Advanced Consumer Behavior Analysis System Using Deep Learning and Sensor Integration

Abstract

The invention provides a deep learning-based market segmentation system for analyzing consumer behavior in retail environments. It integrates various sensor technologies, including LiDAR, RGB-D cameras, wearable sensors, 3D facial recognition cameras, RFID, and NFC sensors, for data acquisition. The system also utilizes power-efficient AI processors for on-device data preprocessing, which reduces latency and enhances data privacy. By employing deep learning models, the system generates real-time consumer behavior insights, allowing retailers to make informed decisions regarding store layout, promotional strategies, and inventory management. This comprehensive approach enhances customer experience, optimizes operations, and reduces reliance on cloud infrastructure, making it suitable for environments with limited connectivity and privacy concerns.

Specification

Description:[014] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[015] Referring now to the drawings, these are illustrated in FIG. 1, the deep learning market segmentation system comprises:
LiDAR and RGB-D Cameras: LiDAR sensors and RGB-D cameras are positioned within the retail environment to collect consumer foot traffic data, including in-store movement patterns, dwell times, and traffic hotspots. This data is used to determine consumer navigation patterns and store layout effectiveness.
Wearable Sensors: Wearable devices, such as smartwatches with biometric sensors, are employed to collect psychographic and demographic data from consumers. The collected data includes heart rate variability, which provides real-time consumer feedback regarding marketing stimuli, thereby aiding in determining consumer preferences and emotional responses.
[016] In accordance with another embodiment of the present invention, 3D facial recognition cameras are installed throughout the retail space to capture facial attributes and determine consumer demographics such as age and gender. Additionally, these cameras detect consumer sentiment by analyzing facial expressions, which are then fed into deep learning models to refine market segmentation.
[017] In accordance with another embodiment of the present invention, RFID and NFC sensors are used to track product interactions, providing insights into which products consumers handle and how long they engage with them. This data helps in segmenting consumers based on product interest and engagement.
[018] In accordance with another embodiment of the present invention, Battery-operated sensors such as RFID readers and wearable sensors incorporate ARM Cortex-M processors for on-device machine learning, allowing initial data segmentation and categorization before transmitting to a central server. The Syntiant NDP neural decision processor is used for efficient, low-power preprocessing and segmentation tasks directly on IoT devices, reducing bandwidth consumption and enabling real-time insights as illustrated in figure 2.
[019] In accordance with another embodiment of the present invention, the LiDAR and RGB-D cameras capture consumer movement, while 3D facial recognition cameras capture facial data. Wearable sensors collect biometric data, and RFID/NFC sensors gather product interaction information. Data is initially processed on-device using ARM Cortex-M processors and Syntiant NDP neural decision processors. The preprocessing step categorizes data into relevant consumer behavior metrics, thereby reducing data volume before transferring to the central server.
[020] The preprocessed data is transmitted to a central server, where deep learning algorithms analyze the consumer movement, interaction, and sentiment data. The system segments consumers into clusters based on behavior, demographic factors, and psychographic insights. The resulting market segments provide actionable insights for retail managers, such as optimal store layout recommendations, targeted promotional strategies, and inventory management adjustments.
[021] The primary advantage of the invention lies in its ability to perform real-time market segmentation using advanced sensor technologies, leading to more accurate and dynamic insights into consumer behavior. By integrating LiDAR, RGB-D cameras, wearable sensors, and facial recognition, the system captures a comprehensive view of consumer interactions within a retail environment. This comprehensive approach allows businesses to make timely and data-driven decisions, ultimately improving the customer experience and increasing sales.
Another significant advantage is the use of power-efficient AI processors for on-device data processing. This localized data processing reduces reliance on cloud infrastructure, minimizes latency, and enhances data privacy. As a result, the system is well-suited for environments where network connectivity is limited or where data privacy is a major concern. The reduction in bandwidth usage also makes the system more cost-effective and efficient.
[022] The present invention provides an advanced system for consumer behavior analysis and market segmentation in retail environments by utilizing a combination of deep learning techniques and various sensor technologies. The real-time data acquisition and localized processing capabilities enable retailers to make informed decisions swiftly and effectively, ultimately leading to enhanced customer satisfaction and increased operational efficiency. The invention's use of power-efficient AI processors further distinguishes it by providing a secure and cost-effective solution, making it highly suitable for diverse retail settings.
[023] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[024] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A deep learning market segmentation system comprising:
a. LiDAR and RGB-D cameras for capturing consumer movement patterns within a retail environment;
b. Wearable sensors for collecting psychographic and demographic data of consumers, including heart rate variability;
c. 3D facial recognition cameras for determining age, gender, and consumer sentiment;
d. RFID and NFC sensors for capturing product interaction data;
e. Power-efficient AI processors, including ARM Cortex-M processors and Syntiant NDP neural decision processors, for on-device preprocessing and segmentation.
2. The system as claimed in claim 1, wherein the ARM Cortex-M processors are configured to perform on-device machine learning for battery-operated sensors to segment consumer data locally before transmission.
3. The system as claimed in claim 1, wherein the Syntiant NDP neural decision processors are configured for preprocessing and segmentation of consumer data in IoT devices to reduce cloud bandwidth usage.
4. A method for market segmentation in a retail environment comprising:
a. Capturing consumer movement patterns using LiDAR and RGB-D cameras;
b. Collecting psychographic and demographic data using wearable sensors;
c. Determining consumer sentiment using 3D facial recognition cameras;
d. Tracking product interactions using RFID and NFC sensors;
e. Performing on-device preprocessing and segmentation using ARM Cortex-M processors and Syntiant NDP neural decision processors;
f. Analyzing the preprocessed data using deep learning models to generate market segmentation insights.
5. The system as claimed in claim 1, further comprising a central server configured to receive preprocessed consumer data from on-device processors and perform deep learning analysis for generating market segmentation insights.
6. The system as claimed in claim 1, wherein the wearable sensors are further configured to collect biometric data including body temperature and stress levels, thereby providing additional psychographic insights into consumer behavior.
7. The system as claimed in claim 1, wherein the 3D facial recognition cameras are configured to continuously monitor facial expressions of consumers to determine changes in sentiment over time, enabling dynamic segmentation based on emotional responses.
8. The system as claimed in claim 1, wherein the RFID and NFC sensors are configured to track specific product interactions, including the duration of interaction, to assess consumer interest in particular items.
9. The system as claimed in claim 1, further comprising a communication module configured to transmit real-time alerts to retail managers based on predefined consumer behavior thresholds, enabling immediate decision-making.
10. The method as claimed in claim 4, further comprising the step of providing targeted promotional content to consumers based on their segment classification, thereby enhancing personalized marketing efforts.

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