GAI Interface Engine for Scalable Multi-App Integration

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Solution Overview

Problem

Existing technologies face challenges in widespread adoption of generative artificial intelligence (GAI) due to user resistance and issues like AI hallucination, as well as the difficulty in scaling GAI-based systems across multiple applications and users without increasing system size linearly.

Innovation Solution

The implementation of an interface engine that manages communications between applications and generative artificial intelligence models, utilizing components like an icon handler, context switcher, state tracker, and contextual data fetcher to facilitate seamless interactions, reduce user input burden, and enhance system scalability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If GAI models are integrated into multiple applications, then functionality and versatility are improved, but system complexity increases linearly

Engineering Contradiction:

ImproveGAI integration across applicationsVSAvoidsystem size

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal GAI interface engine that serves multiple applications (social media, messaging, search, etc.) through a single standardized framework. The engine handles diverse GAI model types (text, image, audio) and deployment scenarios (cloud, edge, on-device) through unified abstractions, eliminating the need for separate integration code in each application.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The interface engine acts as an intermediary layer between applications and GAI models. It manages communication protocols, handles context switching between applications, coordinates state tracking across multiple services, and abstracts the complexity of different GAI model implementations, allowing applications to interact with GAI through simple standardized interfaces.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If GAI models are deployed across multiple applications and users, then service coverage is improved, but scalability challenges increase

Engineering Contradiction:

Improvedeployment scopeVSAvoidsystem scalability

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system segments GAI functionality into independent, modular components within the interface engine. Each application interacts with GAI through isolated interface instances, and the engine manages context switching between applications without creating linear complexity growth. This modular architecture enables horizontal scaling across multiple applications and users.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The engine dynamically adjusts operational parameters based on deployment context, handling different GAI model types (text, image, audio) and deployment scenarios (cloud, edge, on-device) through configurable parameter changes rather than structural modifications, enabling flexible scaling.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If GAI models are used to answer user questions, then information retrieval is improved, but AI hallucination and user resistance increase

Engineering Contradiction:

Improveinformation retrieval accuracyVSAvoidAI hallucination

Core Design Contradiction:

Loss of informationVSReliability

Solution Approach 1:

The interface engine implements feedback mechanisms that monitor GAI responses and user interactions. It tracks conversation context across applications and provides feedback loops for correcting hallucinations, managing user expectations, and improving response accuracy through continuous learning from user behavior patterns.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The engine performs preliminary context gathering and verification before presenting GAI responses to users. It pre-processes queries to identify potential hallucination risks, retrieves relevant contextual information from multiple applications in advance, and prepares structured responses that reduce the likelihood of misleading information.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If GAI interface components are added to manage communications, then system functionality is improved, but user input burden increases

Engineering Contradiction:

Improvecommunication managementVSAvoiduser input burden

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The interface engine automatically manages context switching between applications, tracks user state across different services, and coordinates GAI interactions without requiring explicit user input for each transition. The system self-manages the complexity of multi-application communication while presenting a simplified interface to users.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250077237A1GAI to app interface engine

Publication Date: 2025.03.06 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Embodiments of the disclosed technologies include, responsive to a first use of a first application by a first user, configuring, in a first prompt, at least one instruction based on first application context data and first user context data. The first prompt is stored in a memory that is accessible to the first application and a second application. Via the second application, first output of a generative artificial intelligence (GAI) model is presented to the first user. Based on the first output of the GAI model, at least one second use of the first application by the first user, or at least one first use of a third application by the first user, is configured.