LLM Task Dependency Planning for Intelligent Assistants

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Solution Overview

Problem

Existing intelligent automated assistants struggle to efficiently manage complex user requests, provide intuitive user interfaces, and optimize power usage in handling natural language inputs and task management.

Innovation Solution

Incorporating large language models (LLMs) to enhance user-device interactions by leveraging task dependency criteria, intuitive user interface controls, and context-aware suggestions, enabling asynchronous and synchronous task performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing intelligent automated assistants handle complex user requests, then task management capability is improved, but power consumption increases

Engineering Contradiction:

Improvetask management capabilityVSAvoidpower consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system segments task management into distinct phases: natural language input processing, task identification, task breakdown into subtasks, execution planning, and result delivery. Each phase is handled by specialized components that process only when needed, reducing overall power consumption while maintaining comprehensive task management capability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis of user requests to identify task types and complexity levels before full processing. This allows the system to prepare execution plans in advance and optimize resource allocation, reducing power consumption during actual task execution while maintaining high adaptability.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If existing intelligent automated assistants process natural language inputs, then user interaction is improved, but processing time increases

Engineering Contradiction:

Improveuser interactionVSAvoidprocessing time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system replaces traditional rule-based natural language processing with machine learning models that can interpret and respond to user inputs more efficiently. This substitution enables faster processing of diverse language patterns while maintaining ease of user interaction through more accurate intent recognition.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system processes only the essential components of natural language inputs required for task identification, rather than analyzing every aspect of the input. This selective processing reduces time loss while maintaining ease of operation by focusing computational resources on critical interpretation elements.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If existing intelligent automated assistants manage multiple tasks, then productivity is improved, but system complexity increases

Engineering Contradiction:

Improvetask execution efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements a hierarchical task management structure where complex tasks are broken down into subtasks, which can further be divided into smaller action items. This nested organization allows the system to manage multiple tasks efficiently by handling them at appropriate levels of abstraction, improving productivity while managing system complexity through structured decomposition.

Inventive Principle:

Principle #7Nested doll (Nesting)

Solution Approach 2:

The system introduces intermediary components that mediate between user requests and task execution, including task planners, schedulers, and coordinators. These intermediaries manage the complexity of handling multiple tasks by providing structured interfaces and coordination mechanisms, enabling high productivity without proportionally increasing overall system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250278175A1Systems and techniques for incorporating large language models into intelligent automated assistants

Publication Date: 2025.09.04 APPLE INC

AI summary

An example process includes receiving a speech input from a user, obtaining a plurality of tasks based on the speech input, and determining, based on the plurality of tasks, an ordered task list associated with a set of task dependency criteria, wherein each task of the ordered task list includes a respective task dependency criteria. The process further includes displaying a representation of the ordered task list and performing each task of the ordered task list based on the set of task dependency criteria. The process further includes, while performing each task, updating the display of the representation of the ordered task list to indicate completion status of each task of the ordered task list, and displaying at least one graphical object corresponding to a respective task of the ordered task list.