Objective Management System for Task Inference
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Solution Overview
Problem
Personal assistant technologies are limited in their ability to assist users across a wide array of tasks and domains, requiring users to provide specific queries and wasting computing resources due to their task-specific and inflexible nature.
Innovation Solution
A framework that infers user tasks and objectives by clustering user activity data, allowing for the delegation of tasks to applications or services, such as bots, to efficiently complete objectives and track task completion, while also inferring user context for personalized assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If personal assistant software provides query-based assistance, then users can obtain specific information, but computing resources are wasted due to multiple refined queries and marginal relevance
Solution Approach 1:
The system performs preliminary clustering of user activity data to infer tasks and objectives before users submit queries. By pre-organizing data into task clusters with associated websites and services, the system eliminates the need for multiple iterative queries and directly presents relevant information, reducing both computing resource waste and information loss.
2Extent of automation
If personal assistant technologies are hard coded and task specific, then they can automatically perform tasks, but they are inflexible and incapable of assisting across wide arrays of tasks and domains
Solution Approach 1:
The system creates a universal framework that clusters user activity data across diverse tasks and domains into standardized task templates. Each task cluster contains generic elements (websites, services, actions) that can be applied across multiple domains. This allows the system to automatically perform tasks in any domain by matching user behavior patterns to the universal task structures, achieving both automation and versatility.
Solution Approach 2:
The system dynamically adapts to new tasks and domains by continuously clustering user activity data and updating task clusters. Rather than relying on static hard-coded rules, the system evolves its task understanding based on observed user behaviors, enabling it to automatically assist with new types of tasks while maintaining versatility across existing domains.
3Measurement precision
If users provide specific queries to personal assistant software, then they can obtain information, but they must seek suitable websites and services using multiple queries
Solution Approach 1:
The system pre-clusters user activity data into task clusters that associate specific tasks with relevant websites, services, and actions. When a user engages in a task, the system immediately retrieves the pre-organized cluster information and presents relevant websites and services without requiring the user to submit multiple queries or search manually, thus maintaining information precision while eliminating time loss.
Data Source
AI summary
In some implementations, a first set of user activity data is received from a plurality of sensors where the first set of user activity data corresponds to a plurality of users. A task list of an objective is extracted from the received first set of user activity data based on patterns formed in the first set of user activity data in association with the plurality of users pursuing the objective. Based on determining a second set of user activity data indicates pursuit of the objective by a user, it is determined that at least a task of the task list is uncompleted by the user based on comparing the second set of user activity data to the task. Content corresponding to the task is provided to a user device associated with the user based on determining the task is uncompleted.