Sensor Data Analysis System for Real-Time Customer Profile Synthesis
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Solution Overview
Problem
Current data analytics systems face challenges in scalability and efficiency when integrating multiple systems and analyzing large volumes of data to provide timely and meaningful insights, particularly in retail environments, where understanding customer preferences and behavior is crucial for effective decision-making.
Innovation Solution
The development of computer-implemented systems and methods for sensor data analysis that utilize machine learning to collect and process video and audio data from various sources, creating complex profiles of individuals to offer personalized experiences by detecting facial data, mood, and context, enabling real-time interaction and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data analytics systems collect large amounts of customer data, then data volume increases, but the ability to quickly synthesize and analyze the data deteriorates
Solution Approach 1:
The patent segments the complex data analysis process into multiple hierarchical levels: individual sensor data processing, customer profile construction, pattern recognition, and decision support. This segmentation allows parallel processing of different data types and reduces the computational burden at each level, enabling fast synthesis of large volumes of customer data through distributed processing across multiple systems.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data into structured formats, pre-constructing customer profiles from historical data, and pre-identifying relevant patterns before actual analysis is needed. This preparation work is done in advance so that when large volumes of new customer data arrive, the system can quickly synthesize and analyze them without starting from scratch.
2Adaptability or versatility
If multiple data analytics systems are deployed at different locations, then system coverage increases, but integration complexity deteriorates
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple types of sensor data (video, audio, biometric) from various sources using the same processing pipeline. This universal approach allows different location-specific systems to be deployed with consistent data structures and processing logic, reducing integration complexity while maintaining broad adaptability across diverse environments and data types.
Solution Approach 2:
The system introduces intermediary components including standardized data exchange protocols, central coordination servers, and adaptive integration layers that mediate between location-specific analytics systems. These intermediaries translate and harmonize data from different sources, enabling seamless integration of multiple deployed systems without requiring complex point-to-point connections between each system.
3Measurement precision
If detailed customer profiles are constructed from multiple data sources, then personalization accuracy improves, but data processing time increases
Solution Approach 1:
The system applies partial action by selectively processing only the most relevant customer data attributes needed for specific personalization tasks rather than analyzing all available data. This selective approach maintains high personalization accuracy for targeted applications while reducing overall processing time by avoiding unnecessary computation on irrelevant data points.
Solution Approach 2:
The system performs preliminary actions by pre-constructing customer profiles from historical multi-source data, pre-identifying relevant patterns and preferences, and pre-ranking data importance before real-time personalization is needed. This advance preparation enables the system to quickly retrieve and apply pre-analyzed customer insights without performing time-consuming data synthesis during actual personalization operations.
Data Source
AI summary
Sensor data analysis may include obtaining video data, detecting facial data within the video data, extracting the facial data from the video data, detecting indicator data within the video data, extracting the indicator data from the video data, transforming the extracted facial data into representative facial data, and determining a mood of the person by associating learned mood indicators derived from other detected facial data with the representative facial data. The analysis may include determining that the representative facial data is associated with a complex profile, and determining a context regarding the person within the environment by weighting and processing the determined mood, at least one subset of data representing information about the person of the complex profile, and the indicator data. The analysis may include determining a user experience for the person, and communicating the determined user experience to a device associated with the person.