and personalizes highlight files based on user profiles, preferences, and relationship data to enhance relevance and interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional smart camera systems capture and store all sensory data, then the quantity of captured moments is high, but the relevance and meaningfulness of highlight files deteriorates due to inability to accurately determine points of interest
Solution Approach 1:
The patent replaces traditional rule-based or manual highlight detection mechanisms with machine-learning algorithms that automatically analyze sensory data, user profiles, and contextual information to identify points of interest. This substitution enables more accurate determination of meaningful moments without requiring manual intervention or simple threshold-based filtering.
Solution Approach 2:
The system dynamically adjusts the parameters for identifying points of interest by incorporating multiple data sources including user profiles, preferences, relationship data, and contextual information. This multi-parameter approach allows the highlight detection to adapt to individual user characteristics and situations, improving both accuracy and relevance.
2Adaptability or versatility
If the system personalizes highlight files using user profiles and preferences, then the relevance to user interests is improved, but the device complexity increases due to additional data processing requirements
Solution Approach 1:
The patent implements a multi-functional machine-learning system that simultaneously processes sensory data, user profiles, preferences, relationship data, and contextual information through integrated algorithms. This universal approach consolidates multiple processing functions into a unified system, reducing overall complexity compared to separate specialized systems for each data type.
Solution Approach 2:
The system automatically generates personalized highlight files by having the machine-learning algorithms self-adjust to user preferences and behaviors over time. The system serves itself by continuously learning from user interactions and automatically updating its understanding of user interests, eliminating the need for manual configuration or complex external management systems.
3Productivity
If the system processes and analyzes all sensory data in real-time, then the productivity of generating highlight files is high, but the use of energy increases due to continuous computational processing
Solution Approach 1:
The patent applies partial processing by having the machine-learning system focus computational resources on analyzing only the most relevant portions of sensory data based on preliminary filters and user-specific parameters. Instead of uniformly processing all data at full computational intensity, the system selectively applies deep analysis only where needed, reducing overall energy consumption while maintaining highlight generation speed.
Solution Approach 2:
The system performs preliminary processing of sensory data by pre-processing incoming data streams, pre-fetching relevant user profile information, and pre-identifying potential points of interest before full highlight generation. This preliminary action prepares data in advance, enabling faster final processing with reduced energy requirements during the critical highlight generation phase.
Data Source
AI summary
In one embodiment, a method includes accessing sensory data captured by cameras, identifying people in a field of view of the cameras based on facial recognition of the sensory data, detecting actions of one or more of the people based on the sensory data, generating media files with each being associated with one or more of a recording of at least one of the people or at least one of the determined actions, and sending instructions for presenting one or more of the media files to a client system.