20240086412.TECHNIQUES FOR PERSONALIZING APP STORE RECOMMENDATIONS simplified abstract (apple inc.)

TECHNIQUES FOR PERSONALIZING APP STORE RECOMMENDATIONS

Organization Name

apple inc.

Inventor(s)

Jayasimha R. Katukuri of San Jose CA (US)

Peter Leong of Mountain View CA (US)

Chandrasekar Venkataraman of Palo Alto CA (US)

Rabi S. Chakraborty of San Jose CA (US)

Hardik Vala of Sunnyvale CA (US)

TECHNIQUES FOR PERSONALIZING APP STORE RECOMMENDATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240086412 titled 'TECHNIQUES FOR PERSONALIZING APP STORE RECOMMENDATIONS

Simplified Explanation

The technique described in the abstract is for providing software application recommendations to a user of a computing device. Here is a simplified explanation of the abstract:

• Receiving a request for software application recommendations from the computing device.
• Identifying a user profile associated with the user.
• Accessing software application profiles managed by the server computing device.
• Analyzing the user profile against a subset of software application profiles to identify recommendations.
• Associating the recommendations with the software applications.
• Displaying the recommendations on the computing device.

Potential Applications

This technology could be applied in e-commerce platforms to recommend software applications to users based on their preferences and usage patterns.

Problems Solved

This technology solves the problem of users being overwhelmed by the vast number of software applications available and helps them discover new applications that are relevant to their needs.

Benefits

The benefits of this technology include personalized recommendations, improved user experience, and increased user engagement with software applications.

Potential Commercial Applications

One potential commercial application of this technology could be in app stores or software marketplaces to enhance the user experience and increase app downloads.

Possible Prior Art

One possible prior art for this technology could be personalized recommendation systems used in e-commerce websites or streaming platforms to suggest products or content to users based on their preferences and behavior.

How does the system ensure user privacy and data security when analyzing user profiles?

The system must have robust security measures in place to protect user data and ensure that sensitive information is not compromised during the analysis process.

How does the system handle feedback from users to improve the accuracy of software application recommendations?

It is important for the system to have mechanisms in place to collect feedback from users on the recommended applications and use this information to continuously refine and improve the recommendation algorithm.

Original Abstract Submitted

disclosed herein is a technique for providing software application recommendations to a user of a computing device. the technique can include: (1) receiving, from the computing device, a request for at least one software application recommendation, (2) identifying, among a plurality of user profiles, a user profile associated with the user, (3) accessing a plurality of software application profiles (saps), wherein each sap of the plurality of saps is associated with a respective software application managed by the server computing device, (4) analyzing the user profile against a subset of the plurality of saps to identify, among the respective software applications associated with the subset of the plurality of saps, at least one software application to recommend, (5) associating the at least one software application recommendation with the at least one software application, and (6) causing the computing device to display the at least one software application recommendation.