LLM Multimodal Response Generation with Contextual Content Interleaving

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Solution Overview

Problem

Large language models generate multi-modal responses where multimedia content is not contextualized with textual content, leading to inefficient user interaction and unnecessary consumption of computational resources, especially on devices with limited display or input capabilities.

Innovation Solution

A system that generates multi-modal responses using large language models, arranging multimedia content logically with textual content and tailoring it for specific tasks, such as slide decks, by interleaving relevant multimedia content with textual content, reducing the need for user rearrangement and conserving computational resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If multimedia content is pre-pended or post-pended to textual content in multi-modal responses, then the structure is simple to generate, but the user experience deteriorates and computational resources are wasted due to lack of contextualization

Engineering Contradiction:

Improveease of generating multi-modal responseVSAvoiduser interaction efficiency

Core Design Contradiction:

Ease of manufactureVSEase of operation

Solution Approach 1:

The patent segments multimedia content and textual content into distinct units that can be independently processed and then strategically combined. Each piece of multimedia content is associated with specific contextual tags that link it to relevant textual portions, allowing the system to generate responses by assembling these segmented elements in contextually appropriate positions rather than简单地 pre-pending or post-pending them.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces contextual tags as intermediary elements that mediate between multimedia content and textual content. These tags provide the linkage information needed to position multimedia content appropriately within the response structure, enabling intelligent placement without requiring complex manual arrangement while maintaining contextual coherence.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If all multimedia content is included in the multi-modal response regardless of context, then completeness is improved, but computational resource consumption increases and latency increases

Engineering Contradiction:

Improvecompleteness of informationVSAvoidcomputational resource consumption

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The patent applies partial action by selectively including only those multimedia content items that are contextually relevant to the user's query and the generated textual response. The system evaluates contextual tags associated with each multimedia item and includes only those that serve the specific information need, avoiding the waste of computational resources on transmitting and rendering irrelevant content while maintaining information completeness for the given context.

Inventive Principle:

Principle #16Partial or excessive action

3Speed

If multimedia content is not contextualized with textual content, then generation speed is improved, but user experience deteriorates and additional user inputs are required for rearrangement

Engineering Contradiction:

Improveresponse generation speedVSAvoidduration of human-to-computer interaction

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The patent performs preliminary action by pre-associating contextual tags with multimedia content during the generation process. This preliminary contextualization allows the system to quickly assemble responses by placing pre-tagged multimedia items in appropriate positions based on the generated text, maintaining fast generation speeds while eliminating the need for post-generation rearrangement by users.

Inventive Principle:

Principle #10Preliminary action

4Adaptability or versatility

If the multi-modal response format is fixed with pre-pended or post-pended multimedia, then device compatibility is improved, but adaptability to different use cases deteriorates

Engineering Contradiction:

Improveadaptability to different applicationsVSAvoidcomplexity of response structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces dynamics by making the response structure flexible and adaptive rather than fixed. The system dynamically determines the placement and inclusion of multimedia content based on contextual tags and the specific application scenario. This dynamic approach allows the same generation system to adapt to different use cases (e.g., slide decks, chat responses, educational content) while maintaining manageable complexity through the use of standardized contextual tagging mechanisms.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250217585A1Generating tailored multi-modal response(s) through utilization of large language model(s) and/or other generative model(s)

Publication Date: 2025.07.03 GOOGLE LLC

AI summary

Implementations relate to generating tailored multi-modal response(s) through utilization of large language model(s) (LLM(s)). In some implementations, processor(s) of a system can: receive natural language (NL) based input indicative of a request for a set of slides to be generated, generate a multi-modal response, using an LLM, that is responsive to the NL based input, the multi-modal response comprising a generated set of slides, and cause the multi-modal response to be rendered at the client device of the user. In additional or alternative implementations, the NL based input can be indicative of a request for assistance with completing a particular task. In these implementations, the processor(s) can generate the multi-modal response comprising assistive content for assisting the user in performing the particular task. In various implementations, the LLM can be fine-tuned prior to receiving the NL based input.