Topic-Based Activity Collections for Faster Context Recall
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing software applications lack the ability to record and restore the context of user activity, requiring users to manually organize and recall information, which is inefficient and burdensome due to the sheer amount of data generated.
Innovation Solution
A system that automatically organizes user activity into collections based on shared topics by analyzing content captures, such as screenshots, using machine learning models to detect and extract relevant text information, and assign topics, allowing for intuitive retrieval of context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual organization and keyword search are used to retrieve information, then users can find specific content, but users spend excessive time recalling and manually organizing information
Solution Approach 1:
The system automatically captures screenshots, extracts text using OCR, identifies entities, and organizes content into collections without user intervention. The system serves itself by autonomously performing organization tasks that would otherwise require manual user effort, thereby resolving the contradiction between reducing time loss and increasing automation.
Solution Approach 2:
The system performs preliminary actions by capturing screenshots and organizing content into collections before users need to retrieve information. By pre-organizing content with extracted entities and topics, the system eliminates the need for users to manually recall and organize information later, thus reducing time loss while maintaining high automation.
2Reliability
If comprehensive content capture is performed to preserve user activity context, then context retrieval is improved, but device processing power is consumed
Solution Approach 1:
The system extracts only the essential and relevant information from screenshots using OCR and entity recognition, rather than processing and storing entire images or all visual data. By extracting text, entities, and topics, the system maintains high context retrieval accuracy while significantly reducing the processing power and energy required compared to comprehensive content capture.
Solution Approach 2:
The system creates simplified textual representations (copies) of visual content through OCR extraction, storing text and extracted entities rather than the original images. This copying approach preserves the essential contextual information needed for retrieval while consuming far less device processing power and storage resources than storing complete high-resolution screenshots.
3Loss of information
If existing search features are used to retrieve content, then specific information can be found, but the context of the activity is lost
Solution Approach 1:
The system merges multiple pieces of contextual information including screenshots, extracted text, identified entities, topics, and metadata into unified collections. By combining these diverse elements, the system preserves complete activity context without requiring separate complex systems for each type of information, thus reducing overall system complexity while preventing information loss.
Solution Approach 2:
The system creates a universal collection structure that can store and retrieve various types of content (screenshots, text, entities, topics) in a single integrated framework. This multi-functional approach allows the system to preserve diverse context information without requiring separate complex storage and retrieval mechanisms for each content type, thereby reducing system complexity while preventing information loss.
Data Source
AI summary
The techniques disclosed herein provide a system for automatically organizing content captures of user activity into collections based on a shared topic. Due to the significant portion of daily life that occurs via personal computing devices, there is increasing need for helpful user experiences to enhance productivity and engagement. Namely, those directed to assisting a user in managing and recalling their past activity. As such, the presented system retrieves content captures depicting a moment of interest. Accordingly, the system performs a visual analysis of each content capture to determine a semantic context and assign one or more topics. This is accomplished by utilizing a pair of machine learning models to identify and extract relevant text information. The extracted text information is then analyzed by a topic classification model to assign the one or more topics. The system further includes graphical user interfaces for viewing and managing collections of content captures.