Activity Summary Generation from Plural Device Signals
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Solution Overview
Problem
Computing devices generate a wealth of activity signals that are difficult to utilize effectively and efficiently, requiring users to manually extract information from multiple logic components, which is burdensome and error-prone.
Innovation Solution
A computer-implemented technique that captures activity signals from diverse logic components and automatically generates summary documents by identifying activity clusters using a rules-based engine or machine-trained model, storing information in a graphical data structure, and presenting a graphical user interface for user control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually extract information from each logic component in a standalone manner, then information can be obtained from individual components, but the process is burdensome and error-prone
Solution Approach 1:
The patent combines multiple logic components into a unified activity monitoring system that automatically collects data from diverse sources (applications, operating system components, devices) through a centralized framework, eliminating the need for manual extraction from each component separately
Solution Approach 2:
The system implements self-service by automatically generating activity summaries without requiring user intervention. The activity monitoring logic components autonomously capture signals, the processor automatically processes and clusters activity information items, and the system produces summary documents without manual data extraction
2Loss of information
If activity signals are captured from a diverse collection of logic components, then comprehensive activity information can be obtained, but the complexity of processing and organizing the data increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: activity signal capture from multiple logic components, extraction of activity information items, clustering of related items, and generation of summary documents. This modular architecture manages complexity while preserving comprehensive information
Solution Approach 2:
The patent introduces an intermediary processing layer that receives activity signals from diverse logic components and transforms them into structured activity information items. This intermediary layer standardizes data from different sources before further processing, reducing overall system complexity
3Loss of information
If manual data extraction is performed from multiple logic components, then detailed information can be obtained, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary action by having logic components continuously capture and store activity signals in the background before user requests. Activity information items are pre-processed and organized into clusters in advance, so when a summary is needed, the system can quickly retrieve and generate results without time-consuming manual extraction
4Productivity
If automated summary generation is implemented, then productivity is improved, but the complexity of the system increases
Solution Approach 1:
The system achieves self-service through automated activity monitoring and summary generation. The processor automatically clusters activity information items based on relationships between them and generates summary documents without user intervention, improving productivity while keeping the automation logic contained within the system
Data Source
AI summary
A computer-implemented technique is described herein for receiving activity signals from plural logic components running on one or more computing devices. Each activity signal includes an activity information item (AII) that describes an activity performed by a subject (e.g., a user), or to be performed by the subject, as recorded by a logic component. The technique stores AIIs extracted from the activity signals in a graph data structure. The technique then: determines, by interrogating the graph data structure, one or more activity clusters within a span of time, each activity cluster pertaining to a group of AIIs associated with a same encompassing project; generates one or more summary documents based on the identified activity cluster(s); and sends the summary documents to an output device.