ML System for Synthesizing Digital Correspondences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional artifact drafting workflows are highly manual and inefficient, requiring significant time and effort to compose multiple digital artifacts, and fail to scale for extensive compositions.
Innovation Solution
A machine learning-based system that synthesizes digital artifacts by identifying unstructured conversational dialogue data, mapping requests to specific objectives, generating prompts, and using a large language model to automatically compose digital correspondences and transactional artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual artifact drafting workflows are used, then quality control and tone consistency can be maintained, but time consumption and inefficiency increase significantly
Solution Approach 1:
The system enables self-service artifact generation by allowing users to input high-level requirements and receive automatically synthesized digital artifacts. The machine learning model independently handles the drafting, reviewing, and refinement processes that previously required manual human intervention, thus resolving the contradiction between maintaining quality and reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical manual drafting process with an automated machine learning-based system. The ML model substitutes human writers and reviewers, performing artifact composition and quality assessment automatically. This substitution maintains quality control through programmed guidelines while dramatically reducing the time required for artifact creation.
2Productivity
If manual artifact drafting is used, then flexibility in customization is maintained, but scalability fails when extensive numbers of artifacts are needed
Solution Approach 1:
The machine learning system provides universal artifact generation capabilities that can handle multiple types of digital artifacts (emails, reports, documents, etc.) through a single platform. Users interact with a unified interface that automatically adapts to different artifact types and requirements, enabling scalable production without increasing operational burden. The system maintains flexibility by allowing customization through natural language prompts while handling the complexity internally.
3Productivity
If automated systems are introduced to reduce manual effort, then productivity increases, but system complexity increases
Solution Approach 1:
The system introduces a conversational interface as an intermediary between the user and the complex machine learning model. Users interact through natural language prompts rather than directly configuring the underlying ML system. This intermediary layer shields users from system complexity while enabling high productivity, as the complex artifact synthesis process is triggered and controlled through simple conversational inputs.
Data Source
AI summary
A system and method includes identifying unstructured conversational dialogue data sourced from communications between a subscriber and a conversational dialogue agent; automatically mapping, via the one or more computers, one or more distinct unstructured data synthetization requests defined in the unstructured conversational dialogue data to a distinct artifact synthetization objective defined within a synthetization objective distillation layer; generating, via the one or more computers, a plurality of artifact synthetization prompts corresponding to the plurality of unstructured synthetization data requests based on the distinct artifact synthetization objective mapped to each of the one or more distinct unstructured synthetization data requests; and generating, by a target machine learning model, a plurality of synthesized digital artifacts based on an input of the plurality of artifact synthetization prompts generated for the plurality of unstructured synthetization data requests.