Browser Task Data Ingestion for Lower User Processing Load
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Solution Overview
Problem
Users face high processing loads when managing tasks, which can prevent them from completing higher priority tasks and degrade efficiency, and existing systems lack effective methods to reduce this load by delegating tasks to representatives or third-party services.
Innovation Solution
A task facilitation service that collects data from various sources, including user interactions and external applications, to generate task recommendations and execute tasks through representatives or third-party services, using machine learning and automation to predict and manage task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users directly manage and process task data themselves, then task completion control is maintained, but user processing load increases and efficiency decreases
Solution Approach 1:
The patent introduces a task facilitation service as an intermediary between users and task execution. This service collects task data from multiple sources (user inputs, calendar applications, email applications, to-do list applications), processes it through machine learning models, and generates task recommendations. Users interact only with the recommendation interface rather than directly managing all task data processing, thereby reducing their processing load while maintaining control over task completion decisions.
Solution Approach 2:
The system enables self-service by automatically collecting task data from various applications through APIs, processing it through machine learning models, and generating task recommendations without requiring direct user intervention in the data processing pipeline. Users simply provide initial task data, and the system autonomously processes and generates recommendations, reducing the manual effort users must expend.
2Measurement precision
If task data is collected from multiple external sources, then task recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The task facilitation service implements a universal data collection mechanism that works across multiple application types (calendar applications, email applications, to-do list applications) through standardized APIs. The system uses a unified approach to collect task data from different sources, process it through common machine learning models, and generate recommendations through a single interface, thereby managing complexity while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent introduces an intermediary layer (the task facilitation service) that handles all complex interactions with external applications through standardized APIs. This intermediary abstracts the complexity of multi-source data collection, providing a unified interface for users while managing diverse data sources behind the scenes. The machine learning models further act as intermediaries to process heterogeneous data from different sources into unified task recommendations.
Data Source
AI summary
A computer-implemented method for generating tasks in a task facilitation service based on website data includes receiving website data for a website from a browser executed on a user computing device associated with a user. The method also includes processing the website data to generate a task recommendation for a task of the user and transmitting an indication corresponding to the task recommendation. When the indication is received by a computing device, the computing device is enabled to approve the task recommendation to generate a task corresponding to the task recommendation in the task facilitation service.