Specialized Language Model via Prompt Analysis

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Solution Overview

Problem

Large language models (LLMs) are ill-suited for deployment on singular devices due to their size and complexity, leading to increased WAN bandwidth utilization and processing latency, especially in IoT and edge computing environments where only a small set of discrete tasks are required.

Innovation Solution

The creation of specialized language models through prompt analysis, where prompt-response pairs are classified and used to train compact models capable of performing specific tasks, allowing these models to be deployed on edge devices for local execution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a full-scale large language model is deployed, then task capability and versatility are improved, but device resource requirements and complexity increase making edge deployment infeasible

Engineering Contradiction:

Improvetask capabilityVSAvoidmodel complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the full-scale LLM's functionality by extracting and training separate specialized language models for specific tasks (e.g., code generation, natural language processing). Each specialized model handles a discrete task independently, dividing the monolithic complex system into manageable modular components that can be deployed on edge devices with limited resources.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts only the necessary components and capabilities needed for specific tasks from the full-scale LLM. By using prompt-response pairs to identify and extract task-specific patterns, the system creates streamlined specialized models that retain only the essential functionality required for particular tasks, eliminating unnecessary complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

2Productivity

If a cloud-hosted large language model is used, then comprehensive task performance is improved, but WAN bandwidth utilization and processing latency increase

Engineering Contradiction:

Improvetask performanceVSAvoidprocessing latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent performs preliminary action by pre-training specialized language models offline using curated prompt-response pairs for specific tasks. These pre-trained models are then deployed to edge devices, enabling them to perform tasks locally without requiring real-time cloud communication, thus eliminating latency associated with WAN bandwidth utilization.

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If prompt-response pairs are collected and classified for training, then model specialization accuracy is improved, but data processing time and computational resources increase

Engineering Contradiction:

Improvespecialization accuracyVSAvoidtraining time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent applies partial action by collecting and classifying only the subset of prompt-response pairs relevant to specific tasks rather than processing the entire dataset. This selective approach focuses computational resources on task-specific data, achieving high specialization accuracy while reducing overall training time and resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250200298A1Language model specialization via prompt analysis

Publication Date: 2025.06.19 CISCO TECHNOLOGY INC

AI summary

In one implementation, a device obtains prompt-response pairs of prompts for input to a language model and their corresponding responses from the language model. The device classifies each of the prompt-response pairs as relating to one or more tasks. The device trains a specialized language model using the prompt-response pairs related to a particular task. The device causes the specialized language model to be deployed for use to perform the particular task.