Spatial Offer Selection Using Real-Time Product Recognition
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Solution Overview
Problem
Existing online advertisements lack personalization and relevance, leading to ad blindness, user disengagement, and reduced conversion rates due to oversaturation, as they fail to consider individual customer preferences and browsing behaviors.
Innovation Solution
Implementing mixed-reality and spatial computing technologies to capture real-time images, identify products, and use machine learning algorithms to select and present personalized offers based on user interaction data, dynamically updating the selection model based on real-time user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If generic online advertisements are displayed to all customers, then the volume of promotional content increases, but the relevance and personalization to individual customers deteriorates
Solution Approach 1:
The patent applies local quality by customizing advertisement content based on individual customer characteristics, browsing history, and preferences. Each customer receives tailored promotional content that matches their specific interests and needs, rather than generic ads. This is achieved through machine learning models that analyze customer data and generate personalized ad variations, ensuring high relevance while maintaining scalable delivery across many customers.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting advertisement parameters such as content, timing, placement, and format based on real-time customer behavior data. The system modifies ad parameters to optimize personalization for each customer segment, allowing the same promotional campaign to be delivered in multiple customized forms simultaneously, thus resolving the contradiction between volume and personalization.
2Adaptability or versatility
If advanced data analytics and machine learning algorithms are implemented to personalize ads, then the quality and relevance of advertisements improves, but the system complexity increases
Solution Approach 1:
The patent implements universality by creating a multi-functional platform that handles data collection, analysis, model training, ad generation, and delivery within a single integrated system. The machine learning infrastructure serves multiple purposes: analyzing customer behavior, predicting preferences, generating personalized ads, and optimizing delivery timing. This consolidates complexity into a universal system that performs multiple functions efficiently, reducing the need for separate specialized systems.
Solution Approach 2:
The system applies self-service through automated machine learning models that continuously learn from customer data and automatically generate optimized advertisement content without manual intervention. The models self-adjust parameters, select content, and personalize ads based on real-time data processing, reducing the operational complexity burden on human operators while maintaining high personalization quality.
3Quantity of substance
If the volume of ads displayed on digital marketplaces increases, then the coverage and visibility of promotional content improves, but customer engagement and satisfaction deteriorates due to ad fatigue
Solution Approach 1:
The patent applies partial action by selectively displaying advertisements only to customers who have a predicted interest in the promoted products or services. Rather than showing ads to all users, the system uses machine learning to identify and target only the relevant subset of customers, reducing overall ad volume while maintaining high engagement rates. This selective approach prevents ad fatigue among uninterested users while ensuring sufficient visibility for interested segments.
Solution Approach 2:
The system implements dynamics by continuously adapting advertisement delivery based on real-time customer feedback and behavior changes. The machine learning models dynamically adjust targeting criteria, ad frequency, and content based on evolving customer preferences, ensuring that ads remain relevant and engaging. This dynamic approach allows the system to maintain high engagement while scaling coverage to new customer segments over time.
Data Source
AI summary
A device may capture real-time images of a product. Capturing may include using an outward-facing sensor on a device. A device may identify the product by using an identification machine learning model. A device may obtain user interaction data corresponding to previous images displayed on the device. A device may identify available offers. A device may identify a set of offers from the available offers using an offer selection machine learning model trained to automatically select offers presentable to users through devices. The offer selection machine learning model may be trained using a dataset of historical interaction data and corresponding offers. A device may present the set of offers through the device. A device may monitor in real-time user interaction with the set of offers. A device may update the offer selection machine learning model according to the real-time user interaction with the set of offers.