18520836. Merchant Advertisement Informed Item Level Data Predictions simplified abstract (Capital One Services, LLC)
Contents
- 1 Merchant Advertisement Informed Item Level Data Predictions
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Merchant Advertisement Informed Item Level Data Predictions - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Merchant Advertisement Informed Item Level Data Predictions
Organization Name
Inventor(s)
Joshua Edwards of Philadelphia PA (US)
Adam Vukich of Springfield VA (US)
George Bergeron of Falls Church VA (US)
Merchant Advertisement Informed Item Level Data Predictions - A simplified explanation of the abstract
This abstract first appeared for US patent application 18520836 titled 'Merchant Advertisement Informed Item Level Data Predictions
Simplified Explanation
The patent application describes a system for predicting item level data based on merchant advertisement information, detecting transaction patterns, and updating transaction records to indicate likely item level transaction information.
- Predicting item level data based on merchant advertisement information
- Detecting transaction patterns
- Retrieving and parsing merchant advertisement information to generate a price list
- Determining a number of transactions with a common payment amount reaching a threshold value
- Matching items from the price list with the common payment amount
- Updating transaction records to indicate likely item level transaction information
- Presenting likely transaction information to a user
Potential Applications
The technology described in this patent application could be applied in e-commerce platforms, retail analytics, and financial services for improving transaction processing and customer experience.
Problems Solved
This technology helps in predicting item level data accurately, detecting transaction patterns efficiently, and updating transaction records effectively, leading to better decision-making and customer satisfaction.
Benefits
The benefits of this technology include improved accuracy in predicting item level data, enhanced efficiency in detecting transaction patterns, and increased effectiveness in updating transaction records, ultimately resulting in optimized business operations.
Potential Commercial Applications
The potential commercial applications of this technology could be in e-commerce platforms, retail chains, financial institutions, and data analytics companies, where transaction processing and customer insights play a crucial role in business success.
Possible Prior Art
One possible prior art could be systems that use machine learning algorithms to predict customer behavior based on transaction data and historical patterns. Another could be software solutions that analyze advertisement data to generate pricing strategies for products.
Unanswered Questions
The patent application does not provide details on the privacy and security measures implemented in the system to protect sensitive transaction information.
What are the potential limitations or challenges in implementing this technology on a large scale?
The patent application does not address the scalability issues or potential challenges that may arise when implementing this technology in real-world scenarios with a high volume of transactions and data processing requirements.
Original Abstract Submitted
Systems as described herein may include predicting item level data based on merchant advertisement information. A transaction pattern may be detected. The merchant advertisement information may be retrieved and parsed to generate a price list. A number of transactions that each shares a common payment amount may be determined and the number may reach a threshold value. Items from the price list may be matched with the common payment amount. The transaction records may be updated to indicate likely item level transaction information. In a variety of embodiments, the likely transaction information may be presented to a user.
