Task Prompt System Aligning Tasks to User Thought States

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Solution Overview

Problem

Conventional systems fail to efficiently schedule tasks based on users' thought states, physiological conditions, and context, leading to inefficient task completion.

Innovation Solution

A task prompt generation system that uses sensors to collect context data, categorizes time segments into thought states, and maps tasks to suitable time segments for optimal completion, utilizing an AI neural network to generate prompts for tasks during designated times.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional systems schedule tasks without considering user thought states, then task scheduling is simple and straightforward, but task completion efficiency is low

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the user's day into discrete time segments and categorizes each segment into different thought states (e.g., focused, relaxed, transition). Tasks are also segmented and mapped to specific thought states. This segmentation allows the system to match tasks with appropriate thought states without requiring complex continuous analysis, thereby improving task completion efficiency while maintaining manageable system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces sensors as intermediaries that collect physiological and contextual data, and an AI neural network as an intermediary that processes this data to determine thought states. These intermediaries bridge the gap between raw user data and task scheduling decisions, enabling efficient task completion without requiring the scheduling system itself to directly analyze complex physiological signals.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If the system collects and analyzes context data from multiple sensors, then task scheduling accuracy based on thought states is improved, but system complexity and data processing requirements increase

Engineering Contradiction:

Improvethought state detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The AI neural network serves as a universal processor that handles multiple sensor inputs (physiological sensors, contextual sensors) and performs multiple functions: raw data processing, thought state classification, and task matching. This multi-functional approach improves measurement precision while consolidating complexity into a single processing component rather than requiring separate systems for each function.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system transforms complex multi-dimensional sensor data into simplified thought state parameters (categorical states like focused, relaxed, transition). This parameter transformation reduces data complexity while preserving the essential information needed for accurate task scheduling, thereby improving measurement precision without proportionally increasing system complexity.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If tasks are mapped to specific thought states using AI neural network, then task completion efficiency is maximized, but computational requirements and processing time increase

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary categorization of time segments into thought states based on sensor data before task assignment. The AI neural network pre-processes and stores thought state classifications for upcoming time segments, so when tasks need to be scheduled, the system only needs to match tasks with pre-categorized states rather than performing full analysis in real-time. This reduces computational energy consumption while maintaining high task completion efficiency.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If the system generates prompts for tasks at designated time segments, then user awareness and task completion rate improve, but system complexity and prompt management overhead increase

Engineering Contradiction:

Improvetask completion rateVSAvoidprompt management ease

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system automatically generates and manages task prompts based on the mapped thought states and scheduled tasks. Once the AI neural network establishes the mapping between tasks and thought states, the system autonomously generates prompts at the appropriate time segments without requiring manual intervention. This self-service approach improves task completion rate while reducing the operational burden on users, as the system handles prompt management automatically.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20220318763A1Methods and systems for generating and outputting task prompts

Publication Date: 2022.10.06 TOYOTA RESEARCH INSTITUTE INC

AI summary

A method for generating and outputting a prompt for performing a task in a designated time segment is provided. The method includes obtaining, from a plurality of sensors, context data associated of the user related to time segments, categorizing each of the time segments into one of a plurality of thought states based on the context data, mapping a task from a task dataset associated with the user into one of the plurality of thought states, and generating a prompt for performing the task during a designated time segment of the time segments, the designated time segment corresponding to the one of the plurality of thought states to which the task is mapped.