Representative User Models for Task Assignment and Load Reduction
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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 to connect users with suitable representatives, using feature vectors and machine-learning models to predict tasks, recommend actions, and facilitate task execution through third-party service providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users implement various tasks themselves, then task completion is achieved, but processing load increases and efficiency degrades
Solution Approach 1:
The patent introduces a task-facilitation service as an intermediary between users and task execution. This service includes components such as a task recommendation system, proposal generation system, and coordination system that mediate the task implementation process. Users delegate tasks to this intermediary service, which then manages task assignment, coordination with third-party service providers, and execution monitoring, thereby reducing user processing load while maintaining task completion.
Solution Approach 2:
The system enables self-service through automated task management capabilities. The task-facilitation service automatically recommends tasks, generates proposals, coordinates execution, and manages resources without requiring continuous user intervention. This automation allows the system to serve itself in managing task implementation, reducing the energy users would otherwise expend on manual task management.
2Reliability
If users manage all task implementation, then control over tasks is maintained, but time consumption increases and higher priority tasks are delayed
Solution Approach 1:
The patent extracts time-consuming task management functions from the user's direct control and transfers them to the task-facilitation service. The service handles task recommendation, proposal generation, coordination, and execution monitoring, extracting these functions so users don't need to invest time in them. This allows users to maintain control over important decisions while freeing up time for higher priority tasks.
Solution Approach 2:
The system performs preliminary actions by automatically recommending tasks before users need to execute them, generating proposals in advance, and pre-coordinating resources. This preliminary automation of task management activities reduces the time users would otherwise spend on these functions, allowing them to focus on higher priority tasks while the system handles preliminary task preparation and coordination.
3Productivity
If processing load is high, then more tasks can be attempted, but processing errors and task failures increase
Solution Approach 1:
The task-facilitation service acts as an intermediary that manages task execution quality. It includes systems for recommending appropriate tasks, generating detailed proposals, coordinating with third-party service providers, and monitoring execution. This intermediary layer ensures tasks are properly managed and executed, reducing errors and failures even as the number of tasks increases, thereby maintaining reliability while scaling productivity.
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.