Generative Prompt Completion Using Probabilistic Placeholders

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Solution Overview

Problem

Existing generative models often require additional user inputs to provide detailed parameters for content generation tasks, leading to inefficient use of computational resources and prolonged human-computer interaction.

Innovation Solution

A generative model processes free-form natural language inputs to recognize missing parameters and insert placeholders, retrieving corresponding placeholder data with probability values to generate a customized query, reducing the need for extensive training data and computational resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If the system prompts the user to provide detailed parameters for content generation, then the quality and appropriateness of generated content improves, but the complexity of operation and interaction time increase

Engineering Contradiction:

Improvequality of generated contentVSAvoidcomplexity of user interaction

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system performs preliminary analysis of the user's free-form input to identify missing parameters before the actual content generation task. By proactively determining what parameters are needed and inserting placeholders, the system prepares the ground for efficient generation without requiring the user to anticipate or provide all details upfront.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system serves itself by automatically analyzing its own input requirements. The first generative model examines the user's free-form input and autonomously identifies which parameters are missing, inserting placeholders without human intervention. This self-service capability eliminates the need for users to manually provide detailed specifications.

Inventive Principle:

Principle #25Self-service

2Reliability

If the system requests multiple additional user inputs to complete parameter specification, then the completeness of generation parameters improves, but the time and computational resources consumed increase

Engineering Contradiction:

Improvecompleteness of parametersVSAvoidinteraction duration

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The first generative model acts as an intermediary between the user's free-form input and the second generative model's content generation task. It translates the informal user input into a structured initial query with placeholders, bridging the gap without requiring multiple back-and-forth user interactions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary parameter identification and placeholder insertion before the actual content generation begins. This advance preparation ensures parameter completeness is achieved in a single user input rather than through multiple iterative exchanges.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If the system uses a detailed and well-defined prompt for content generation, then the efficiency of the generation task improves, but the complexity of processing free-form user input increases

Engineering Contradiction:

Improveefficiency of generation taskVSAvoidcomplexity of input processing

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex task of converting free-form input to structured prompts into distinct phases: first, the initial generative model analyzes input and identifies missing parameters; second, placeholders are inserted to create an initial query; third, placeholder values are determined; fourth, the final structured prompt is generated. This segmentation manages complexity through modular processing steps.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP4697198A1Efficient performance of generative task(s) using generative model(s)

Publication Date: 2026.02.18 GOOGLE LLC

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AI summary

Implementations relate to receiving a free-form natural language input associated with a client device; processing, using a first generative model (GM), first GM input to generate corresponding first GM output; determining, based on the first GM output, an initial query that includes placeholder(s); retrieving placeholder data that includes, for the placeholder(s), a corresponding set of variables and a set of probability values corresponding to the set of variables; determining, based on the initial query, a final query; and providing the final query for processing by the first GM or a second GM. Determining the final query includes, for the placeholder(s): selecting, based on the corresponding set of variables and the set of probability values corresponding to the set of variables, a variable from the corresponding set of variables; and replacing the placeholder(s) with the selected variable.