Ad-Hoc Pseudo-Completion Tasks with Approval Checkpoints

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Solution Overview

Problem

Existing systems require a user to completely offload a computing task to another user, relinquishing control over the task, which can lead to incomplete or incorrect task execution.

Innovation Solution

A system utilizing natural language processing (NLP) and robotic process automation (RPA) to detect task keywords, train a machine learning model, and freeze action execution until approval is given by the task-giver, ensuring control is maintained during task offloading.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a user completely offloads a computing task to another user, then the task execution speed and user availability are improved, but the task completion reliability and accuracy deteriorate

Engineering Contradiction:

Improvetask execution speedVSAvoidtask completion reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system records user inputs and freezes action execution before finality actions are executed, preparing the task for potential review and correction. This preliminary recording and freezing mechanism ensures that tasks can be verified before completion, maintaining reliability while enabling offloading.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback to the task-giver about the offloaded task's progress and status, allowing the task-giver to monitor and intervene if necessary. This feedback loop maintains task reliability even when the original user is not actively engaged.

Inventive Principle:

Principle #23Feedback

2Ease of operation

If a user completely offloads a computing task to another user, then the ease of operation is improved, but the control over task execution is lost

Engineering Contradiction:

Improvetask offloading convenienceVSAvoidcontrol mechanism complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system freezes action execution before finality actions, creating a control checkpoint that allows task-givers to review and approve task completion. This preliminary freezing mechanism maintains control without significantly complicating the offloading process.

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If action execution is frozen before finality action, then the task accuracy is improved, but the task completion time increases

Engineering Contradiction:

Improvetask execution accuracyVSAvoidtask completion time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system freezes execution only at critical finality actions rather than throughout the entire task process. This partial freezing approach maintains accuracy where it matters most while minimizing delays in overall task completion.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12412134B2Ad-hoc pseudo completion task

Publication Date: 2025.09.09 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Embodiments detect a task keyword related to an offloaded task of a task-giver, train a machine learning (ML) model using the task keyword, determine that the task keyword is similar to a historical task by utilizing the trained ML model, record a user input in response to determining that the task keyword is similar to the historical task, freeze an action execution during a pseudo action before a finality action is executed, provide the recorded user input to the task-giver and requesting approval from the task-giver, and execute the offloaded task using robotic process automation (RPA) in response to receiving approval from the task-giver.