Application Store Discovery Using LLM-Filtered App Summaries
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Solution Overview
Problem
Users face challenges in discovering specific applications from large application stores due to overwhelming search results and insufficient information about application functionalities, leading to difficulty in selecting appropriate applications.
Innovation Solution
An application store utilizes generative models, such as large language models (LLMs), to generate filtered application information that provides insights into applications, including summaries, highlights, and answers, enhancing user discovery by distinguishing between similar applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword searching is used to find applications, then users can input search terms to locate applications, but the search results present an overwhelming number of titles that are difficult to navigate
Solution Approach 1:
The patent introduces an intermediary summarization layer between the search results and the user. Instead of directly presenting numerous application titles, the system generates concise summaries that capture key information about each application, serving as a mediator that reduces the information overload while preserving search accuracy.
Solution Approach 2:
The system extracts essential information from application descriptions, reviews, and metadata to create condensed summaries. By taking out only the most relevant features and highlights, the system reduces the volume of information presented to users while maintaining the ability to accurately identify relevant applications.
2Loss of information
If individual application listings are browsed to reveal developer information, then users can access detailed information about each application, but this requires investigating each application individually which is time-consuming
Solution Approach 1:
The system performs preliminary summarization of application information before users need to evaluate it. By pre-generating concise summaries that include key features, highlights, and lowlights, the system prepares the information in advance, allowing users to quickly compare applications without investing time in individual investigations.
Solution Approach 2:
The patent segments application information into distinct components such as key features, highlights, lowlights, and summaries. This segmentation allows users to quickly scan different aspects of multiple applications simultaneously, rather than reading through complete descriptions of each application individually, thus reducing evaluation time while maintaining information completeness.
3Loss of information
If comprehensive application information is provided for each application, then users have sufficient information to make decisions, but the information may be insufficient to distinguish between similar applications
Solution Approach 1:
The system applies local quality by providing different types of summarized information for different aspects of applications. Instead of uniform detailed descriptions, the system generates specific summaries highlighting key features, positive highlights, and negative lowlights, allowing users to quickly identify differentiating factors between similar applications while maintaining sufficient information for decision-making.
Data Source
AI summary
In one example, a computing system comprises a memory that stores instructions, and processing circuitry that executes the instructions to: generate one or more user interface elements for an application store, wherein the application store hosts a plurality of applications where each application is associated with respective application information; generate intermediary application information by at least providing the application information as input to a first machine learning module, wherein the intermediary application information is a compressed version of the application information and lacks particular subject matter from the application information; generate filtered application information by at least providing the intermediary application information as input to a second machine learning module; receive a request for one or more applications; and responsive to receiving the request, output the filtered application information along with an indication of the one or more applications for presentation within the application store.