On-Device Search Engine Clustering Native App Content
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Solution Overview
Problem
Current search engines on mobile devices lack an efficient method to organize and retrieve application content items, such as contacts, messages, and documents, by clustering them into relevant topics or tasks based on user interactions, leading to suboptimal search results.
Innovation Solution
A computer-implemented method using a search engine on a user device to generate cluster feature-vector representations from native application data and context information, allowing for the identification of topics or tasks associated with these items and providing a user interface with selectable controls for filtered search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search is used to retrieve application content, then the search engine can identify relevant results based on search term frequency, but the search results lack organization by topics or tasks and fail to capture user interaction context
Solution Approach 1:
The patent segments application content into clusters based on topics or tasks by analyzing user interactions. Instead of treating all content uniformly, the system divides content into meaningful groups (e.g., messages, calls, emails) and further organizes them by topic clusters, enabling more precise search results while maintaining manageable system complexity through structured data organization
Solution Approach 2:
The patent introduces an intermediary layer of topic clusters between the search query and the actual application content. This mediator (topic cluster) translates user interactions into organized categories, allowing the search engine to retrieve content through a structured intermediate representation that improves relevance without directly managing the full complexity of all application data
2Adaptability or versatility
If application content from multiple native applications is indexed and searched, then comprehensive search coverage is achieved, but the system complexity increases due to handling diverse data formats and contexts
Solution Approach 1:
The patent implements a universal indexing mechanism that handles diverse data from multiple native applications (messaging, email, calendar, contacts) through a common feature-vector representation system. This multi-functional approach allows the search engine to process different data types uniformly, achieving comprehensive search coverage while managing complexity through standardized processing pipelines
Solution Approach 2:
The patent transforms diverse application data into a standardized parameter space using feature vectors. By changing the representation parameters of different data types into a common mathematical space, the system can uniformly index and search across all application content without needing separate processing logic for each data type, thus reducing overall system complexity
3Measurement precision
If the search engine processes and stores cluster feature-vector representations for all application content, then accurate topic-based search is enabled, but the storage requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and indexing application content into topic clusters before actual search queries are executed. The system proactively organizes content by analyzing user interactions and creating topic clusters in advance, so that when searches occur, the pre-organized data can be retrieved quickly without needing to process all content from scratch, thus reducing search processing time while maintaining high topic identification accuracy
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer-storage medium, for using a search engine implemented on a user device to identify topics or tasks associated with native application content. The method may include actions of receiving a set of data that is generated by the native application and that includes (i) native application content, and (ii) context information associated with the native application content, indexing the data on the user device, and then identifying one or more tasks based on the indexed data.