18217612. COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS simplified abstract (FUJITSU LIMITED)
Contents
- 1 COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS - 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
COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS
Organization Name
Inventor(s)
COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18217612 titled 'COMPUTER-READABLE RECORDING MEDIUM STORING RULE FORMATION SUPPORT PROGRAM, RULE FORMATION SUPPORT METHOD, AND RULE FORMATION SUPPORT APPARATUS
Simplified Explanation
The patent application abstract describes a process that involves analyzing transaction history information to determine attribute selection frequencies for users, comparing these frequencies to those of a target user, and integrating the data to create or update a model for presenting attributes to the target user.
- Obtaining transaction history information for each user
- Determining attribute selection frequencies for a plurality of users and a target user
- Identifying attributes with significant differences in selection frequencies
- Integrating data to create or update a model for presenting attributes to the target user
Potential Applications
This technology could be applied in personalized recommendation systems, targeted marketing campaigns, and user interface design.
Problems Solved
This technology helps in understanding user preferences and tailoring recommendations or content accordingly, leading to improved user engagement and satisfaction.
Benefits
The benefits of this technology include enhanced user experience, increased user engagement, and more effective personalized recommendations.
Potential Commercial Applications
Commercial applications of this technology include e-commerce platforms, streaming services, social media platforms, and online advertising companies.
Possible Prior Art
One possible prior art could be the use of collaborative filtering techniques in recommendation systems, where user preferences are inferred based on similar users' behavior.
Unanswered Questions
This article does not address the specific methods or protocols in place to ensure user data privacy and security.
What are the computational requirements for implementing this technology at scale?
The article does not provide information on the computational resources needed to process large volumes of transaction data for multiple users.
Original Abstract Submitted
A process includes obtaining, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user, specifying an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency, and when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.
