Group Targeting System Using Sensor Fusion for Contextual Output
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Solution Overview
Problem
Conventional systems for providing targeted information, such as advertisements, are limited to individual-based features like gender, age, and emotional state, failing to account for group dynamics and context, thus unable to effectively target information to groups of individuals.
Innovation Solution
A computer-implemented method and system that identifies groups of individuals using sensors, determines individual-based features, and generates outputs based on group characteristic information, enabling targeted content for groups such as couples or families by analyzing spatial, facial, and speech features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems provide targeted information based on individual-based features only, then the system complexity remains low, but the adaptability and versatility of targeted categories are limited
Solution Approach 1:
The patent segments the analysis into two distinct levels: individual-based feature determination (age, gender, emotional state) and group characteristic information determination (group type, group emotion, group activity). This segmentation allows the system to handle both individual and group dimensions separately, thereby expanding the versatility of targeted categories while managing system complexity through modular processing stages.
Solution Approach 2:
The patent transitions from a single-dimension approach (individual features only) to a multi-dimensional approach by adding the group level as another dimension. The system now operates in both individual space and group space, enabling targeted information to be generated based on either dimension or their combination, thus significantly expanding the adaptability of targeted categories.
2Loss of information
If conventional systems analyze only single individual features, then the measurement precision for individual characteristics is maintained, but the loss of information regarding group context and dynamics increases
Solution Approach 1:
The patent merges individual-based features from multiple sensors (skeleton data, facial recognition, speech recognition) to determine group characteristic information. By combining these different data sources and analysis levels, the system recovers group context information that would be lost in individual-only analysis, while the merging process itself is managed through integrated sensor fusion and coordinated processing.
Solution Approach 2:
The patent introduces group characteristic information as an intermediary layer between individual sensor data and final targeted information output. This intermediary aggregates and synthesizes individual features into group-level characteristics (group type, emotion, activity), thereby preserving group context while maintaining a structured processing pipeline that manages complexity through intermediate representation.
3Productivity
If the system determines both individual-based features and group characteristic information, then the productivity and effectiveness of targeted information generation is improved, but the use of energy and computational resources increases
Solution Approach 1:
The patent performs preliminary determination of individual-based features (age, gender, emotional state) for each detected individual before proceeding to group characteristic information determination. This preliminary action prepares the data in advance, allowing the group-level analysis to build upon already-processed individual features, thereby improving the overall productivity and effectiveness of targeted information generation while optimizing computational energy usage through staged processing.
Data Source
AI summary
There is provided a computer-implemented method for generating an output with respect to a group of individuals. The method includes: identifying the group of individuals amongst a plurality of individuals in an area being monitored by one or more sensors; determining, for each individual in the group of individuals, one or more individual-based features associated with the individual based on sensing data obtained from the one or more sensors; determining a group characteristic information associated with the group of individuals based on the one or more individual-based features determined for each individual in the group; and generating the output based on the group characteristic information determined for the group of individuals. There is also provide a corresponding system for generating an output with respect to a group of individuals.