Representative User Models for Delegated Task Matching
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Solution Overview
Problem
Users face high processing loads from implementing various tasks, which can prevent them from addressing higher priority tasks and degrade efficiency, leading to processing errors and task failures.
Innovation Solution
A task-facilitation service generates representative models based on user data and interactions to facilitate task delegation to representatives, using machine-learning algorithms to match users with suitable representatives and automate task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users implement various tasks themselves, then they maintain control over task execution, but their processing load increases and efficiency decreases
Solution Approach 1:
The patent introduces a representative model as an intermediary between the user and task execution. The representative model receives task requests from users, processes them autonomously, and executes tasks on behalf of users. This mediator approach transfers processing load from users to the representative model while maintaining task control through automated execution, directly resolving the contradiction between task control and processing load reduction
2Manufacturing precision
If users manage all task implementation, then they ensure task accuracy, but they experience processing errors and task failures due to overload
Solution Approach 1:
The representative model operates autonomously to execute tasks on behalf of users. It independently processes task requests, makes decisions, and completes tasks without requiring continuous user intervention. This self-service capability reduces user processing load and minimizes human errors while maintaining task accuracy through automated, consistent execution protocols
3Productivity
If users focus on high-priority tasks, then they improve overall efficiency, but they lack resources to manage lower-priority tasks
Solution Approach 1:
The patent segments task management into two distinct functions: users focus on high-priority task definition and oversight, while the representative model handles execution of both high and low-priority tasks. This segmentation allows users to concentrate cognitive resources on strategic decisions while the representative model manages operational tasks, thereby improving overall efficiency without reducing task management capacity
Data Source
AI summary
Systems and methods are presented herein for generating representative models and assigning members of a task-facilitation service to representatives based on corresponding representative models. The task-facilitation service can transmit a set of queries that when received cause a computing device to generate a set of responses. The task-facilitation service may generate a feature vector that corresponds to the set of responses. The feature vector may be used to generate a representative model that corresponds to a user of the computing device. The representative model may be usable to establish communications one or more members of the task-facilitation service. The task-facilitation service may determine a correspondence between the representative model and one or more user models that correspond to the one or more members. The task-facilitation service may receive a selection of a particular user model and facilitate a communication to a client device associated with the particular user model.