Suggested Actions From Context Data for Proactive AI Reminders

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Solution Overview

Problem

Existing artificial intelligence systems lack the ability to proactively assist users in remembering actions or items, failing to effectively utilize context data for suggesting relevant tasks at a later time.

Innovation Solution

An artificial intelligence system that captures and processes context data to identify semantic entities, ranks and stores them, and generates suggested actions based on user preferences and schedules, which are then displayed at contextually relevant times.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing AI systems provide reactive assistance only, then system complexity remains low, but user memory assistance capability is insufficient

Engineering Contradiction:

Improveuser memory assistance capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by capturing and storing context data during the first time interval before the user needs reminders. The machine-learned models process and rank semantic entities in advance, preparing suggested actions that will be presented during the second time interval. This proactive approach enables memory assistance without requiring complex real-time processing when reminders are needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system segments the operation into distinct time intervals: a first time interval for capturing and processing context data, and a second time interval for presenting suggested actions. This temporal segmentation allows the system to handle complexity in manageable phases rather than attempting to process everything simultaneously, resolving the contradiction between enhanced capability and system complexity.

Inventive Principle:

Principle #1Segmentation

2Productivity

If the system processes and stores semantic entities with high precision, then task completion efficiency improves, but processing time and computational resources increase

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs semantic entity processing and ranking in advance during the first time interval. By pre-processing context data and storing the ranked semantic entities, the system eliminates the need for time-consuming processing when the user needs reminders during the second time interval, thus improving productivity without increasing overall processing time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system processes only the necessary portion of context data to identify and rank the top semantic entities, rather than processing all possible data exhaustively. This partial action approach maintains sufficient task completion efficiency while minimizing processing time and computational resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250217713A1Systems and Methods for Generating and Providing Suggested Actions

Publication Date: 2025.07.03 GOOGLE LLC

AI summary

An artificial intelligence system can save or otherwise retain data associated with semantic entities as they are recognized over time. For example, the saved semantic entities can be ranked, sorted, categorized, prioritized etc. based on the user's preferences and/or the user's plans or schedule. The artificial intelligence system can generate one or more suggested actions for a user that are related to one or more of the identified semantic entities.