Intelligent Transaction Assistant for Shopping Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face challenges in optimizing their shopping and spending experiences due to limited knowledge of available discounts, sales, and offers, making it difficult to make informed purchasing decisions, especially with the overwhelming amount of information available from various sources.
Innovation Solution
An intelligent transaction optimization assistant using machine learning operations that learns from previous experiences and social network data to provide optimized shopping strategies, including receiving media data, historical transaction data, and location information, and offering lists of actions to enhance the shopping experience based on predefined goals such as financial cost and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers rely on traditional information sources for shopping decisions, then they have access to basic product information, but they lack comprehensive knowledge of available discounts, sales, and offers
Solution Approach 1:
The patent introduces an intelligent assistant system as an intermediary between consumers and transaction information sources. This mediator aggregates discount, sales, and offer data from multiple sources, processes it through machine learning operations, and delivers personalized recommendations to consumers, thereby resolving the information gap without requiring consumers to directly manage complex data collection systems
Solution Approach 2:
The system enables self-service by allowing consumers to input their preferences, constraints, and shopping goals, after which the intelligent assistant autonomously performs information gathering, analysis, and recommendation generation without requiring manual intervention or complex user configuration
2Loss of time
If consumers manually research all available discounts and offers, then they can make informed decisions, but they spend excessive time and effort
Solution Approach 1:
The system performs preliminary actions by proactively gathering, processing, and organizing discount and offer information before consumers need to make purchasing decisions. The machine learning model continuously learns from transaction data and user behavior to pre-calculate optimized shopping strategies, saving consumers time while maintaining decision accuracy
Solution Approach 2:
The system implements feedback mechanisms where consumer responses, purchasing patterns, and satisfaction metrics are continuously fed back into the machine learning model. This feedback loop enables the system to refine its recommendations over time, improving both the speed of information delivery and the precision of shopping decisions through iterative learning
3Adaptability or versatility
If an intelligent assistant collects extensive user data for personalization, then it provides accurate recommendations, but it increases system complexity and data processing requirements
Solution Approach 1:
The patent applies local quality by tailoring the data collection and processing intensity to individual user needs and contexts. The machine learning model dynamically adjusts the level of personalization and data analysis based on user preferences, transaction history, and specific shopping scenarios, providing high adaptability while optimizing computational resources by focusing processing power only where needed
Data Source
AI summary
Embodiments for using an intelligent transaction optimization assistant by a processor. One or more actions to enhance a transaction experience of one or more users may be provided according to one or more selected constraints learned via a machine learning operation from previous transaction experiences, user behavior relating to the one or more previous transaction experiences, transaction experiences shared amongst entities associated with a social network, or a combination thereof.